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Detection of Attacks for Restoring Privacy of Users to Improve Mobile Collaborative Learning (MCL) Over Heterogeneous Network


Abstract and Figures

The paper highlights the problems of rogue dynamic host configuration protocol (DHCP) server that affects and disrupts network resources during communication. The rogue DHCP is unauthorized server that releases incorrect IP address to legal users and sniffs their traffic illegally. The paper introduces multi-frame signature-cum anomaly-based intrusion detection systems (MSAIDS) supported with novel algorithms and addition of new rules in existing IDS. This innovative security mechanism reinforces the confidence of users, protects network from illicit intervention. The work is particularly done to restore the privacy of users over MCL but can be implemented with all types of small to large networks. Finally, the paper validates the idea through test bed simulation and compares the findings with other well-known existing techniques.
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Detection of Attacks for Restoring Privacy of Users to
Improve Mobile Collaborative Learning (MCL) Over
Heterogeneous Network
Abdul Razaque and Khaled Elleithy
University of Bridgeport 126 Park Avenue,
Bridgeport, CT 06604 USA
Abstract. The paper highlights the problems of rogue dynamic host configuration
protocol (DHCP) server that affects and disrupts network resources during
communication. The rogue DHCP is unauthorized server that releases incorrect IP
address to legal users and sniffs their traffic illegally. The paper introduces multi-frame
signature-cum anomaly-based intrusion detection systems (MSAIDS) supported with
novel algorithms and addition of new rules in existing IDS. This innovative security
mechanism reinforces the confidence of users, protects network from illicit
intervention. The work is particularly done to restore the privacy of users over MCL but
can be implemented with all types of small to large networks. Finally, the paper
validates the idea through test bed simulation and compares the findings with other
well-known existing techniques.
Keywords: Mobile collaborative learning, DHCP server, rogue DHCP server, signature-
cum anomaly based Intrusion detection, sniffer.
1. Introduction
The rapid developments in information technologies (IT) have improvised the use of
mobile devices in open, large scale and heterogeneous environments. The mobile
devices build the bridge to connect users anytime and anywhere in the world. The
deployment of mobile devices has not only underpinned communication but also
created many chances for malicious attackers to crack the integrity and privacy of
users. The mobile users are highly dependent on DHCP server for issuance of IP
address because DHCP server provides highly organized service to mobile devices.
From other side, unauthorized DHCP server (rogue DHCP) creates the problems for
users and breaks the security. It invites intruders and attackers to redirect and intercept
network traffic of any device that uses the DHCP. Intruder modifies the original
contents of communication. The malware and Trojans horse install rogue DHCP
server automatically on network and affect the legitimate servers .If rogue DHCP
server assigns an incorrect IP address faster than original DHCP server, it causes
potentially black hole for users. To control the malicious attacks and avoiding the
network blockage, the network administrators put their efforts to guarantee the
components of server, using various tools. The graphical user interface (GUI) tool is
used to prevent the attack of rogue detection [5]. Idea of using multilayer switches
may be configured to control the attacks of rogue DHCP server but it is little bit
complex and not efficient to detect rogue DHCP server.
The DHCP spoofing is another solution for detecting rogue DHCP server. However, if
single segment is spoofed that can damage the whole network. Spoofing method takes
long time till intruder has enough time to capture the traffic and assign wrong IP
address [8]. Time-tested, DHCP Find’s, DHCP Sentry, Dhcploc.exe and
DHCP-probe provide the solution to detect and defend rogue DHCP server malware
[6]. All of these tools cannot detect the new malicious attacks [2].
Distributed Intrusion Detection System (DIDS) is another technique to support the
mobile agents. This technique helps the system to sense the intrusion from incoming
and outgoing traffics to detect known attacks [1]. Ant colony optimization (ACO)
based distributed intrusion detection system is introduced to detect intrusions in the
distributed environments. It detects the visible activities of attackers and identifies the
attack of false alarm rate but it does not detect DOS attacks [3]. Anomaly based
intrusion detection are introduced to detect those attacks for which no signatures exist
[4], [6], [10]. This paper introduces the multi-frame signature-cum anomaly based
intrusion detection system supported with novel algorithms, inclusion of new rules in
existing IDS to detect malicious attacks and increase the privacy and confidentiality
of users. The reminder of paper is organized as follows: The section 2 describes
related work and background study. Possible attacks of rogue DHCP server are
explained in Section 3. The proposed solutions including functional components are
given in section 4. Simulation setup is explained in section 5. The analysis of result
and discussion are given in section 6. Finally conclusion of the paper is given in
section 7.
