No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Data mining analysis of RTID alarms
Abstract
IBM's emergency response service provides real-time intrusion detection (RTID) services through the Internet for a variety of clients. As the number of clients increases, the volume of alerts generated by the RTID sensors becomes intractable. This problem is aggravated by the fact that some sensors may generate hundreds or even thousands of innocent alerts per day. With an eye towards managing these alerts more effectively, IBM's data mining services group analyzed a database of RTID reports. The first objective was an approach for characterizing the “normal” stream of alerts from a sensor. Using such models tuned to individual sensors, we then developed a methodology for detecting anomalies. In contrast to many popular approaches, the decision to filter an alarm out or not takes into consideration the context in which it occurred and the historical behavior of the sensor it came from. Our second objective was to identify all the different profiles of our clients. Based on their history of alerts, we discovered several different types of clients, with different alert behaviors and thus different monitoring needs. We present the issues encountered, solutions, and findings, and discuss how our results may be used in large-scale RTID operations.
... This problem relates to vast numbers of alerts generated by IDSs, which often reach thousands per day. In actual fact, most of these alarms are False-Positive (FP) alerts [2,4]. FP alerts are alarm messages generated by the IDSs which mark a non-malicious traffic activity. ...
... FP alerts are alarm messages generated by the IDSs which mark a non-malicious traffic activity. The number of these alerts has increased due to rises in both malicious activity and complex network structures; as such, it is practically impossible and complex to verify the legality of each alert [4]. ...
Intrusion-Detection-Systems (IDSs) are the best and most effective techniques when it comes to addressing the threats (such as malware and cyber-attacks etc.) being faced by computer networks; indeed, these systems have been used for more than 20 years. However, these systems generate a huge number of alerts, a large percentage of which are false or incorrect. This problem adversely affects the performance and effectiveness of network security. In this paper, we propose a new system to eliminate duplicated and redundant IDS alerts; the overall aim is to improve network security by minimizing the rate of false positive alarms. This system consists of two major phases, as well as various sub-phases. The first phase involves removing duplicated alerts by applying a new filtering algorithm which has been prepared for this purpose. The aim of the second phase is to reduce false alerts by eliminating the redundant alerts; this is achieved by applying association rules and mining frequent itemset algorithms. This system is evaluated and tested by using five weeks of data from the DARPA 99 dataset. The results show that this system significantly reduces the number of FP alarms by 97.98%. These results also demonstrate the system's substantial ability to reduce the very large number of false alarms related to IDSs.
VERI MADENCILIĞI TEKNIĞINI ILE ID'LER KULLANARAK AĞ GÜVENLIĞININ YÜKSEK KALITELI HALE GETIRILMESI Özet Saldırı Tespit Sistemleri (IDS), bilgisayar ağları tarafından karşılaşılan tehditleri (kötü amaçlı yazılımlar ve siber saldırı-lar gibi) ele almaya gelince en iyi ve etkili tekniklerdir; Gerçekten de, bu sistemler 20 yıldan fazla kullanılmaktadır. Bu-nunla birlikte, bu sistemler çok sayıda uyarı üretir; bunların büyük bir yüzdesi yanlış veya yanlıştır. Bu sorun, ağ güven-liğinin performansını ve etkililiğini olumsuz olarak etkiler. Bu yazıda, çoğaltılmış ve gereksiz IDS uyarılarını ortadan kaldırmak için yeni bir sistem öneriyoruz; genel amaç, yanlış pozitif alarm oranını en aza indirerek ağ güvenliğini art-tırmaktır. Bu sistemin yanı sıra çeşitli alt safhalar olmak üzere iki ana safhadan oluşur. Birinci aşamada, bu amaçla ha-zırlanmış yeni bir filtreleme algoritması uygulayarak çoğaltılan uyarıların kaldırılması gerekir. İkinci aşamada hedef, gereksiz uyarıları ortadan kaldırarak yanlış uyarıları azaltmaktır; bu ilişki kurallarını uygulayarak ve sık öğe seti algo-ritmalarını kullanarak gerçekleştirilir. Bu sistem, DARPA 99 veri kümesindeki beş haftalık verileri kullanarak değerlendi-rilir ve test edilir. Sonuçlar, bu sistemin FP alarm sayısını% 97.98 oranında önemli ölçüde düşürdüğünü göstermekte-dir. Bu sonuçlar, aynı zamanda, sistemin IDS'lerle ilgili çok sayıda yanlış alarmı azaltma kabiliyetini de göstermektedir.
... This problem relates to vast numbers of alerts generated by IDSs, which often reach thousands per day. In actual fact, most of these alarms are False-Positive (FP) alerts [2,4]. FP alerts are alarm messages generated by the IDSs which mark a non-malicious traffic activity. ...
... FP alerts are alarm messages generated by the IDSs which mark a non-malicious traffic activity. The number of these alerts has increased due to rises in both malicious activity and complex network structures; as such, it is practically impossible and complex to verify the legality of each alert [4]. ...
Intrusion-Detection-Systems (IDSs) are the best and most effective techniques when it comes to addressing the threats (such as malware and cyber-attacks etc.) being faced by computer networks; indeed, these systems have been used for more than 20 years. However, these systems generate a huge number of alerts, a large percentage of which are false or incorrect. This problem adversely affects the performance and effectiveness of network se-curity. In this paper, we propose a new system to eliminate duplicated and redundant IDS alerts; the overall aim is to improve network security by minimizing the rate of false positive alarms. This system consists of two major phases, as well as various sub-phases. The first phase involves removing duplicated alerts by applying a new fil-tering algorithm which has been prepared for this purpose. The aim of the second phase is to reduce false alerts by eliminating the redundant alerts; this is achieved by applying association rules and mining frequent itemset algorithms. This system is evaluated and tested by using five weeks of data from the DARPA 99 dataset. The re-sults show that this system significantly reduces the number of FP alarms by 97.98%. These results also demon-strate the system’s substantial ability to reduce the very large number of false alarms related to IDSs
... Firstly, their detection accuracy is very low. In fact, more than 99% of IDSs alerts are false positive [2,3]. Second, they are able to detect only known attacks that were matched to the predefined signatures. ...
