'Cooperative Automated worm Response and Detection ImmuNe ALgorithm (CARDINAL) inspired by T-cell Immunity and Tolerance'

The role of T-cells within the immune system is to confirm and assess anomalous situations and then either respond to or tolerate the source of the effect. To illustrate how these mechanisms can be harnessed to solve real-world problems, we present the blueprint of a T-cell inspired algorithm for computer security worm detection. We show how the three central T-cell processes, namely T-cell maturation, differentiation and proliferation, naturally map into this domain and further illustrate how such an algorithm fits into a complete immune inspired computer security system and framework.

'Malicious Code Execution Detection and Response Immune System inspired by the Danger Theory'

The analysis of system calls is one method employed by anomaly detection systems to recognise malicious code execution. Similarities can be drawn between this process and the behaviour of certain cells belonging to the human immune system, and can be applied to construct an artificial immune system. A recently developed hypothesis in immunology, the Danger Theory, states that our immune system responds to the presence of intruders through sensing molecules belonging to those invaders, plus signals generated by the host indicating danger and damage. We propose the incorporation of this concept into a responsive intrusion detection system, where behavioural information of the system and running processes is combined with information regarding individual system calls.

'Danger Theory: The Link between AIS and IDS?'

Abstract We present ideas about creating a next generation Intrusion Detection System (IDS) based on the latest immunological theories. The central challenge with computer security is determining the difference between normal and potentially harmful activity. For half a century, developers have protected their systems by coding rules that identify and block specific events. However, the nature of current and future threats in conjunction with ever larger IT systems urgently requires the development of automated and adaptive defensive tools. A promising solution is emerging in the form of Artificial Immune Systems (AIS): The Human Immune System (HIS) can detect and defend against harmful and previously unseen invaders, so can we not build a similar Intrusion Detection System (IDS) for our computers? Presumably, those systems would then have the same beneficial properties as HIS like error tolerance, adaptation and self-monitoring. Current AIS have been successful on test systems, but the algorithms rely on self-nonself discrimination, as stipulated in classical immunology. However, immunologist are increasingly finding fault with traditional self-nonself thinking and a new 'Danger Theory' (DT) is emerging. This new theory suggests that the immune system reacts to threats based on the correlation of various (danger) signals and it provides a method of 'grounding' the immune response, i.e. linking it directly to the attacker. Little is currently understood of the precise nature and correlation of these signals and the theory is a topic of hot debate. It is the aim of this research to investigate this correlation and to translate the DT into the realms of computer security, thereby creating AIS that are no longer limited by self-nonself discrimination. It should be noted that we do not intend to defend this controversial theory per se, although as a deliverable this project will add to the body of knowledge in this area. Rather we are interested in its merits for scaling up AIS applications by overcoming self-nonself discrimination problems.

Danger theory: the link between AIS and IDS?

We present ideas about creating a next generation Intrusion Detection System (IDS) based on the latest immunological theories. The central challenge with computer security is determining the difference between normal and potentially harmful activity. For half a century, developers have protected their systems by coding rules that identify and block specific events. However, the nature of current and future threats in conjunction with ever larger IT systems urgently requires the development of automated and adaptive defensive tools. A promising solution is emerging in the form of Artificial Immune Systems (AIS): The Human Immune System (HIS) can detect and defend against harmful and previously unseen invaders, so can we not build a similar Intrusion Detection System (IDS) for our computers? Presumably, those systems would then have the same beneficial properties as HIS like error tolerance, adaptation and self-monitoring. Current AIS have been successful on test systems, but the algorithms rely on self-nonself discrimination, as stipulated in classical immunology. However, immunologist are increasingly finding fault with traditional self-nonself thinking and a new ‘Danger Theory’ (DT) is emerging. This new theory suggests that the immune system reacts to threats based on the correlation of various (danger) signals and it provides a method of ‘grounding’ the immune response, i.e. linking it directly to the attacker. Little is currently understood of the precise nature and correlation of these signals and the theory is a topic of hot debate. It is the aim of this research to investigate this correlation and to translate the DT into the realms of computer security, thereby creating AIS that are no longer limited by self-nonself discrimination. It should be noted that we do not intend to defend this controversial theory per se, although as a deliverable this project will add to the body of knowledge in this area. Rather we are interested in its merits for scaling up AIS applications by overcoming self-nonself discrimination problems.