2. Related Work and Background Study
The modern technologies and its deployment in computer and mobile devices have
not only created new opportunities for better services but from other perspective,
privacy of the users is highly questionable. The network-intruder and virus contagion
extremely affect the computer systems and its counterparts. They also alter the top
confidential data. Handling these issues and restoring the security of systems, IDS are
introduced to control malicious attackers.IDS are erroneous and not providing the
persistent solution in its current shape. The first contribution in the field of intrusion
detection was deliberated by J.P Anderson in [7].The author introduced notion about
the security of computer systems and related threats. Initially, three attacks were
discovered that are misfeasors, external penetrations and internal penetrations.
The classification of typical IDS is discussed in [17]. The focus of the contribution is
about reviewing the agent-based IDS for mobile devices. They have stated problems
and strength of each category of classification and suggested the methods to improve
the performance of mobile agent for IDS design.
Four types of attacks are discussed in [21] for security of network. They have also
simulated the behavior of these attacks by using simulation of ns2. A multi-ant
colonies technique is proposed in [22] for clustering the data. It involves independent,
parallel ant colonies and a queen ant agent. Authors state that each process for ant
colony takes dissimilar forms of ants at moving speed. They have generated various
clustering results by using ant-based clustering algorithm. The findings show that
outlier’s lowest strategy for choosing the recent data set has the better performance.
The contribution covers the clustering-based approach.
The work done in [18] is about the framework of distributed Intrusion Detection
System that supports mobile agents. The focus of work is to sense both outside and
inside network division. The mobile-agents control remote sniffer, data and known
attacks. The paper has introduced data mining method for detection and data analysis.
Dynamic Multi-Layer Signature based (DMSIDS) is proposed in [2]. It detects
looming threats by using mobile agents. Authors have introduced small and well-
organized multiple databases. The small signature-based databases are also updated at
the same time regularly.
In addition, all of the proposed techniques cover general idea of network
detection but proposed MSAIDS technique handles the irreplaceable issues of DHCP
rogue server. The contribution also prevents almost all types of DOS attacks. The
major contribution of work is to validate technique by employing innovative
algorithms and inclusion of new rules in existing tradition. It also helps the legitimate
users to start secure and reliable communication frequently over MCL. One of the
most promising aspects of this research is uniqueness because there is no single
contribution is available in survey about the DHCP rogue and its severe targeted
3. Possible Attacks of Rogue DHCP Server
The introduction of distributed system has highly affected the security [11]. There are
several forms of vulnerabilities and vigorous threats to expose the security of systems.
To take important security measures and enhancing the secure needs for
organizations, several mechanisms are implemented but those mechanisms also invite
attackers to play with privacy and confidentiality of users. One of the major threats
for privacy of data is intervention of rogue DHCP server. The first sign of problem
associated with rogue DHCP server is discontinuation of network service. The static
and portable devices start experiencing due to network issues. The issues are started
by assigning the wrong IP address to requested users to initiate the session.
The malicious attackers take the advantages of rogue DHCP server and sniff the
traffic sent by legitimate users. Rogue DHCP server spreads wrong network
parameters that create the bridge for intruders to expose the privacy. Trojans like
DNS-changing installs the rogue DHCP server and pollutes network. Rogue DHCP
server creates several problems to expose the privacy of legitimate users. We highlight
two major types of security attacks to be created by rogue DHCP server.
3.1. Sniffing the Network Traffic
It is brutal irony in information security that the features which are used to protect
static and portable devices to function in efficient and smooth manner; and from other
side, same features maximize the chances for intruders to compromise and exploit the
same tools and networks. Hence packet sniffing is used to monitor network traffic to
prevent the network from bottleneck and make an efficient data transmission.
Intruders use same resources for collecting information for illegal use. Rogue DHCP
server helps malicious intruders to expose privacy of users. When networks are victim
of rogue DHCP server that provides very important information related to IP address,
domain name system and default gateway to intruders.
All of this information helps intruders to sniff traffic of legitimate users. Rogue
DHCP server is introduced on secure environment to collect confidential information
and sniffs the traffic and wreaks the privacy of users shown in figure 1.