... Researchers have done many efforts on the reduction of meaningless IDS alerts [2][3][4]. They are mainly based on data mining and machine learning techniques to deal with the IDS alerts in an automated manner. ...
Most organizations or CERTs deploy and operate Intrusion Detection Systems (IDSs) to carry out the security monitoring and response service. Although IDSs can contribute for defending our information property and crucial systems, they have a fatal drawback in that they are able to detect only known attacks that were matched to the predefined signatures. In our previous work, we proposed a security monitoring and response framework based on not only IDS alerts, but also darknet traffic. The proposed framework regards all incoming darknet packets that were not detected by IDSs as unknown attacks. In our further analysis, we recognized that not all of darknet traffic is related to the real attacks. In this paper, we propose an advanced classification method of darknet packets to effectively identify whether they were caused by the real attacks or not. With the proposed method, the security analyst can ignore the darknet packets that were not related to the real attacks. In fact, the experimental results show that it succeeded in removing 23.45% of unsuspicious darknet packets.
... Data comes from host based IDS, network based IDS, file systems logs, system logs, blacklist, network traces and vulnerability scans. However, practitioners [4,8] and researchers [2,5,9] have observed that these kinds of systems can easily trigger thousands of alerts per day, up to 99% of which are false positives. This volume of false alerts has made very difficult to identify the real threatens. ...
... This volume of false alerts has made very difficult to identify the real threatens. As a result, the manual investigation of alerts to find attack attempts or on going attacks, has been found to be labor-intensive and error-prone [4, 6,9]. Although tools to automate alert investigations are being developed and recently new manual approaches to security data analysis have been proposing [16,15], there is currently no silver-bullet solution to this problem. ...
In response to attack against corporative and enterprise networks, administrators deploy intrusion detection systems, monitors, vulnerability scans and log systems. These systems monitor and record host and network device activities searching for signs of anomalies and security incidents. Doing that, these systems generally produce a huge number of alerts that overwhelms security analysts. This paper proposes the application of a conceptual clustering technique for filtering alerts and shows the results obtained for seven months of security alerts generated in a real large scale SaaS Cloud system. The technique has been useful to support manual analysis activities conducted by the operations team of the reference Cloud system.
... IDS produces more alerts for single attack instance. It is challenging for the human expert to handle the bountiful alert [2] .The research focus on how to reduce this bountiful alerts. This alert contains more false positive alerts which are not related to the attacks. ...
... Manganaris S et al proposed the judgment making technique in huge data's because IDS produce large data's [1] . Axelsson S analyses the types of IDS and the methods used to find out the attacks [2]. Bishop C.M dealt with the calculation of pattern checking in large database using Markov method [3]. ...
The vast alert generation of IDS in the network is the major problem. It is the vital task to find solutions to reduce the alerts. Novel techniques namely Fuzzy Association rule and Fuzzy art map are proposed to identify attacks optimally and to reduce alerts. The execution time is reduced by placing the level of severity and importance. All alerts that are issued by IDSs are not on the same level of severity and importance. It would be great if the system can identify which alerts are highly important and which are not, so that the number of alerts that need to be dealt with can be reduced. The alert is reduced by finding out the attacks accurately using various methods. The Membership function is used to classify the attack as low, mid or high using continuous attribute. The rules are set for each attack using fuzzy association rule. The chi-square, confidence and support values are estimated for each rule and the minimum value will be set for all parameters .The Rules higher than the verge value are taken and the rules for each generation are updated. Then the rules are compared with test data set and calculated the match degree for each attack. The proposed fuzzy association rule is to obtain superlative features. The Fuzzy art map technique is used to classify the intrusion and normal data by calculating the match degree. Hence this technique aims to effectively reduce the alert rate when compared with existing approaches.
... This is done to determine when a micro-pattern appears in a sequence. The use of this threshold is justified since it is difficult for an entire micro-pattern to appear exactly in the new sequence, given the variability of behaviour (see [14,15]). ...
... By also having the micro-patterns appear in a sequence we were able to ensure that the sequence contained the profile of loitering behaviour, thus obtaining new F1Score: As can be seen from the Table 2, the results with the highest values are observed when the sequences contain 75 % of the profile micro-patterns (see row highlighted in bold). These results are considered valid (see [15,16]) for this study as they provide a high recall value and because the level of precision does not severely decrease, but instead gradually increases based on the fact that the input sequences contain the optimum number of micro-patterns. The optimum percentage (75 %) of inclusion of the micro-patterns in the input sequences is thus determined by the highest value of the F1Score (0.91). ...
Loitering is a common behaviour of the elderly people. We goal is develop an artificial intelligence system that automatically detects loitering behaviour in video surveillance environments. The first step to identify this behaviour was used a Generalized Sequential Patterns that detects sequential micro-patterns in the input loitering video sequences. The test phase determines the appropriate percentage of inclusion of this set of micro-patterns in a new input sequence, namely those that are considered to form part of the profile, and then be identified as loitering. The system is dynamic; it obtains micro-patterns on a repetitive basis. During the execution time, the system takes into account the human operator and updates the performance values of loitering in shopping mall. The profile obtained is consistent with what has been documented by experts in this field and is sufficient to focus the attention of the human operator on the surveillance monitor.
... Association rules have been used in network intrusion detection [27] as well as IDS sensor profiling [28]. However, association rules are used to automatically build misuse detection models in [27], and model the normal IDS sensor behaviors with bursts of alerts in [28], while in TIAA, association rules are used to discover patterns in intrusion alert attribute values. ...
... Association rules have been used in network intrusion detection [27] as well as IDS sensor profiling [28]. However, association rules are used to automatically build misuse detection models in [27], and model the normal IDS sensor behaviors with bursts of alerts in [28], while in TIAA, association rules are used to discover patterns in intrusion alert attribute values. ...
