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Abstract

Security systems for databases produce numerous alerts about anomalous activities and policy rule violations. Prioritizing these alerts will help security personnel focus their efforts on the most urgent alerts. Currently, this is done manually by security experts that rank the alerts or define static risk scoring rules. Existing solutions are expensive, consume valuable expert time, and do not dynamically adapt to changes in policy. Adopting a learning approach for ranking alerts is complex due to the efforts required by security experts to initially train such a model. The more features used, the more accurate the model is likely to be, but this will require the collection of a greater amount of user feedback and prolong the calibration process. In this paper, we propose CyberRank, a novel algorithm for automatic preference elicitation that is effective for situations with limited experts' time and outperforms other algorithms for initial training of the system. We generate synthetic examples and annotate them using a model produced by Analytic Hierarchical Processing (AHP) to bootstrap a preference learning algorithm. We evaluate different approaches with a new dataset of expert ranked pairs of database transactions, in terms of their risk to the organization. We evaluated using manual risk assessments of transaction pairs, CyberRank outperforms all other methods for cold start scenario with error reduction of 20%.

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... To address this challenge, we suggest incorporating the concept of diversity from recommendation systems (Matt et al. 2014) into logging policies. Unlike search engines or recommendations, sampling a more diverse group of users is not technically complicated as the user's transactions risk can be aggregated to a single score (Grushka-Cohen et al. 2016;Evina et al. 2019). However, logging capacity is constrained, and by focusing solely on diversity, undocumented malicious activity in the high risk group can be missed. ...
... When an SO assigns risk to a transaction, various contextual information is used, such as time of day, user activity profile, location (IP address), the nature of the activity (i.e. is it permitted), data sensitivity, and the resulting data volume. When a DAM system is installed in an organization these rules can be defined manually (by the SO) as a risk policy or learned by annotating risk scores on some representative transaction using a classifier such as CyberRank (Grushka-Cohen et al. 2016). ...
... To reduce the complexity of the features for comparison and evaluation, working with aggregated data is useful. Previous work such as (Grushka-Cohen et al. 2016;Evina et al. 2019) leveraged SO knowledge to aggregate database activity into a single risk score. (Grushka-Cohen et al. 2019) suggested a simulation package made of low complexity data where the user activity for a time frame is represented by a single aggregated risk score. ...
Preprint
Full-text available
Database activity monitoring (DAM) systems are commonly used by organizations to protect the organizational data, knowledge and intellectual properties. In order to protect organizations database DAM systems have two main roles, monitoring (documenting activity) and alerting to anomalous activity. Due to high-velocity streams and operating costs, such systems are restricted to examining only a sample of the activity. Current solutions use policies, manually crafted by experts, to decide which transactions to monitor and log. This limits the diversity of the data collected. Bandit algorithms, which use reward functions as the basis for optimization while adding diversity to the recommended set, have gained increased attention in recommendation systems for improving diversity. In this work, we redefine the data sampling problem as a special case of the multi-armed bandit (MAB) problem and present a novel algorithm, which combines expert knowledge with random exploration. We analyze the effect of diversity on coverage and downstream event detection tasks using a simulated dataset. In doing so, we find that adding diversity to the sampling using the bandit-based approach works well for this task and maximizing population coverage without decreasing the quality in terms of issuing alerts about events.
... Furthermore, even if the transaction data is compressed, the sheer volume of transactions in corporate or government grade production systems prohibits saving all of the data, particularly due to cost considerations. Techniques for sampling and their effects on anomaly detection have been studies in the domain of network traffic flow [4,7,11] and the domain of Cyber security for Internet page retrieval. However, these domains are quite different from the domain of database transaction as the data is richer, containing more features, and the damage from a single transaction can be greater than the damage from a network packet. ...
... It has been established that sampling introduces bias to anomaly detection. In a previous work [4] we suggested a Gibbs sampling approach using the transaction risk as the prior for sampling it, an approach we test using the new simulation environment. ...
... We aimed to simulate a system where for each time frame users transactions are represented by the risk of his activity during that time frame. The risk can be assessed for transactions using a rule based scoring policy or a ranking approach such as CyberRank [4]. According to security experts interviewed during the development of the simulation users behavior is not random, but has trends both in the activity volume and the risk the activity presents. ...
Chapter
Monitoring database activity is useful for identifying and preventing data breaches. Such database activity monitoring (DAM) systems use anomaly detection algorithms to alert security officers to possible infractions. However, the sheer number of transactions makes it impossible to track each transaction. Instead, solutions use manually crafted policies to decide which transactions to monitor and log. Creating a smart data-driven policy for monitoring transactions requires moving beyond manual policies. In this paper, we describe a novel simulation method for user activity. We introduce events of change in the user transaction profile and assess the impact of sampling on the anomaly detection algorithm. We found that looking for anomalies in a fixed subset of the data using a static policy misses most of these events since low-risk users are ignored. A Bayesian sampling policy identified 67% of the anomalies while sampling only 10% of the data, compared to a baseline of using all of the data.
... We propose using a sampling strategy based on the perceived risk posed by each transaction to the organization. The risk can be estimated using a manually calibrated policy or estimated using a machine learning ranking algorithm such as CyberRank [12]. ...
... The users can be ether application data base user or a real user. As described in our CyberRank work [12], the user is an important entity whose behavior and activity is useful for identifying risk and controlling database transactions. When a transaction occurs, it is compared to the user's history to detect anomalies. ...
... The risk captures the likelihood that an SO would investigate the anomaly [12], however the investigation will be more thorough if low-risk transactions are also captured for the suspect user. ...