Figure 1. Sniffing the traffic and masquerading attack
Rogue DHCP server also facilitates for intruders to capture the MAC address of
legitimate users. It causes sniffing the traffic through switch. In this case, intruder
spoofs IP addresses of both sender and receiver and plays the man-of-middle to sniff
traffic and extract important contents of communication. It causes the great attack on
privacy of users.
3.2. Denial of Service Attack (DOS)
Intruder gets support through rogue DHCP server also uses DOS attacks after sniffing
confidential contents of traffic. Due to DOS attack, the access of important services
for legitimate users is blocked. Intruder often crashes routers, host, servers and other
computer entity by sending overwhelming amount of traffic on the network. Rogue
DHCP server creates friendly environment for intruder to launch DOS attacks because
intruder needs small effort for this kind of attack and it is also difficult to detect and
attack back to intruder [12]. In addition, it is also easy to create floods on internet
because it is comprised of limited resources including processing power, bandwidth
and storage capabilities. Rogue DHCP makes flooding attack at domain name system
(DNS) because target of intruder is to prevent the legitimate users [12] & [16].
These attacks on DNS have obtained varying success while disturbing resolution of
names related to targeted zone. Rogue DHCP server takes advantages of inevitable
human errors during installation, configuration and developing software. It creates
several types of DOS attack documented in literature [20]. Intruder with support of
Rogue DHCP server makes three types of attacks: fragile (smurf), SYN Flood and
DNS DOS attacks shown in figure 2. These attacks are vulnerable and dangerous for
security point of view.
Fig.ure 2.denial of service attack (DOS) attack
4. Proposed Solution (Multi-frame Signature-cum-anomaly
based Intrusion detection system)
Networks are being converged rapidly and thousands of heterogeneous devices
are connected. The devices integrated in large networks, communicate through
several types of protocols and technologies. This large scale heterogeneous
environment invites the intruders to expose security of users. Hence, IDS are
introduced to recognize the patterns of attacks, if they are not fixed strategically,
many intruders cross IDS by traversing alternate route in network.
Many signature-based IDS are available to detect attacks but some of new attacks
cannot be identified and controlled. Anomaly-based IDS is another option but it
only detects limited new attacks. The multi-frame signature-cum anomaly-based
intrusion detection system (MSAIDS) supported with algorithms is proposed to
resolve issue of DHCP rogue. The proposed framework consists of detecting
server that controls IDS and its related three units: (i) DHCP verifier unit (ii)
signature database (iii) anomaly database.
During each detection process, intrusion detection starts matching from DHCP
verifier, if any malicious activity is detected that stops process otherwise checks
with two units until finds either malicious activity or not. Figure 3 shows
Figure 3. Multi-frame signature-cum-anomaly based IDS
The detecting server (DS) is responsible to check inbound and outbound traffic for
issuance of IP address. The DS gets IP request (inbound traffic) from routers and
forwards to DHCP server after satisfactory checkup. When any IP address is released
for requested node then applies DHCP detecting algorithm for validation of DHCP
server and detecting types of attack shown in algorithm 1.
Algorithm 1: Verify DHCP server and detecting the attack
1. Input: MF =(FD, FS,FA & I)
2. Output : For every strategy I € FA, I € FS, D € FD)
3. D = Each valid DHCP Server
4. IP= Internet protocol address
5. N= Number of mobile devices
6. FD= Frame DHCP server
7. If D € FD
8. IP→ N
9. endif
10. S= Number of available signatures in signature based Intrusion detection
system (SIDS)
11. FS= Frame of signatures
13. I= Number & Types of attacks
14. For ( I=S; I ≤ FS; I++)
15. If I FS
16. SIDS attack alert
17. endif
18. endfor
19. A= Number of signatures available in Anomaly based Intrusion detection
system AIDS
20. FA= Frame of AIDS
22. For ( I =A; I ≤ FA ; I ++)
23. If I FA
24. AIDS raises alert
25. If ( I FS & I FA)
26. No alert ( No attack)
27. endif
28. endif
29. endfor
4.1. Monitoring Process of Detecting Server ( DS)
The following rules collectively function to determine the anomalies.
i. Pre-selected rules: They help to detect those patterns, which are already
stored in DS that apply to identify the inbound traffic.
ii. Post-selected rules: They refer to those patterns which are stored for
detection of legitimate DHCP server that help to identify outbound traffic.