This paper presents the development of TIAA, a visual toolkit for in-trusion alert analysis. TIAA is developed to provide an interactive platform for analyzing potentially large sets of intrusion alerts reported by heterogeneous in-trusion detection systems (IDSs). To ensure timely response from the system, TIAA adapts main memory index structures and query optimization techniques to improve the efficiency of intrusion alert correlation. TIAA includes a num-ber of useful utilities to help analyze potentially intensive intrusion alerts, in-cluding alert aggregation/disaggregation, clustering analysis, focused analysis, frequency analysis, link analysis, and association analysis. Moreover, TIAA pro-vides several ways to visualize the analysis results, making it easier for a human analyst to understand the analysis results. It is envisaged that a human analyst and TIAA form a man-machine team, with TIAA performing automated tasks such as intrusion alert correlation and execution of analysis utilities, and the human analyst deciding what sets of alerts to analyze and how the analysis utilities are applied.
... Knowing if such traffic suddenly increases might give an early warning that something bad is about to happen and counter measures can be deployed in time. 26 Chapter 3 ...
... A somewhat different approach is used by Manganaris et al. [26] Instead of filtering out or removing the benign alarms as Julisch [16] and Clifton and Gengo [5] do above, they are viewed as something normal. The idea is presented as applying anomaly detection on the output from a misuse detection system. ...
... The frequency of alarms depends on how the IDS is configured, i.e., which rules are set to trigger an alarm. In practice, most of the alarms raised by IDSes are false alarms; typical IDS false alarm rates are above 90% with many as high as 99% [Julisch, 2003, Manganaris et al., 2000. Axelsson [2000] raises the issue of the base rate fallacy, stating that the ratio of actual attacks to benign traffic is so low that IDSes must be extraordinarily accurate to have acceptable detection performance. ...
Organizations use intrusion detection systems (IDSes) to identify harmful activity among millions of computer network events. Cybersecurity analysts review IDS alarms to verify whether malicious activity occurred and to take remedial action. However, IDS systems exhibit high false alarm rates. This study examines the impact of IDS false alarm rate on human analyst sensitivity (probability of detection), precision (positive predictive value), and time on task when evaluating IDS alarms. A controlled experiment was conducted with participants divided into two treatment groups, 50% IDS false alarm rate and 86% false alarm rate, who classified whether simulated IDS alarms were true or false alarms. Results show statistically significant differences in precision and time on task. The median values for the 86% false alarm rate group were 47% lower precision and 40% slower time on task than the 50% false alarm rate group. No significant difference in analyst sensitivity was observed.
... Furthermore, the rule X Y [5,14] for the transaction set D holds the confidence c if there exists c% of transaction set D containing X along with Y [14]. Also rule X Y for the transaction set D is said to have support s if there exist s% of transactions in D containing X Y. Association rules are selected based how much support and confidence a rule posses [14]. ...
... Their goal is to identify sequences of alarms caused by normal operations based on preposition that a common sequence of alerts is probably not the result of actual intrusion attempt. Manganaris et al. [27] briefly mentions sequential patterns as an alternative to association rules in their work dedicated to adaptive alert filtering and sensor profiling. Sequential pattern mining was proposed as a future work. ...
Data mining is well-known for its ability to extract concealed and indistinct patterns in the data, which is a common task in the field of cyber security. However, data mining is not always used to its full potential among cyber security community. In this paper, we discuss usability of sequential pattern and rule mining, a subset of data mining methods, in an analysis of cyber security alerts. First, we survey the use case of data mining, namely alert correlation and attack prediction. Subsequently, we evaluate sequential pattern and rule mining methods to find the one that is both fast and provides valuable results while dealing with the peculiarities of security alerts. An experiment was performed using the dataset of real alerts from an alert sharing platform. Finally, we present lessons learned from the experiment and a comparison of the selected methods based on their performance and soundness of the results.
... • Analyzing data at low level: Data for intrusion detection are collected from many sources, and it contains several features. Several data mining algorithms work on intrusion detection datasets, which contain data at the TCPDUMP level (Lee and Stolfo 1998) or at the alert level [181] including features such as source and destination IP addresses, port numbers, time stamps, and the duration of each connection. When data mining techniques are applied on such low-level features, they can produce a good description of an individual connection or a flow, but they need a broader context to decide with more certainty if a given connection is a suspicious activity or not. ...
Research in cyber-security has demonstrated that dealing with cyber-attacks is by no means an easy task. One particular limitation of existing research originates from the uncertainty of information that is gathered to discover attacks. This uncertainty is partly due to the lack of attack prediction models that utilize contextual information to analyze activities that target computer networks. The focus of this paper is a comprehensive review of data analytics paradigms for intrusion detection along with an overview of techniques that apply contextual information for intrusion detection. A new research taxonomy is introduced consisting of several dimensions of data mining techniques, which create attack prediction models. The survey reveals the need to use multiple categories of contextual information in a layered manner with consistent, coherent, and feasible evidence toward the correct prediction of cyber-attacks.
... In other words, this leads to miss out the true attack that must have occurred amidst the flooding of false alarms in the IDS. For example, an administrator may look upon; say 10,000-20,000 alarms per day, [12] out of which a true attack may be hidden. The 100s of alarms reviewed on daily basis forms only a partial count of the enormous responses generated. ...
Mobile Ad hoc Networks (MANETs) are collections of self-organizing mobile nodes with dynamic topologies possessing no fixed infrastructure. These networks are gaining importance as they can be applied in various areas like rescue operations, conferences, environmental monitoring and the like. Compared with infrastructure-based wireless networks, wireless ad hoc networks are more vulnerable to attacks and therefore providing security is more challenging. Intrusion Detection Systems (IDS) are an important area of research which acts as a second line of defense against unauthorized activities in networks. The effectiveness of an ID is measured by the response it generates specific to the type of intrusion detected. Among them, false alarms need to be addressed. False positives are misleading alerts generated, considering an attack has been launched when noneas occurred.Consequently various works have been worked out by researchers to reduce the number and impact of false positives by fine-tuning the thresholds based on the type of network scenarios and attacks recognized.This paper will highlight the various works done in the implementation of Intrusion Detection Systems and how the reduction in false alarm rates has been dealt for each method.