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Data leakage and theft from databases is a dangerous threat to organizations. Data Security and Data Privacy protection systems (DSDP) monitor data access and usage to identify leakage or suspicious activities that should be investigated. Because of the high velocity nature of database systems, such systems audit only a portion of the vast number of transactions that take place. Anomalies are investigated by a Security Officer (SO) in order to choose the proper response. In this paper we investigate the effect of sampling methods based on the risk the transaction poses and propose a new method for "combined sampling" for capturing a more varied sample.
... For this third survey phase, ACM Digital Library citation database was additionally included in order to make the research more comprehensive and accurate. Only 2 relevant papers were found in ACM Digital Library [36,37]. ...
... According to the results, the new risk model reduces the number of incidents and allows security analysts to focus solely on a smaller number of actual and critical incidents, which consequently reduces the time and resources. In an article [36], a new algorithm for ranking cyber security alerts for databases is proposed. The goal was to develop an AHP prioritization method that can automatically rank alerts at the level of risk posed by a particular transaction, thus allowing the security professionals to focus their time and efforts on the most important alerts. ...
... In this task, the model's goal is to predict the user who submitted the query containing a particular operator. While the DBMS generally knows the user submitting a query, such a classifier is useful for determining when a user-submitted query does not match the queries usually submitted by that user, a common learning task in database intrusion detection [6,18,23,33]. ...
... SageDB [57] proposes integrating machine learning techniques into join processing, sorting, and indexing. Recent works in intrusion detection [6,18], index structures [26], SLA management [15,32,35,36,44,45,56], entity matching [42], physical design [39,47], and latency prediction [2,13,14,16,30,[60][61][62] have all employed machine learning techniques. With little exception, each of these works have included hand-engineered features derived for each particular task. ...
Preprint
Integrating machine learning into the internals of database management systems requires significant feature engineering, a human effort-intensive process to determine the best way to represent the pieces of information that are relevant to a task. In addition to being labor intensive, the process of hand-engineering features must generally be repeated for each data management task, and may make assumptions about the underlying database that are not universally true. We introduce flexible operator embeddings, a deep learning technique for automatically transforming query operators into feature vectors that are useful for a multiple data management tasks and is custom-tailored to the underlying database. Our approach works by taking advantage of an operator's context, resulting in a neural network that quickly transforms sparse representations of query operators into dense, information-rich feature vectors. Experimentally, we show that our flexible operator embeddings perform well across a number of data management tasks, using both synthetic and real-world datasets.
... Risk score can be assigned based on predefined list of rules and conditions. The science community has been extensively working on using machine learning models for risk scoring in many domains [18,6,4] For an ML risk score to be accepted by the medical community it is important that it allows experts to tweak it (add clinical features, local biases) and is explainable [14]. Clalit Health Services (CHS) created a risk-scoring tool to predict the severity of COVID-19. ...
Preprint
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Testing is an important part of tackling the COVID-19 pandemic. Availability of testing is a bottleneck due to constrained resources and effective prioritization of individuals is necessary. Here, we discuss the impact of different prioritization policies on COVID-19 patient discovery and the ability of governments and health organizations to use the results for effective decision making. We suggest a framework for testing that balances the maximal discovery of positive individuals with the need for population-based surveillance aimed at understanding disease spread and characteristics. This framework draws from similar approaches to prioritization in the domain of cyber-security based on ranking individuals using a risk score and then reserving a portion of the capacity for random sampling. This approach is an application of Multi-Armed-Bandits maximizing exploration/exploitation of the underlying distribution. We find that individuals can be ranked for effective testing using a few simple features, and that ranking them using such models we can capture 65% (CI: 64.7%-68.3%) of the positive individuals using less than 20% of the testing capacity or 92.1% (CI: 91.1%-93.2%) of positives individuals using 70% of the capacity, allowing reserving a significant portion of the tests for population studies. Our approach allows experts and decision-makers to tailor the resulting policies as needed allowing transparency into the ranking policy and the ability to understand the disease spread in the population and react quickly and in an informed manner.
... In other cases, the risk assessment is sometimes discussed. Others proposed to include risk assessment solutions in access control systems for verifying the risk level associated with access requests before according authorizations [5], [6]. We, among other authors are thoroughly studying the issue and discuss about correlation so that, once detected, these anomalies can be analyzed to determine the correlation between them. ...
Chapter
Database activity monitoring systems aim to protect organizational data by logging users’ activity to Identify and document malicious activity. High-velocity streams and operating costs, restrict these systems to examining only a sample of the activity. Current solutions use manual policies to decide which transactions to monitor. This limits the diversity of the data collected, creating a “filter bubble” over representing specific subsets of the data such as high-risk users and under-representing the rest of the population which may never be sampled. In recommendation systems, Bandit algorithms have recently been used to address this problem. We propose addressing the sampling for database activity monitoring problem as a recommender system. In this work, we redefine the data sampling problem as a special case of the multi-armed bandit problem and present a novel algorithm, C–\(\epsilon \)–Greedy, which combines expert knowledge with random exploration. We analyze the effect of diversity on coverage and downstream event detection using simulated data. In doing so, we find that adding diversity to the sampling using the bandit-based approach works well for this task, maximizing population coverage without decreasing the quality in terms of issuing alerts about events, and outperforming policies manually crafted by experts and other sampling methods.
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AI2: Training a big data machine to defend. Veeramachaneni K. and Arnaldo I. AI2: Training a big data machine to defend
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