iii. Parameterized rules: They refer to many ingredients that help to set
selected rules with unique value presented in the following:
a. Validity ingredient: It helps to detect attack if intruder modifies the contents
of message.
b. Time interval ingredient: It helps to detect two types of attacks which are
exhaustion attack and negligence attack. In exhaustion attack, the intruder
increases message-sending rate. In negligence attack, intruder does not send
the message. In addition, time interval for two consecutive messages is
increased or decreased than allowed amount of time that gives sign of attack.
c. Flooding ingredient: It helps to identify attack on basis of noise and
disturbance to be created in communication channel.
d. Retransmission ingredient: It helps to determine attack, if retransmission
does not occur before specified timeout period.
e. High transmission radio range ingredients: It helps to determine SYN
flood and wormhole attack, when intruder uses powerful radio sending
message to further located node.
f. Pattern replication ingredient: It helps to detect attack when same patterns
are repeated several time, it blocks the DOS attacks.
All of these ingredients collectively help to DS for detecting the attacks and figure 4
shows the process how to determine valid IP address and attack.
Figure 4. Detecting attack and issuance of safe IP
DS also controls the multi frame that comprises of central IDS and integrated
with three layers that control the misuse detection.
4.2. Central IDS
The aim of central IDS is to control and store messages received from DS. It works as
middleware for DS and other layers to send the verification request and receive alerts.
The main function of central IDS is to update and manage the policy according to
nature of attacks. If it needs any change in attack-detection that is employed on all the
layers. The central IDS implements updated policy is shown in figure no 5.
Figure 5. Policy of Central IDS for network
4.3. DHCP Verifier
DHCP verifier is top layer that distinguishes between rogue DHCP and original
DHCP server. The signatures of original DHCP servers are stored at the DHCP
verifier. It checks validity of DHCP server that issues IP address for client. On basis
of stored signatures, DHCP server is identified whether it is rogue or original DHCP
server. Top layer produces unique sign of alert for both DHCP rogue and original
DHCP. Top layer receives parameters for verification from central IDS. DHCP
verifier running on top layer is also responsible to return alert to central IDS.
4.4. Signature Based Detection Layer
Signature-based detection is middle layer that detects known threats. It compares
signatures with observed events to determine possible attacks. Some known attacks
are identified on basis of implemented security policy. For example, if telnet tries to
use “root’ username that is violating security policy of organization that is considered
known attack. If operating system has 645 status code values that is sign of host’s
disabled auditing and refers as attack. If attachment is with file name “freepics.exe”
that is alert of malware. Middle layer is effective for detection of known threats and
using well-defined signature patterns of attack. The stored patterns are encoded in
advance to match with network traffics to detect attack. This layer compares log entry
with list of signatures by deploying string comparison operation. If signature based
layer does not detect attack, anomaly based detection layer starts to process.
4.5. Anomaly Based Detection Layer
Lower layer is anomaly based detection that identifies unknown and DOS attacks. It
works on pick-detect method. This method monitors inbound and outbound traffic
Packets are evaluated, adaptive thresholds and mean values are set. It calculates the
metrics and compares with thresholds [19]. On basis of comparisons, it detects
various types of anomalies including false positive, false negative, true positive and
true negative. If pick-detect methods determines true positive and false negative then
it sends alert to Central IDS. The process of detecting anomalies is given in algorithm
Algorithm 2: Detecting the types of alerts with AIDS
1. FS= Frame of signatures
3. Si = False Negative
4. Sj= True negative
5. Sk= True positive
6. Sk= False positive
7. 0 = don’t match & 1= match
8. Sijkl = 1/d Sijkl
9. Sij = { 0, if i & j
10. No false negative & true positive
11. Sij = { 1, if i & j
12. false negative & true positive
13. Alert of attack
14. Skl = { 0, if k & l [ do not match] & 1, if k & l [match]
15. Alert of true negative & false positive
16. No sign of attack
17. endif
18. endif
19. endif
20. endif
In addition to determine and calculate value of true positive and false negative; we
apply algorithm 3 that helps to find attack and non-attack situation for TN and FN.