... In Manganaris et al. (Manganaris, Christensen, Zerkle, & Hermiz, 1999) and Huang et al. (Huang, Kao, Hun, Jai, & Lin, 2005), data-mining techniques are employed to screen and analyze alerts of attacks. Experiments conducted using data-mining techniques in analysis of alarms showed results that perform very well on attack-detection rate and false-alarm rate. ...
Firstly, the fact that business intelligence (BI) applications are growing in importance, and secondly, the growing and more sophisticated attacks launched by hackers, the concern of how to protect the knowledge capital or databases that come along with BI or in another words, BI security, has thus arisen. In this chapter, the BI environment with its security features is explored, followed by a discussion on intrusion detection (ID) and intrusion prevention (IP) techniques. It is understood through a Web-service case study that it is feasible to have ID and IP as countermeasures to the security threats; thus further enhancing the security of the BI environment or architecture.
... All presented statistical models in mentioned researches include online training and thus, compatibility with current conditions and flexibility based on new changes, are completely possible. Also, the algorithms described in [43], [44] and [45] are expressed based on Association Rules for detecting alerts which normally occur together. An important application of this method is determining alert priorities based on this that which alerts have occurred together and have these accompaniments occurred on a usual procedure or a new pattern is observed, but also this algorithm can be used for creating related meta-alerts. ...
Alert correlation is a system which receives alerts from heterogeneous Intrusion Detection Systems and reduces false alerts, detects high level patterns of attacks, increases the meaning of occurred incidents, predicts the future states of attacks, and detects root cause of attacks. To reach these goals, many algorithms have been introduced in the world with many advantages and disadvantages. In this paper, we are trying to present a comprehensive survey on already proposed alert correlation algorithms. The approach of this survey is mainly focused on algorithms in correlation engines which can work in enterprise and practical networks. Having this aim in mind, many features related to accuracy, functionality, and computation power are introduced and all algorithm categories are assessed with these features. The result of this survey shows that each category of algorithms has its own strengths and an ideal correlation frameworks should be carried the strength feature of each category.
... A security operator supervises log alerts emanating from different IDS. When the data traffic is important and the number of suspicious activities is high, the security operator is quickly invaded by the huge number of alerts generated by IDS [25]. There are two ways to deal with the large number of IDS alerts. ...
Intrusion Detection Systems (IDS) are necessary and important tools for monitoring information systems. However they produce a huge quantity of alerts. Alerts correlation is a process that reduces the number of alerts reported by intrusion detection systems. In this paper, we propose a new algorithm for a logical-based alerts correlation approach that integrates: security operator's knowledge and preferences. The representation and the reasoning on these knowledge and preferences are done using a new logic called Instantiated First Order Qualitative Choice Logic (IFO-QCL). Our modeling views an alert as an interpretation which allows us to have an efficient algorithm that performs the correlation process in a polynomial time. This paper also provides experimental results which are achieved on datasets issued from a real monitoring system.
... Visualization,,Clustering[Manganaris et al., 2000],Association rule discovery,[Clifton and Gengo, 2000;Barbara et al., 2001],Classification[Lee and Stolfo, 1998]. Data mining is used as a tool for detecting intrusions. ...
... Therefore, it is becoming more difficult to understand the entire network situation and respond to the security incidents rapidly and accurately if they depend on only a single type of security information. In particular, considering zombie PCs (e.g., bots) that are already infected by malware are also trying to propagate themselves to both internal and external computer systems, and furthermore security appliances trigger an unmanageable amount of alerts (in fact, by some estimates, several thousands of alerts are raised everyday [1], and about 99% of them are false positives [2]), this situation makes it difficult for the analyst to investigate all of them and to identify which alerts are more serious and which are not. ...
In order to cope with the continuous evolution in cyber threats, many security products (e.g., IDS/IPS, TMS, Firewalls) are being deployed in the network of organizations, but it is not so easy to monitor and analyze the security events triggered by the security products constantly and effectively. Thus, in many cases, real-time incident analysis and response activities for each organization are assigned to an external dedicated security center. However, since the external security center deploys its security appliances to only the boundary or the single point of the network, it is very difficult to understand the entire network situation and respond to security incidents rapidly and accurately if they depend on only a single type of security information. In addition, security appliances trigger an unmanageable amount of alerts (in fact, by some estimates, several thousands of alerts are raised everyday, and about 99% of them are false positives), this situation makes it difficult for the analyst to investigate all of them and to identify which alerts are more serious and which are not. In this paper, therefore, we propose an advanced incident response methodology to overcome the limitations of the existing incident response scheme. The main idea of our methodology is to utilize polymorphic security events which can be easily obtained from the security appliances deployed in each organization, and to subject them to correlation analysis. We evaluate the proposed methodology using diverse types of real security information and the results show the effectiveness and superiority of the proposed incident response methodology.
... Clifton and Gengo [1] considers that false alarms appear in the alert because normal operation with similar characteristics of the invasion occurs in a particular environment, and the alarms caused by these operations have a certain sequential pattern. Manganaris [2] divides the continuous alarm flow into a lot of alarm bursts, and map every alarm burst into a transaction. Dr. Mei Haibin [3] firstly proposed using the classic Apriori algorithm to implement the association mining for massive alerts. ...
Research in network security, with the attacks becoming more frequent, increasing complexity means, for the large-scale network intrusion detection, this paper presents a warning by analyzing the behavior of the log, the contents of the relevant association, through the DHT(Distributed Hash Table) distributed architecture, the Collabarative matching, fusion, and ultimately determine the method of attack paths. First, by improving the classical Apriori algorithm, greatly improving the efficiency of the association. At the same time, through the behavior pattern matching algorithms to extract information about the behavior of the alert and the behavior sequence elements to match the template, and through the right path to finally determine the value of the threat of the network path. After the design of a DHT network, the distributed collaborative match the path used to find complex network attacks. Finally, the overall algorithm flow, proposed a complete threat detection system architecture. (C) 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of [CEIS2011]
... If the similarity value is more than some threshold, alerts are placed in one cluster. Filter-based approaches [14,15,16,19,20,29]either identify the false positive and the irrelevant alert or assign a priority to each alert. For instance, an alert could be classified as irrelevant if it represents an attack against a non-existent service. ...
