Anomaly-based Correlation of IDS Alarms

An Intrusion Detection System (IDS) is one of the major techniques for securing information systems and keeping pace with current and potential threats and vulnerabilities in computing systems. It is an indisputable fact that the art of detecting intrusions is still far from perfect, and IDSs tend to generate a large number of false IDS alarms. Hence human has to inevitably validate those alarms before any action can be taken. As IT infrastructure become larger and more complicated, the number of alarms that need to be reviewed can escalate rapidly, making this task very difficult to manage. The need for an automated correlation and reduction system is therefore very much evident. In addition, alarm correlation is valuable in providing the operators with a more condensed view of potential security issues within the network infrastructure. The thesis embraces a comprehensive evaluation of the problem of false alarms and a proposal for an automated alarm correlation system. A critical analysis of existing alarm correlation systems is presented along with a description of the need for an enhanced correlation system. The study concludes that whilst a large number of works had been carried out in improving correlation techniques, none of them were perfect. They either required an extensive level of domain knowledge from the human experts to effectively run the system or were unable to provide high level information of the false alerts for future tuning. The overall objective of the research has therefore been to establish an alarm correlation framework and system which enables the administrator to effectively group alerts from the same attack instance and subsequently reduce the volume of false alarms without the need of domain knowledge. The achievement of this aim has comprised the proposal of an attribute-based approach, which is used as a foundation to systematically develop an unsupervised-based two-stage correlation technique. From this formation, a novel SOM K-Means Alarm Reduction Tool (SMART) architecture has been modelled as the framework from which time and attribute-based aggregation technique is offered. The thesis describes the design and features of the proposed architecture, focusing upon the key components forming the underlying architecture, the alert attributes and the way they are processed and applied to correlate alerts. The architecture is strengthened by the development of a statistical tool, which offers a mean to perform results or alert analysis and comparison. The main concepts of the novel architecture are validated through the implementation of a prototype system. A series of experiments were conducted to assess the effectiveness of SMART in reducing false alarms. This aimed to prove the viability of implementing the system in a practical environment and that the study has provided appropriate contribution to knowledge in this field

Feature selection using information gain for improved structural-based alert correlation

Grouping and clustering alerts for intrusion detection based on the similarity of features is referred to as structurally base alert correlation and can discover a list of attack steps. Previous researchers selected different features and data sources manually based on their knowledge and experience, which lead to the less accurate identification of attack steps and inconsistent performance of clustering accuracy. Furthermore, the existing alert correlation systems deal with a huge amount of data that contains null values, incomplete information, and irrelevant features causing the analysis of the alerts to be tedious, time-consuming and error-prone. Therefore, this paper focuses on selecting accurate and significant features of alerts that are appropriate to represent the attack steps, thus, enhancing the structural-based alert correlation model. A two-tier feature selection method is proposed to obtain the significant features. The first tier aims at ranking the subset of features based on high information gain entropy in decreasing order. The second tier extends additional features with a better discriminative ability than the initially ranked features. Performance analysis results show the significance of the selected features in terms of the clustering accuracy using 2000 DARPA intrusion detection scenario-specific dataset

Advanced attack tree based intrusion detection

Computer network systems are constantly under attack or have to deal with attack attempts. The first step in any network’s ability to fight against intrusive attacks is to be able to detect intrusions when they are occurring. Intrusion Detection Systems (IDS) are therefore vital in any kind of network, just as antivirus is a vital part of a computer system. With the increasing computer network intrusion sophistication and complexity, most of the victim systems are compromised by sophisticated multi-step attacks. In order to provide advanced intrusion detection capability against the multi-step attacks, it makes sense to adopt a rigorous and generalising view to tackling intrusion attacks. One direction towards achieving this goal is via modelling and consequently, modelling based detection. An IDS is required that has good quality of detection capability, not only to be able to detect higher-level attacks and describe the state of ongoing multi-step attacks, but also to be able to determine the achievement of high-level attack detection even if any of the modelled low-level attacks are missed by the detector, because no alert being generated may represent that the corresponding low-level attack is either not being conducted by the adversary or being conducted by the adversary but evades the detection. This thesis presents an attack tree based intrusion detection to detect multistep attacks. An advanced attack tree modelling technique, Attack Detection Tree, is proposed to model the multi-step attacks and facilitate intrusion detection. In addition, the notion of Quality of Detectability is proposed to describe the ongoing states of both intrusion and intrusion detection. Moreover, a detection uncertainty assessment mechanism is proposed to apply the measured evidence to deal with the uncertainty issues during the assessment process to determine the achievement of high-level attacks even if any modelled low-level incidents may be missing

New Methods for Network Traffic Anomaly Detection

In this thesis we examine the efficacy of applying outlier detection techniques to understand the behaviour of anomalies in communication network traffic. We have identified several shortcomings. Our most finding is that known techniques either focus on characterizing the spatial or temporal behaviour of traffic but rarely both. For example DoS attacks are anomalies which violate temporal patterns while port scans violate the spatial equilibrium of network traffic. To address this observed weakness we have designed a new method for outlier detection based spectral decomposition of the Hankel matrix. The Hankel matrix is spatio-temporal correlation matrix and has been used in many other domains including climate data analysis and econometrics. Using our approach we can seamlessly integrate the discovery of both spatial and temporal anomalies. Comparison with other state of the art methods in the networks community confirms that our approach can discover both DoS and port scan attacks. The spectral decomposition of the Hankel matrix is closely tied to the problem of inference in Linear Dynamical Systems (LDS). We introduce a new problem, the Online Selective Anomaly Detection (OSAD) problem, to model the situation where the objective is to report new anomalies in the system and suppress know faults. For example, in the network setting an operator may be interested in triggering an alarm for malicious attacks but not on faults caused by equipment failure. In order to solve OSAD we combine techniques from machine learning and control theory in a unique fashion. Machine Learning ideas are used to learn the parameters of an underlying data generating system. Control theory techniques are used to model the feedback and modify the residual generated by the data generating state model. Experiments on synthetic and real data sets confirm that the OSAD problem captures a general scenario and tightly integrates machine learning and control theory to solve a practical problem