Real-time alert correlation with type graphs

The premise of automated alert correlation is to accept that false alerts from a low level intrusion detection system are inevitable and use attack models to explain the output in an understandable way. Several algorithms exist for this purpose which use attack graphs to model the ways in which attacks can be combined. These algorithms can be classified in to two broad categories namely scenario-graph approaches, which create an attack model starting from a vulnerability assessment and type-graph approaches which rely on an abstract model of the relations between attack types. Some research in to improving the efficiency of type-graph correlation has been carried out but this research has ignored the hypothesizing of missing alerts. Our work is to present a novel type-graph algorithm which unifies correlation and hypothesizing in to a single operation. Our experimental results indicate that the approach is extremely efficient in the face of intensive alerts and produces compact output graphs comparable to other techniques.

Using multi-core processors to support network security applications

Multi-core processors represent a major evolution in computing hardware technology. Multi-core provides a network security application with more processing power from the hardware perspective. However, there are still significant software design challenges that must be overcome. In this paper, we present new architecture for multi-core supported network security applications, which aims at providing network security processing without causing performance penalty to normal network operations. We also provide an instance of this architecture - a multi-core supported intrusion detection system based on neural network. While hardware-based parallelisms have shown their advantage on throughput performance, parallelisms based multi-core provides more flexible, high performance, comprehensive, intelligent, and scalable solutions to network security applications. © 2008 IEEE.

Detection and classification of anomaly intrusion using hierarchy clustering and SVM

Anomaly detection as a kind of intrusion detection is good at detecting the unknown attacks or new attacks, and it has attracted much attention during recent years. In this paper, a new hierarchy anomaly intrusion detection model that combines the fuzzy c-means (FCM) based on genetic algorithm and SVM is proposed. During the process of detecting intrusion, the membership function and the fuzzy interval are applied to it, and the process is extended to soft classification from the previous hard classification. Then a fuzzy error correction sub interval is introduced, so when the detection result of a data instance belongs to this range, the data will be re-detected in order to improve the effectiveness of intrusion detection. Experimental results show that the proposed model can effectively detect the vast majority of network attack types, which provides a feasible solution for solving the problems of false alarm rate and detection rate in anomaly intrusion detection model.

Detecting and characterising malicious executable payloads

Buffer overflow vulnerabilities continue to prevail and the sophistication of attacks targeting these vulnerabilities is continuously increasing. As a successful attack of this type has the potential to completely compromise the integrity of the targeted host, early detection is vital. This thesis examines generic approaches for detecting executable payload attacks, without prior knowledge of the implementation of the attack, in such a way that new and previously unseen attacks are detectable. Executable payloads are analysed in detail for attacks targeting the Linux and Windows operating systems executing on an Intel IA-32 architecture. The execution flow of attack payloads are analysed and a generic model of execution is examined. A novel classification scheme for executable attack payloads is presented which allows for characterisation of executable payloads and facilitates vulnerability and threat assessments, and intrusion detection capability assessments for intrusion detection systems. An intrusion detection capability assessment may be utilised to determine whether or not a deployed system is able to detect a specific attack and to identify requirements for intrusion detection functionality for the development of new detection methods. Two novel detection methods are presented capable of detecting new and previously unseen executable attack payloads. The detection methods are capable of identifying and enumerating the executable payload’s interactions with the operating system on the targeted host at the time of compromise. The detection methods are further validated using real world data including executable payload attacks.

Using honeypots to analyse anomalous Internet activities

Monitoring Internet traffic is critical in order to acquire a good understanding of threats to computer and network security and in designing efficient computer security systems. Researchers and network administrators have applied several approaches to monitoring traffic for malicious content. These techniques include monitoring network components, aggregating IDS alerts, and monitoring unused IP address spaces. Another method for monitoring and analyzing malicious traffic, which has been widely tried and accepted, is the use of honeypots. Honeypots are very valuable security resources for gathering artefacts associated with a variety of Internet attack activities. As honeypots run no production services, any contact with them is considered potentially malicious or suspicious by definition. This unique characteristic of the honeypot reduces the amount of collected traffic and makes it a more valuable source of information than other existing techniques. Currently, there is insufficient research in the honeypot data analysis field. To date, most of the work on honeypots has been devoted to the design of new honeypots or optimizing the current ones. Approaches for analyzing data collected from honeypots, especially low-interaction honeypots, are presently immature, while analysis techniques are manual and focus mainly on identifying existing attacks. This research addresses the need for developing more advanced techniques for analyzing Internet traffic data collected from low-interaction honeypots. We believe that characterizing honeypot traffic will improve the security of networks and, if the honeypot data is handled in time, give early signs of new vulnerabilities or breakouts of new automated malicious codes, such as worms. The outcomes of this research include: • Identification of repeated use of attack tools and attack processes through grouping activities that exhibit similar packet inter-arrival time distributions using the cliquing algorithm; • Application of principal component analysis to detect the structure of attackers’ activities present in low-interaction honeypots and to visualize attackers’ behaviors; • Detection of new attacks in low-interaction honeypot traffic through the use of the principal component’s residual space and the square prediction error statistic; • Real-time detection of new attacks using recursive principal component analysis; • A proof of concept implementation for honeypot traffic analysis and real time monitoring.