One of the most important challenges facing the intrusion detection systems (IDSs) is the huge number of generated alerts. A system administrator will be overwhelmed by these alerts in such a way that she/he cannot manage and use the alerts. The best-known solution is to corre-late low-level alerts into a higher level attack and then produce a high-level alert for them. In this paper a new automated alert correlation approach is presented. It em-ploys Fuzzy Logic and Artificial Immune System (AIS) to discover and learn the degree of correlation between two alerts and uses this knowledge to extract the attack scenarios. The proposed system doesn't need vast domain knowledge or rule definition efforts. To correlate each new alert with previous alerts, the system first tries to find the correlation probability based on its fuzzy rules. Then, if there is no matching rule with the required matching threshold, it uses the AIRS algorithm. The system is eval-uated using DARPA 2000 dataset and a netForensics hon-eynet data. The completeness, soundness and false alert rate are calculated. The average completeness for LL-DoS1.0 and LLDoS2.0, are 0.957 and 0.745 respectively. The system generates the attack graphs with an accept-able accuracy and, the computational complexity of the probability assignment algorithm is linear.
... IDSs usually generate large number of alerts whenever abnormal activities are transmitted from/to the protected network and/or hosts. It is common that an IDS reports 10-200 alerts per day and this number increases when more than one IDS is deployed [24]. These number of alerts can easily overwhelm the security analysts who manage the IDSs in the network. ...
Organizations use intrusion detection systems (IDSes) to identify harmful activity among millions of computer network events. Cybersecurity analysts review IDS alarms to verify whether malicious activity occurred and to take remedial action. However, IDS systems exhibit high false alarm rates. This study examines the impact of IDS false alarm rate on human analyst sensitivity (probability of detection), precision (positive predictive value), and time on task when evaluating IDS alarms. A controlled experiment was conducted with participants divided into two treatment groups, 50% IDS false alarm rate and 86% false alarm rate, who classified whether simulated IDS alarms were true or false alarms. Results show statistically significant differences in precision and time on task. The median values for the 86% false alarm rate group were 47% lower precision and 40% slower time on task than the 50% false alarm rate group. No significant difference in analyst sensitivity was observed.
SCBDA (Semi-trust control Based data acquisition) systems are used for controlling and monitoring industrial processes. We propose a methodology to systematically identify potential process-related threats in SCBDA. Process-related threats take place when an attacker gains user access rights and performs actions, which look legitimate, but which are intended to disrupt the SCBDA process. To detect such threats, we propose a semi-automated approach of log processing. We conduct experiments on a real-life water treatment facility. A preliminary case study suggests that our approach is effective in detecting anomalous events that might alter the regular process workflow.
Contenido
1. Intrusion Detection in 1993
2. Intrusion Detection in 1994
3. Intrusion Detection in 1995
4. Intrusion Detection in 1996
5. Intrusion Detection in 1997
6. Intrusion Detection in 1998
7. Intrusion Detection in 1999
8. Intrusion Detection in 2000
9. Intrusion Detection in 2001
10. Intrusion Detection in 2002
11. Intrusion Detection in 2003
Intrusion Detection Systems (IDSs) are widely deployed with increasing of unauthorized activities and attacks. However they often overload security managers by triggering thousands of alerts per day. And up to 99% of these alerts are false positives (i.e. alerts that are triggered incorrectly by benign events). This makes it extremely difficult for managers to correctly analyze security state and react to attacks. In this chapter the authors describe a novel system for reducing false positives in intrusion detection, which is called ODARM (an Outlier Detection-Based Alert Reduction Model). Their model based on a new data mining technique, outlier detection that needs no labeled training data, no domain knowledge and little human assistance. The main idea of their method is using frequent attribute values mined from historical alerts as the features of false positives, and then filtering false alerts by the score calculated based on these features. In order to filer alerts in real time, they also design a two-phrase framework that consists of the learning phrase and the online filtering phrase. Now they have finished the prototype implementation of our model. And through the experiments on DARPA 2000, they have proved that their model can effectively reduce false positives in IDS alerts. And on real-world dataset, their model has even higher reduction rate.
Anomaly means something which is not normal. Any data point which deviates or placed in distance from all other normal data points is an anomaly. That is why anomalies are also called as outliers. Anomaly detection is also called as deviation detection because anomalous objects have attribute values that are different from all other normal data objects. In this paper we have discussed about various causes of anomalies, anomaly detection approaches and also issues that are to be taken care during finding out the best technique for anomaly detection.
Intrusion detection systems (IDSs) are commonly used to detect attacks on computer networks. These tools analyze incoming and outgoing traffic for suspicious anomalies or activities. Unfortunately, these generate a significant amount of noise complexifying greatly the analysis of the data. This chapter addresses the problem of false alarms in IDSs. Its first purpose is to improve their accuracy by detecting real attacks and by reducing the number of unnecessary alerts. To do so, this intrusion detection mechanism enhances the accuracy of anomaly intrusion detection systems using a set of agents to ensure the detection and the adaptation of normal profile to support the legitimate changes that occur over time and are the cause of many false alarms. Besides this, as a perspective of this work, this chapter opens up new research directions by listing the different requirements of an IDS and proposing solutions to achieve them.
Alarm classification and visualization of historical data is significant and sophisticated in the area of smart management in telecom network due to alarm flood and propagation. In this article, we propose a heterogeneous distance to compute the similarity distance matrix of alarms, which is applied to alarm classification. By using Multidimensional Scaling, alarm data in high dimension is translated into a 2-dimensional graph in alarm windows. Then alarm attention and relation are clearly shown by comparing current and past alarms. Experiments show MDS based on the heterogeneous distance has a better classifying effect than other distance measures. The case study demonstrates the method can show alarm correlation easily and help to locate faults when applied to the analysis of telecom alarm data.