A Method for Malicious Network Packet Detection based on Anomalous TTL Values

In the current digital age, a pervasive shift towards digitalization is evident in all aspects of life, encompassing entertainment, education, business, and more. Consequently, the demand for internet access has surged, paralleled therefore unfortunate escalation in cybercrimes. This study undertakes an exploration into the intrinsic nature of network packets, aiming to discern their potential for malice or legitimacy. In the internet, 32 intermediate nodes are encountered by a Network packet before it reaches its final host. Our findings suggest that the time-to-live (TTL) parameter in certain IP packets diverges from the initial TTL by more than 32 intermediary hops. It's likely that these packets are generated by specialized software. We anticipate that malicious IP packets exhibit unconventional TTL values, influenced by factors such as the source machine's operating system and protocols like TCP/ICMP/UDP, etc. To gauge the effectiveness and value of the proposed method, an experiment was conducted utilizing the SNORT NIDS system. Filtering rules based on signatures were formulated to thoroughly analyze the traffic. Real network data, along with DARPA and MACCDC 2012 datasets, were employed as inputs for the SNORT NIDS, and it has been observed that the suggested approach successfully detects the anomalous network packets

Incident Prioritisation for Intrusion Response Systems

The landscape of security threats continues to evolve, with attacks becoming more serious and the number of vulnerabilities rising. To manage these threats, many security studies have been undertaken in recent years, mainly focusing on improving detection, prevention and response efficiency. Although there are security tools such as antivirus software and firewalls available to counter them, Intrusion Detection Systems and similar tools such as Intrusion Prevention Systems are still one of the most popular approaches. There are hundreds of published works related to intrusion detection that aim to increase the efficiency and reliability of detection, prevention and response systems. Whilst intrusion detection system technologies have advanced, there are still areas available to explore, particularly with respect to the process of selecting appropriate responses. Supporting a variety of response options, such as proactive, reactive and passive responses, enables security analysts to select the most appropriate response in different contexts. In view of that, a methodical approach that identifies important incidents as opposed to trivial ones is first needed. However, with thousands of incidents identified every day, relying upon manual processes to identify their importance and urgency is complicated, difficult, error-prone and time-consuming, and so prioritising them automatically would help security analysts to focus only on the most critical ones. The existing approaches to incident prioritisation provide various ways to prioritise incidents, but less attention has been given to adopting them into an automated response system. Although some studies have realised the advantages of prioritisation, they released no further studies showing they had continued to investigate the effectiveness of the process. This study concerns enhancing the incident prioritisation scheme to identify critical incidents based upon their criticality and urgency, in order to facilitate an autonomous mode for the response selection process in Intrusion Response Systems. To achieve this aim, this study proposed a novel framework which combines models and strategies identified from the comprehensive literature review. A model to estimate the level of risks of incidents is established, named the Risk Index Model (RIM). With different levels of risk, the Response Strategy Model (RSM) dynamically maps incidents into different types of response, with serious incidents being mapped to active responses in order to minimise their impact, while incidents with less impact have passive responses. The combination of these models provides a seamless way to map incidents automatically; however, it needs to be evaluated in terms of its effectiveness and performances. To demonstrate the results, an evaluation study with four stages was undertaken; these stages were a feasibility study of the RIM, comparison studies with industrial standards such as Common Vulnerabilities Scoring System (CVSS) and Snort, an examination of the effect of different strategies in the rating and ranking process, and a test of the effectiveness and performance of the Response Strategy Model (RSM). With promising results being gathered, a proof-of-concept study was conducted to demonstrate the framework using a live traffic network simulation with online assessment mode via the Security Incident Prioritisation Module (SIPM); this study was used to investigate its effectiveness and practicality. Through the results gathered, this study has demonstrated that the prioritisation process can feasibly be used to facilitate the response selection process in Intrusion Response Systems. The main contribution of this study is to have proposed, designed, evaluated and simulated a framework to support the incident prioritisation process for Intrusion Response Systems.Ministry of Higher Education in Malaysia and University of Malay

Behavior-Based Modeling and Its Application to Email Analysis

The Email Mining Toolkit (EMT) is a data mining system that computes behavior profiles or models of user email accounts. These models may be used for a multitude of tasks including forensic analyses and detection tasks of value to law enforcement and intelligence agencies, as well for as other typical tasks such as virus and spam detection. To demonstrate the power of the methods, we focus on the application of these models to detect the early onset of a viral propagation without "content-base" (or signature-based) analysis in common use in virus scanners. We present several experiments using real email from 15 users with injected simulated viral emails and describe how the combination of different behavior models improves overall detection rates. The performance results vary depending upon parameter settings, approaching 99% true positive (TP) (percentage of viral emails caught) in general cases and with 0.38% false positive (FP) (percentage of emails with attachments that are mislabeled as viral). The models used for this study are based upon volume and velocity statistics of a user's email rate and an analysis of the user's (social) cliques revealed in the person's email behavior. We show by way of simulation that virus propagations are detectable since viruses may emit emails at rates different than human behavior suggests is normal, and email is directed to groups of recipients in ways that violate the users' typical communications with their social groups