AIDIS: Detecting and Classifying Anomalous Behavior in UbiquitousKernel Processes

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Targeted attacks on IT systems are a rising threat against the confidentiality, integrity, and availability of critical information and infrastructures. With the rising prominence of advanced persistent threats (APTs), identifying and under-standing such attacks has become increasingly important. Current signature-based systems are heavily reliant on fixed patterns that struggle with unknown or evasive applications, while behavior-based solutions usually leave most of the interpretative work to a human analyst.In this article we propose AIDIS, an Advanced Intrusion Detection and Interpretation System capable to explain anomalous behavior within a network-enabled user session by considering kernel event anomalies identified through their deviation from a set of baseline process graphs. For this purpose we adapt star-structures, a bipartite representation used to approximate the edit distance be-tween two graphs. Baseline templates are generated automatically and adapt to the nature of the respective operating system process.We prototypically implemented smart anomaly classification through a set of competency questions applied to graph template deviations and evaluated the approach using both Random Forest and linear kernel support vector machines.The determined attack classes are ultimately mapped to a dedicated APT at-tacker/defender meta model that considers actions, actors, as well as assets and mitigating controls, thereby enabling decision support and contextual interpretation of ongoing attack

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Searching System Call Information for Clues: The Effects of Intrusions of Processes

The United States Air Force extensively uses information systems as a tool managing and maintaining its information. The increased dependence on these systems in recent years has necessitated the need for protection front threats of information warfare and cyber terrorism. One type of protection utilizes intrusion detection systems to provide indications that intrusive behavior has occurred. Other types of protection may include packet filtering, cryptography and strong user authentication. Traditional approaches toward intrusion detection rely on features that are external to computer processes. By treating processes as black-boxes, intrusion detection systems may miss a wealth of information that could be useful for detecting intrusions. This thesis effort investigate the effectiveness of anomaly-based intrusion detection using system call information from a computational process. Previous work uses sequences of system calls to identity anomalies in processes. Instead of sequences of system calls, information associated with each system call is used to build a profile of normality that may be used to detect a process deviation. Such information includes parameters passed, results returned and the instruction pointer associated with the system call. Three methods of detecting deviations are evaluated for this problem. These include direct matching, relaxed matching and artificial immune system matching techniques. The test data used includes stack-based buffer overflows, heap-based buffer overflows and file binding race conditions. Results from this effort show that although attempted exploits were difficult to detect, certain actual exploits were easily detectable from system call information. In addition, each of the matching approaches provides some indication of anomalous behavior, however each has strengths and limitations. This effort is considered a piece of the defense-in- depth model of intrusion detection

Advanced Threat Intelligence: Interpretation of Anomalous Behavior in Ubiquitous Kernel Processes

Targeted attacks on digital infrastructures are a rising threat against the confidentiality, integrity, and availability of both IT systems and sensitive data. With the emergence of advanced persistent threats (APTs), identifying and understanding such attacks has become an increasingly difficult task. Current signature-based systems are heavily reliant on fixed patterns that struggle with unknown or evasive applications, while behavior-based solutions usually leave most of the interpretative work to a human analyst. This thesis presents a multi-stage system able to detect and classify anomalous behavior within a user session by observing and analyzing ubiquitous kernel processes. Application candidates suitable for monitoring are initially selected through an adapted sentiment mining process using a score based on the log likelihood ratio (LLR). For transparent anomaly detection within a corpus of associated events, the author utilizes star structures, a bipartite representation designed to approximate the edit distance between graphs. Templates describing nominal behavior are generated automatically and are used for the computation of both an anomaly score and a report containing all deviating events. The extracted anomalies are classified using the Random Forest (RF) and Support Vector Machine (SVM) algorithms. Ultimately, the newly labeled patterns are mapped to a dedicated APT attacker–defender model that considers objectives, actions, actors, as well as assets, thereby bridging the gap between attack indicators and detailed threat semantics. This enables both risk assessment and decision support for mitigating targeted attacks. Results show that the prototype system is capable of identifying 99.8% of all star structure anomalies as benign or malicious. In multi-class scenarios that seek to associate each anomaly with a distinct attack pattern belonging to a particular APT stage we achieve a solid accuracy of 95.7%. Furthermore, we demonstrate that 88.3% of observed attacks could be identified by analyzing and classifying a single ubiquitous Windows process for a mere 10 seconds, thereby eliminating the necessity to monitor each and every (unknown) application running on a system. With its semantic take on threat detection and classification, the proposed system offers a formal as well as technical solution to an information security challenge of great significance.The financial support by the Christian Doppler Research Association, the Austrian Federal Ministry for Digital and Economic Affairs, and the National Foundation for Research, Technology and Development is gratefully acknowledged