Algorithm 3: Determine the sign of attack or non-sign of attack
I. We select random odd prime number for TN and any even number for FN.
2. The value of FN must not be exceeded than TN.
3. Therefore, FN > 1 & FN< TN
4. Here, FN= {2, 4, 6, 8…} & TN= {3, 5, 7, 11, 13…}
5. Here sign of attack = ST, d = not exposed & b = exposed.
6. b and d has constant value 1.
7. Thus, ST = TN/ (TN+ d)/ FN (FN +b)
8. If value of ST > 1, it means there is no sign of attack, if the value of ST < 1 that
is sign of attack.
9. endif
Assume FN = 2 & TN =3: BY applying the sign of attack formula:
ST = TN/ (TN+ d)/ FN (FN +b): Substitute the values in given formula.
ST = 3/ (3+ 1) / 2(2+1)
ST = 9/8
ST= 1.125
ST > 1
Here, ST > 1 means there is no sign of attack and we will be able to determine that
is True negative (TN).
5. Simulation Setup
The previous sections have presented evidence of problems to be created by rogue
DHCP server and including solutions to control problems. This section focuses on
simulation setup and type of scenario. To validate the approach, the proposed solution
has been implemented by using three methods: test bed simulation, discrete
simulation in C++ and ns2 simulation.
We discuss only test bed simulation in this paper that provides real time results in
controlled and live user environments. This kind of simulation gives complete
understanding about behavior of several types of attacks. All operations associated
with MSAIDS approach and other three existing approaches: Dynamic Multi-Layer
Signature based IDS (DMSIDS), Ant Colony Optimization based IDS (ACOIDS) &
Signature based IDS provide the recital idea. The parameters of test bed simulation are
only given in table 1.
Table 1. Simulation parameters for test bed experiment.
Name of parameters Specification
MySQL database MySQL 5.5
Type of IDS Rule based IDS
GD Library gd 2.0.28
Snort V-2
Apache web server Apache http 2.0.64 Released
PHP PHP 5.3.8 (Server side language)
ADODB Release 5.12 (abstraction library for PHP
and Python)
Stick Stick beats detection tool used by
Nikito Nikito v.2.1.4
IDS enabled system
Memory: 512 MB
Operating sy
tem: Linux
PCI network card:
10/100 Mbps
III with 600 MHz
Attacker sy
Memory: 1.5 GB
Operating sy
tem: Linux
PCI network card:
10/100/1000 Mbps
CPU: AMD Geode LX running
In addition, most of operating systems do not provide the tracing facilities but
regardless of problems, we would like to obtain result in standardized method by
using different programs on different operating systems. MSAIDS has fully support
of algorithms and data structure that discover potential attacks and perturb the
intrusions before the attacks. The performance highly depends on robust tracing
facility and algorithms, which help to identify the intrusion. The first step is to
analyze performance of proposed algorithms. However, overall target is to obtain
accurate statistical data in highly loaded network. Test bed simulation provides
promising result. The mean value is calculated with help of following theorem.
Theorem 1:
Assume x = test bed simulation;
y= discrete simulation in C++ & ns2 simulation.
R is the proposed approach MASIDS.
Thus, Let f: [x, y] → R is the continuous function for closed interval [x, y]
Let f: [x, y] → R is the differentiable continuous function for open interval (x, y)
Here x < y.
Hence z exists in (x, y)
Such that
f’ (x) = f (x) – f (y)/( x- y)
6. Analysis of Result and Discussion
The training period of experiment covers four classes of attacks probe, DOS, U2R
and R2L. All detected attacks are included in database during the training period in
test bed simulation. The MSAIDS scans all rules of snort and includes new rules
explained in proposed section 4. The testing period targets one concise scenario. The
scenario is simulated by using same parameters for all three existing approaches
including our proposed MSAIDS. The attacks are generated by using stick, covering
all types of signatures and anomaly based attacks. The training period provides quite
interesting results because frequently generated attacks are of different numbers. The
maximum number of attacks pertains to R2L category. The more attacks are also
counted on MSAIDS as compare with other three existing techniques are shown in
table no.2. If attack is not generated then it is counted as normal traffic. The
frequency of single and group characters are displayed when packets reach at the
intruder’s machine. It is observed on the basis of output that different types of
detected attacks are generated due to rogue DHCP server.