The security of information and communication technology is a high priority for any organization. By examining the current problems and challenges this domain is facing, more efficient strategies can be established to safeguard personal information against invasive pressures.
Security and Privacy Management, Techniques, and Protocols is a critical scholarly resource that examines emerging protocols and methods for effective management of information security at organizations. Featuring coverage on a broad range of topics such as cryptography, secure routing protocols, and wireless security, this book is geared towards academicians, engineers, IT specialists, researchers, and students seeking current research on security and privacy management.
The complexity, multiplicity, and impact of cyber-attacks have been increasing at an alarming rate despite the significant research and development investment in cyber security products and tools. The current techniques to detect and protect cyber infrastructures from these smart and sophisticated attacks are mainly characterized as being ad hoc, manual intensive, and too slow. We present in this paper AIM-PSC that is developed jointly by researchers at AVIRTEK and The University of Arizona Center for Cloud and Autonomic Computing that is inspired by biological systems, which can efficiently handle complexity, dynamism and uncertainty. In AIM-PSC system, an online monitoring and multi-level analysis are used to analyze the anomalous behaviors of networks, software systems and applications. By combining the results of different types of analysis using a statistical decision fusion approach we can accurately detect any types of cyber-attacks with high detection and low false alarm rates and proactively respond with corrective actions to mitigate their impacts and stop their propagation.
System administrators cope with security incidents through a variety of monitors, such as intrusion detection systems, event logs, security information and event management systems. Monitors generate large volumes of alerts that overwhelm the operations team and make forensics time-consuming. Filtering is a consolidated technique to reduce the amount of alerts. In spite of the number of filtering proposals, few studies have addressed the validation of filtering results in real production datasets. This paper analyzes a number of state-of-the-art filtering techniques that are used to address security datasets. We use 14 months of alerts generated in a SaaS Cloud. Our analysis aims to measure and compare the reduction of the alerts volume obtained by the filters. The analysis highlights pros and cons of each filter and provides insights into the practical implications of filtering as affected by the characteristics of a dataset. We complement the analysis with a method to validate the output of a filter in absence of ground truth, i.e., the knowledge of the incidents occurred in the system at the time the alerts were generated. The analysis addresses blacklist, conceptual clustering and bytes techniques, and our filtering proposal based on term weighting.
Pattern mining is a branch of data mining used to discover hidden patterns or correlations among data. We use rare sequential pattern mining to find anomalies in critical infrastructure control networks such as supervisory control and data acquisition (SCADA) networks. As anomalous events occur rarely in a system and SCADA systems’ topology and actions do not change often, we argue that some anomalies can be detected using rare sequential pattern mining. This anomaly detection would be useful for intrusion detection or erroneous behaviour of a system. Although research into rare itemsets mining previously exists, neither research into rare sequential pattern mining nor its applicability to SCADA system anomaly detection has previously been completed. Moreover, since there is no consideration to events order, the applicability to intrusion detection in SCADA is minimal. By ensuring the events’ order is maintained, in this paper, we propose a novel Rare Sequential Pattern Mining (RSPM) technique which is a useful anomaly detection system for SCADA. We compared our algorithm with a rare itemset mining algorithm and found anomalous events in SCADA logs.
Intrusion Detection is one of major threats for organization. The approach of intrusion detection using text processing has been one of research interests which is gaining significant importance from researchers. In text mining based approach for intrusion detection, system calls serve as source for mining and predicting possibility of intrusion or attack. When an application runs, there might be several system calls which are initiated in the background. These system calls form the strong basis and the deciding factor for intrusion detection. In this paper, we mainly discuss the approach for intrusion detection by designing a distance measure which is designed by taking into consideration the conventional Gaussian function and modified to suit the need for similarity function. A Framework for intrusion detection is also discussed as part of this research.
In this paper the major objective is to design and analyze the suitability of Gaussian similarity measure for intrusion detection. The objective is to use this as a distance measure to find the distance between any two data samples of training set such as DARPA Data Set, KDD Data Set. This major objective is to use this measure as a distance metric when applying k- means algorithm. The novelty of this approach is making use of the proposed distance function as part of k-means algorithm so as to obtain disjoint clusters. This is followed by a case study, which demonstrates the process of Intrusion Detection. The proposed similarity has fixed upper and lower bounds. The proposed similarity measure satisfies all properties of a typical similarity measure.
With the enormous growth of computer networks and the huge increase in the number of applications that rely on it, network security is gaining increasing importance. Moreover, almost all computer systems suffer from security vulnerabilities which are both technically difficult and economically costly to be solved by the manufacturers. Therefore, the role of Intrusion Detection Systems (IDSs), as special-purpose application to detect attacks in a network, is becoming more important.
The aim of proposed application is to reduce the amount of data retained for processing i.e., attribute selection process and also to improve the detection rate of the existing IDS using data mining technique. For this it uses improved data mining algorithm, i.e. C4.5, which in turn a modified version of APRIORI algorithm, for implementing fuzzy rules, which allows us to construct if-then rules that reflect common ways of describing security attacks.
Due to the rapid growth of networked computer resources and the increasing importance of related applications, intrusions which threaten the infrastructure of these applications have are critical problems. In recent years, several intrusion detection systems designed to identify and detect possible intrusion behaviors. In this work, an intrusion detection model is proposed to for building an intrusion detection system which can solve problems involved in building an intrusion detection systems, including pattern representation. computability, performance, extendibility and maintenance problems. In this model, IDML is first designed to express intrusion patterns, and these patterns are transformed into intrusion pattern state machines. Once the intrusion pattern state machines are obtained, the corresponding intrusion detection mechanism that can use these state machines to detect intrusions is designed. To evaluate the performance of our model, an IDML-based intrusion detection experimental system based upon this architecture has been implemented.
Data mining techniques have been successfully applied in many different fields including marketing, manufacturing, process control, fraud detection, and network management. Over the past five years, a growing number of research projects have applied data mining to various problems in intrusion detection. This chapter surveys a representative cross section of these research efforts. Moreover, four characteristics of contemporary research are identified and discussed in a critical manner. Conclusions are drawn and directions for future research are suggested.