Legislative and security requirements of audit material for evidentiary purpose

This research used the Queensland Police Service, Australia, as a major case study. Information on principles, techniques and processes used, and the reason for the recording, storing and release of audit information for evidentiary purposes is reported. It is shown that Law Enforcement Agencies have a two-fold interest in, and legal obligation pertaining to, audit trails. The first interest relates to the situation where audit trails are actually used by criminals in the commission of crime and the second to where audit trails are generated by the information systems used by the police themselves in support of the recording and investigation of crime. Eleven court cases involving Queensland Police Service audit trails used in evidence in Queensland courts were selected for further analysis. It is shown that, of the cases studied, none of the evidence presented was rejected or seriously challenged from a technical perspective. These results were further analysed and related to normal requirements for trusted maintenance of audit trail information in sensitive environments with discussion on the ability and/or willingness of courts to fully challenge, assess or value audit evidence presented. Managerial and technical frameworks for firstly what is considered as an environment where a computer system may be considered to be operating “properly” and, secondly, what aspects of education, training, qualifications, expertise and the like may be considered as appropriate for persons responsible within that environment, are both proposed. Analysis was undertaken to determine if audit and control of information in a high security environment, such as law enforcement, could be judged as having improved, or not, in the transition from manual to electronic processes. Information collection, control of processing and audit in manual processes used by the Queensland Police Service, Australia, in the period 1940 to 1980 was assessed against current electronic systems essentially introduced to policing in the decades of the 1980s and 1990s. Results show that electronic systems do provide for faster communications with centrally controlled and updated information readily available for use by large numbers of users who are connected across significant geographical locations. However, it is clearly evident that the price paid for this is a lack of ability and/or reluctance to provide improved audit and control processes. To compare the information systems audit and control arrangements of the Queensland Police Service with other government departments or agencies, an Australia wide survey was conducted. Results of the survey were contrasted with the particular results of a survey, conducted by the Australian Commonwealth Privacy Commission four years previous, to this survey which showed that security in relation to the recording of activity against access to information held on Australian government computer systems has been poor and a cause for concern. However, within this four year period there is evidence to suggest that government organisations are increasingly more inclined to generate audit trails. An attack on the overall security of audit trails in computer operating systems was initiated to further investigate findings reported in relation to the government systems survey. The survey showed that information systems audit trails in Microsoft Corporation's “Windows” operating system environments are relied on quite heavily. An audit of the security for audit trails generated, stored and managed in the Microsoft “Windows 2000” operating system environment was undertaken and compared and contrasted with similar such audit trail schemes in the “UNIX” and “Linux” operating systems. Strength of passwords and exploitation of any security problems in access control were targeted using software tools that are freely available in the public domain. Results showed that such security for the “Windows 2000” system is seriously flawed and the integrity of audit trails stored within these environments cannot be relied upon. An attempt to produce a framework and set of guidelines for use by expert witnesses in the information technology (IT) profession is proposed. This is achieved by examining the current rules and guidelines related to the provision of expert evidence in a court environment, by analysing the rationale for the separation of distinct disciplines and corresponding bodies of knowledge used by the Medical Profession and Forensic Science and then by analysing the bodies of knowledge within the discipline of IT itself. It is demonstrated that the accepted processes and procedures relevant to expert witnessing in a court environment are transferable to the IT sector. However, unlike some discipline areas, this analysis has clearly identified two distinct aspects of the matter which appear particularly relevant to IT. These two areas are; expertise gained through the application of IT to information needs in a particular public or private enterprise; and expertise gained through accepted and verifiable education, training and experience in fundamental IT products and system.