The DOS attacks are detected when packet does not reach at destination and received
no acknowledgment. The sign of probe attack is addition of new data in existing
amount of data bytes. U2R is the sign of maximum connection duration. R2L attacks
are little bit complex to detect. We apply method comprises of service requested and
duration of connection for network and attempts failed login for host. It shows that
proposed approach does not restrict the generating ratio of packets. From other side,
the proposed approaches provide highest capturing ratio. The statistical results show
that MSAIDS will substantiate to medical field for diagnosing several disease and
especially for heart. The major breakthrough of this research is to detect the true
positive and false negative attacks because they are very hard to capture.
Table 2. Showing statistical data for attacks.
The proposed method also captures real worm and all other looming attacks. Another
advantage of MSAIDS is to detect all types of anomalies and unknown threats
efficiently. It creates specific alarm for each type of anomalies. MSAIDS is robust and
persist when attack is detected. Data shows 99.996% efficiency of MSAIDS shown in
figure 6. The MSAIDS gives 2.269 to 49.11 higher efficiency than other techniques.
Figure 6. Comparison efficiency of all approaches
Types of attack (MSAIDS) (DMSIDS) (ACOIDS) Signature based IDS
DOS attacks 34214 33542 33421 32741
U2R attacks 12454 11874 11845 11341
R2L attacks 34123 32123 31092 29984
Probe attacks 6214 8758 10181 4907
7. Conclusion and Future Works
In this paper, multi-frame signature-cum anomaly-based intrusion detection
systems (MSAIDS) is presented. MSAIDS controls malicious activities of DHCP
rogue server to restore the privacy of users. The paper highlights malicious threats to
be generated by DHCP rogue. The intruders use DHCP server to sniff traffic and
finally deteriorate confidential information. The mechanism of current IDS does not
have enough capability to control several types of malicious threats. Furthermore,
several daunting and thrilling challenges in the arena of computer network security
are impediment for secure communication. DHCP rogue is visibly very simple but
crashes network as well as privacy of the users and even creates nastier attacks like
Sniffing network traffic, masquerading attack, shutting down systems and DOS. The
first is detailed explanation of attacks and how to resolve this issue. Second, we
propose technique that is based on algorithms and addition of new rules in existing
current IDS. These all of the components of proposal collectively handle the issues of
DHCP rogue.
To validate the proposal, the technique is simulated by using test bed. Two different
kinds of systems are used in test bed; as one is reserved for intruder and other one is
for legitimate user. On basis of simulation, we obtain very interesting data, which
show that MSAIDS improves capturing performance and controls attacks to be
generated by DHCP rogue as compare with original IDS and other well known
techniques. The findings demonstrate that MSAIDS has significantly reduced false
alarms. Finally, we analyze overall efficiency of MSAIDS and existing techniques. In
future, this technique will be deployed to measure the heart beats and cancer.
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With emergence of the latest technology, need for digital documents, images and medias has greatly increased. On the other hand, there has been great demand of protecting those documents. The researchers proposed several approaches to protect those valuable documents. The two most exciting available approaches are to remove the devices and online storage. However, there is problem occurring with those external devices because they are taking longer time for burning and copying the data files. In addition, there is huge possibility of data loss when burning and copy the files. Online storage provides the reasonable solution, but it is costly when storing the large amount of data. This paper introduces the novel idea of a back-up file system that allows users to store the files on extra disk space. Thus, the Black-box is safe idea to backup the secret and valuable documents. The Black-box approach is supported by redundancy mechanism that involves the erasure coding and replication. The redundancy mechanism also improves the storage and computational capacity. The proposed idea is validated using MATLAB simulation.
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2 Gwalior, India. Here we present a framework of distributed Intrusion Detection System (IDS), supported by mobile agents, which senses intrusion both from inside and outside the network division. Mobile agents act as via channel to control the remote sniffers and they collect, clean network data as well as detect known attacks. Remaining data is sent back for analysis to the main station for the detection of new attacks. Data Mining is used for data analysis and for the detection of new intrusive behaviors in this proposed work. The two main branches of it are: Network Intrusion Detection Component which is based on data mining and the platform for mobile agent.
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Intruders computers, who are spread across the Internet have become a major threat in our world, The researchers proposed a number of techniques such as (firewall, encryption) to prevent such penetration and protect the infrastructure of computers, but with this, the intruders managed to penetrate the computers. IDS has taken much of the attention of researchers, IDS monitors the resources computer and sends reports on the activities of any anomaly or strange patterns. The aim of this paper is to explain the stages of the evolution of the idea of IDS and its importance to researchers and research centres, security, military and to examine the importance of intrusion detection systems and categories , classifications, and where can put IDS to reduce the risk to the network.