In this paper, we analyzed the present problems and put forward the network security situation awareness framework based on data mining. The framework of network security situation by model and the whole process of the generation network security situation. We have described formal model for the construction of network security situation measurement based on d-s evidence theory, frequent mode, and sequence model extracted from the data on network security situation based on the knowledge found method and convert the pattern on the related rules of the network security situation, and automatic generation of network security situation figure.
Intrusion Detection Systems (IDS) are very important tools for network monitoring. However, they often produce a large quantity of alerts. The security operator who analyses IDS alerts is quickly overwhelmed. Alert correlation is a process applied to the IDS alerts in order to reduce their number. In this paper, we propose a new approach for logical based alert correlation which integrates the security operator's knowledge and preferences in order to present to him only the most suitable alerts. The representation and the reasoning on these knowledge and preferences are done using a new logic called Instantiated First Order Qualitative Choice Logic (IFO-QCL). Our modeling shows an alert as an interpretation which allows us to have an efficient algorithm that performs the correlation process in a polynomial time. Experimental results are achieved on data collected from a real system monitoring. The result is a set of stratified alerts satisfying the operators criteria.
The problem of clustering is NP-Complete. The existing clustering algorithm in literature is the approximate algorithms, which cluster the underlying data differently for different datasets. The K-Means Clustering algorithm is suitable for frequency but not for binary form. When an application runs several system calls are implicitly invoked in the background. Based on these system calls we can predict the normal or abnormal behavior of applications. This can be done by classification. In this paper we tried to perform classification of processes running into normal and abnormal states by using system call behavior. We reduce the system call feature vector by choosing k-means algorithm which uses the proposed measure for dimensionality reduction. We give the design of the proposed measure. The proposed measure has upper and lower bounds which are finite.
In this paper the major objective is to design and analyze the suitability of Gaussian similarity measure for intrusion detection. The objective is to use this as a distance measure to find the distance between any two data samples of training set such as DARPA Data Set, KDD Data Set. This major objective is to use this measure as a distance metric when applying k-means algorithm. The novelty of this approach is making use of the proposed distance function as part of k-means algorithm so as to obtain disjoint clusters. This is followed by a case study, which demonstrates the process of Intrusion Detection. The proposed similarity has fixed upper and lower bounds.
Intrusion Detection is one of the major threats for any organization of any size. The approach of intrusion detection using text processing has been one of the research interests among researchers working in the area of the network and information security. In this approach for intrusion detection, the system calls serve as the source for mining and predicting any chance of intrusion. When an application runs, there might be several system calls which are initiated in the background. These system calls form the basis and the deciding factor for intrusion detection. We perform an extensive survey on Intrusion detection using text mining techniques and validate the suitability of various kernel measures published in the literature. We finally come out with the research directions for intrusion detection which have not been discussed in detail in the literature. We hope this survey will be useful for researchers working in the direction of intrusion detection using text mining techniques.
Data mining (DM) is the key process in knowledge discovery. Many theoretical and practical DM applications can be found in science and engineering. However there are still such areas where data mining techniques are still at early stage of development and application. In particular, an unsatisfactory progress is observed in DM applications in the analysis of Internet and Web performance issues. This chapter gives the background of network performance measurement and presents our approaches, namely Internet Performance Mining and Web Performance Mining as the ways of DM application to Internet and Web performance issues. The authors present real-life examples of the analysis where explored data sets were collected with the aid of two network measurement systems WING and MWING developed at our laboratory.
The analysis of the security alerts collected during the system operations is a crucial task to initiate effective responses against attacks and intentional system misuse. A variety of monitors are today available to generate security alerts, such as intrusion detection systems, network audit, vulnerability scans, and event logs. While the amount of alerts generated by the security monitors represents a goldmine of information, the ever-increasing volume and heterogeneity of the collected alerts pose a major threat to timely security analysis and forensic activities conducted by the operations team.
This paper proposes a framework consisting of a filter and a decision tree to address large volumes of security alerts and to support the automated identification of the root causes of the alerts. The framework adopts both term weighting and conceptual clustering approaches to fill the gap between the unstructured textual alerts and the formalization of the decision tree. We evaluated the framework by analyzing two security datasets in a production SaaS Cloud, which generates an average volume of 800 alerts/day. The framework significantly reduced the volume of alerts and inferred the root causes of around 98.8% of alerts with no human intervention with respect to the datasets available in this study. More important, we leveraged the output of the framework to provide a classification of the root causes of the alerts in the target SaaS Cloud.
Increasing growth on employing computer networks services on the one hand and networks intrusion on the other hand have caused Intrusion Detection Systems (IDSs) to become a critical research subject in the area of computer systems security. To establish security in computer systems other administrations such as IDSs are required as well as firewalls and other intrusion prevention policies so as to be capable of detecting and dealing with intruders in case of breaking in through firewalls, antivirus and other security tools. The number of alerts generated by IDS, in some of cases, escalates over 2000 messages a day. A tremendous volume of alerts coupled with their low quality makes it challenging for a system administrator to handle intrusions in timely manner. It is hardly possible for systems security managers to handle such distributed alerts in order to increase their quality and convey a comprehensible report on current security state to security analyzer. One of the important approaches to handle such inefficiency is the employment of correlation of raw generated alerts by the system security sensors including IDSs. Such process aims at reduction of generated alerts as well as extraction of attacks scenario in CIDS environment. In this paper, we apply a probabilistic correlation algorithm that is works based on similarity between alerts on three standard data sets. The results indicate that the incoming alerts significantly reduced by this algorithm in rate of 99.96% on Treasure Hunt data set.
Data Mining is a process used in the industry, to facilitate decision making. As the name implies, large volumes of data is mined or sifted, to find useful information for decision making. With the advent of E-business, Data Mining has become more important to practitioners. The purpose of this paper is to find out the importance of Data Mining by looking at the different application areas that have used data mining for decision making.