Cyberthreats, Attacks and Intrusion Detection in Supervisory Control and Data Acquisition Networks

Supervisory Control and Data Acquisition (SCADA) systems are computer-based process control systems that interconnect and monitor remote physical processes. There have been many real world documented incidents and cyber-attacks affecting SCADA systems, which clearly illustrate critical infrastructure vulnerabilities. These reported incidents demonstrate that cyber-attacks against SCADA systems might produce a variety of financial damage and harmful events to humans and their environment. This dissertation documents four contributions towards increased security for SCADA systems. First, a set of cyber-attacks was developed. Second, each attack was executed against two fully functional SCADA systems in a laboratory environment; a gas pipeline and a water storage tank. Third, signature based intrusion detection system rules were developed and tested which can be used to generate alerts when the aforementioned attacks are executed against a SCADA system. Fourth, a set of features was developed for a decision tree based anomaly based intrusion detection system. The features were tested using the datasets developed for this work. This dissertation documents cyber-attacks on both serial based and Ethernet based SCADA networks. Four categories of attacks against SCADA systems are discussed: reconnaissance, malicious response injection, malicious command injection and denial of service. In order to evaluate performance of data mining and machine learning algorithms for intrusion detection systems in SCADA systems, a network dataset to be used for benchmarking intrusion detection systemswas generated. This network dataset includes different classes of attacks that simulate different attack scenarios on process control systems. This dissertation describes four SCADA network intrusion detection datasets; a full and abbreviated dataset for both the gas pipeline and water storage tank systems. Each feature in the dataset is captured from network flow records. This dataset groups two different categories of features that can be used as input to an intrusion detection system. First, network traffic features describe the communication patterns in a SCADA system. This research developed both signature based IDS and anomaly based IDS for the gas pipeline and water storage tank serial based SCADA systems. The performance of both types of IDS were evaluates by measuring detection rate and the prevalence of false positives

Survival in the e-conomy: 2nd Australian information warfare & security conference 2001

This is an international conference for academics and industry specialists in information warfare, security, and other related fields. The conference has drawn participants from national and international organisations

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An Interactive Relaxation Approach for Anomaly Detection and Preventive Measures in Computer Networks

It is proposed to develop a framework of detecting and analyzing small and widespread changes in specific dynamic characteristics of several nodes. The characteristics are locally measured at each node in a large network of computers and analyzed using a computational paradigm known as the Relaxation technique. The goal is to be able to detect the onset of a worm or virus as it originates, spreads-out, attacks and disables the entire network. Currently, selective disabling of one or more features across an entire subnet, e.g. firewalls, provides limited security and keeps us from designing high performance net-centric systems. The most desirable response is to surgically disable one or more nodes, or to isolate one or more subnets.The proposed research seeks to model virus/worm propagation as a spatio-temporal process. Such models have been successfully applied in heat-flow and evidence or gestalt driven perception of images among others. In particular, we develop an iterative technique driven by the self-assessed dynamic status of each node in a network. The status of each node will be updated incrementally in concurrence with its connected neighbors to enable timely identification of compromised nodes and subnets. Several key insights used in image analysis of line-diagrams, through an iterative and relaxation-driven node labeling method, are explored to help develop this new framework