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Intrusion Detection Systems (IDS) is defined as a c omponent that analyses system and user op- erations in computer and network systems in search of activities considered undesirable from se- curity perspectives. Applying mobile agent (MA) to intrusion detection design is a recent devel- opment and it is aimed at effective intrusion detec tion in distributed environment. From the litera- ture, it is clear that most MA-based IDS that are a vailable are not quite effective because their time to detection is high and detect limited intrus ions.This paper proposes a way of classifying a typical IDS and then strategically reviews the exis ting mobile agent-based IDSs focusing on each of the categories of the classification, for exampl e architecture, mode of data collection, the tech- niques for analysis, and the security of these inte lligent codes. Their strengths and problems are stated wherever applicable. Furthermore, suggested ways of improving on current MA-IDS de- signs are presented in order to achieve an efficien t mobile agent-based IDS for future security of distributed network.
Conference Paper
Intrusion Detection Systems (IDS) is defined as a component that analyses system and user operations in computer and network systems in search of activities considered undesirable from security perspectives. Applying mobile agent (MA) to intrusion detection design is a recent development and it is aimed at effective intrusion detection in distributed environment. From the literature, it is clear that most MA-based IDS that are available are not quite effective because their time to detection is high and detect limited intrusions.This paper proposes a way of classifying a typical IDS and then strategically reviews the existing mobile agent-based IDSs focusing on each of the categories of the classification, for example architecture, mode of data collection, the techniques for analysis, and the security of these intelligent codes. Their strengths and problems are stated wherever applicable. Furthermore, suggested ways of improving on current MA-IDS designs are presented in order to achieve an efficient mobile agent-based IDS for future security of distributed network.
Voice over IP (VoIP) systems is a popular technology for transmitting voice traffic over IP networks in the 3G world. VoIP is an efficient mode of communication; on a smaller scale it can also be a useful solution for businesses looking to trim their telephone expenses. In spite of its advantages, it is subjected to different kinds of intrusions. There have been enormous strides made in the field of intrusion detection systems (IDS) for different components of the information technology infrastructure. VoIP systems pose several new challenges to the existing IDS. We introduce Cross-Layer Mechanism and a fresh Intrusion Detection system which can be extended to other Intrusion Detection Systems. This system requires aggregation of protocols and is very efficient for VoIP systems because they involve multiple protocols and several attacks are based on sequences that cross protocol boundaries. Since VoIP systems use multiple application layer protocols, horizontal cross-protocol correlation is required. The architecture of the system proposed monitors all incoming network traffic and is classified into events. The events can potentially have state information and encapsulate information from multiple packets. The protocols used are SIP for call management and RTP for real-time audio data transfer. And our main concentration would be on Denial-Of-Service attacks.
This paper presents a multi-ant colonies approach for clustering data that consists of some parallel and independent ant colonies and a queen ant agent. Each ant colony process takes different types of ants moving speed and different versions of the probability conversion function to generate various clustering results with an ant-based clustering algorithm. These results are sent to the queen ant agent and combined by a hypergraph model to calculate a new similarity matrix. The new similarity matrix is returned back to each ant colony process to re-cluster the data using the new information. Experimental evaluation shows that the average performance of the aggregated multi-ant colonies algorithms outperforms that of the single ant-based clustering algorithm and the popular K-means algorithm. The result also shows that the lowest outliers strategy for selecting the current data set has the best performance quality.
This paper details an essential component of a multi-agent distributed knowledge network system for intrusion detection. We describe a distributed intrusion detection architecture, complete with a data warehouse and mobile and stationary agents for distributed problem-solving to facilitate building, monitoring, and analyzing global, spatio-temporal views of intrusions on large distributed systems. An agent for the intrusion detection system, which uses a machine learning approach to automated discovery of concise rules from system call traces, is described.We use a feature vector representation to describe the system calls executed by privileged processes. The feature vectors are labeled as good or bad depending on whether or not they were executed during an observed attack. A rule learning algorithm is then used to induce rules that can be used to monitor the system and detect potential intrusions. We study the performance of the rule learning algorithm on this task with and without feature subset selection using a genetic algorithm. Feature subset selection is shown to significantly reduce the number of features used while improving the accuracy of predictions.