We are given a large database of customer transactions. Each transaction consists of items purchased by a customer in a visit. We present an efficient algorithm that generates all significant association rules between items in the database. The algorithm incorporates buffer management and novel estimation and pruning techniques. We also present results of applying this algorithm to sales data obtained from a large retailing company, which shows the effectiveness of the algorithm.
In this paper we discuss a data mining framework for constructing intrusion detection models. The key ideas are to mine system audit data for consistent and useful patterns of program and user behavior, and use the set of relevant system features presented in the patterns to compute (inductively learned) classifiers that can recognize anomalies and known intrusions. Our past experiments showed that classifiers can be used to detect intrusions, provided that sufficient audit data is available for training and the right set of system features are selected. We propose to use the association rules and frequent episodes computed from audit data as the basis for guiding the audit data gathering and feature selection processes. We modify these two basic algorithms to use axis attribute(s) as a form of item constraints to compute only the relevant ("useful") patterns, and an iterative level-wise approximate mining procedure to uncover the low frequency (but important) patterns...
We consider the problem of analyzing market-basket data and present several important contributions. First, we present a new algorithm for finding large itemsets which uses fewer passes over the data than classic algorithms, and yet uses fewer candidate itemsets than methods based on sampling. We investigate the idea of item reordering, which can improve the low-level efficiency of the algorithm. Second, we present a new way of generating "implication rules," which are normalized based on both teh antecedent and the consequent and are truly implications (not simply a measure of co-occurence), and we show how they produce more intuitive results than other methods. Finally, we show how different characteristics of real data, as opposed to synthetic data, can dramatically affect the performance of the system and the form of the results.
In 1785 Condorcet proposed a method to aggregate qualitative data, but until very recently this method was attributed to contemporary authors and its importance completely neglected.
Sequences of events describing the behavior and actions of users or systems can be collected in several domains. In this paper we consider the problem of recognizing frequent episodes in such sequences of events. An episode is defined to be a collection of events that occur within time intervals of a given size in a given partial order.Once such episodes are known, one can produce rules for describing or predicting the behavior of the sequence. We describe an efficient algorithm for the discovery of all frequent episodes from a given class of episodes, and present experimental results.
In this paper, we outline a novel coml&atoGl algorithm for ibe discovery of igid motifs contained in a set of input sequences. This is achieved without pair-wise aligmnent of the input sequences or emm~eration of the entire motif space (solution space>. Additionally, the reported motifs are guarauteed to be maximal in both length and compositiox~ Intemal repeats and patterns that repeat across sequences are treated uniformly by the algorithm. Results on real datasets are briefly discussed. During the last twenty years, a number of algorithms have been devised for identifjkg sequence simihuity in amino or nucleic acid sequences. String aligmuent [lo] has been the underlying approach of choice for a large number of the resulting methods [3,7,4, 1,2, 11, 12, 13, 16, 283 which attempted to determine a minimmn cost consensus in the presence of allowable editing trausfonnations.
We are given a large database of customer transactions, where each
transaction consists of customer-id, transaction time, and the items
bought in the transaction. We introduce the problem of mining sequential
patterns over such databases. We present three algorithms to solve this
problem, and empirically evaluate their performance using synthetic
data. Two of the proposed algorithms, AprioriSome and AprioriAll, have
comparable performance, albeit AprioriSome performs a little better when
the minimum number of customers that must support a sequential pattern
is low. Scale-up experiments show that both AprioriSome and AprioriAll
scale linearly with the number of customer transactions. They also have
excellent scale-up properties with respect to the number of transactions
per customer and the number of items in a transaction
Intrusion detection is a new, retrofit approach for providing a sense of security in existing computers and data networks, while allowing them to operate in their current "open" mode. The goal of intrusion detection is to identify unauthorized use, misuse, and abuse of computer systems by both system insiders and external penetrators. The intrusion detection problem is becoming a challenging task due to the proliferation of heterogeneous computer networks since the increased connectivity of computer systems gives greater access to outsiders and makes it easier for intruders to avoid identification. Intrusion detection systems (IDSs) are based on the beliefs that an intruder's behavior will be noticeably different from that of a legitimate user and that many unauthorized actions are detectable. Typically, IDSs employ statistical anomaly and rulebased misuse models in order to detect intrusions. A number of prototype IDSs have been developed at several institutions, and some of them have also been deployed on an experimental basis in operational systems. In the present paper, several host-based and network-based IDSs are surveyed, and the characteristics of the corresponding systems are identified. The host-based systems employ the host operating system's audit trails as the main source of input to detect intrusive activity, while most of the network-based IDSs build their detection mechanism on monitored network traffic, and some employ host audit trails as well. An outline of a statistical anomaly detection algorithm employed in a typical IDS is also included.< >
We consider the problem of analyzing market-basket data and present several important contributions. First, we present a new algorithm for finding large itemsets which uses fewer passes over the data than classic algorithms, and yet uses fewer candidate itemsets than methods based on sampling. We investigate the idea of item reordering, which can improve the low-level efficiency of the algorithm. Second, we present a new way of generating "implication rules," which are normalized based on both the antecedent and the consequent and are truly implications (not simply a measure of co-occurrence), and we show how they produce more intuitive results than other methods. Finally, we show how different characteristics of real data, as opposed to synthetic data, can dramatically affect the performance of the system and the form of the results.
Condorcet: a man of the avant-garde, Appl. Stochastic Models Data Anal
- P Michaud
P. Michaud, Condorcet: a man of the avant-garde, Appl. Stochastic Models Data Anal. 3 (1987) 173±198.
Data Mining with Neural Networks
- Joseph P Bigus
Joseph P. Bigus, Data Mining with Neural Networks, McGraw-Hill, New York, 1996.
Audit trail pattern analysis for detecting suspicious process behavior
- Andreas Wespi
- Marc Dacier
- Hervé Debar
- M Mehdi
- Nassehi
Andreas Wespi, Marc Dacier, Herv e Debar, Mehdi M. Nassehi, Audit trail pattern analysis for detecting suspi-cious process behavior, in: Proceedings of the First
NetRanger User's Guide
- Cisco Systems
- Inc
Cisco Systems, Inc., NetRanger User's Guide, 1999.