AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments

This report considers the application of Articial Intelligence (AI) techniques to the problem of misuse detection and misuse localisation within telecommunications environments. A broad survey of techniques is provided, that covers inter alia rule based systems, model-based systems, case based reasoning, pattern matching, clustering and feature extraction, articial neural networks, genetic algorithms, arti cial immune systems, agent based systems, data mining and a variety of hybrid approaches. The report then considers the central issue of event correlation, that is at the heart of many misuse detection and localisation systems. The notion of being able to infer misuse by the correlation of individual temporally distributed events within a multiple data stream environment is explored, and a range of techniques, covering model based approaches, `programmed' AI and machine learning paradigms. It is found that, in general, correlation is best achieved via rule based approaches, but that these suffer from a number of drawbacks, such as the difculty of developing and maintaining an appropriate knowledge base, and the lack of ability to generalise from known misuses to new unseen misuses. Two distinct approaches are evident. One attempts to encode knowledge of known misuses, typically within rules, and use this to screen events. This approach cannot generally detect misuses for which it has not been programmed, i.e. it is prone to issuing false negatives. The other attempts to `learn' the features of event patterns that constitute normal behaviour, and, by observing patterns that do not match expected behaviour, detect when a misuse has occurred. This approach is prone to issuing false positives, i.e. inferring misuse from innocent patterns of behaviour that the system was not trained to recognise. Contemporary approaches are seen to favour hybridisation, often combining detection or localisation mechanisms for both abnormal and normal behaviour, the former to capture known cases of misuse, the latter to capture unknown cases. In some systems, these mechanisms even work together to update each other to increase detection rates and lower false positive rates. It is concluded that hybridisation offers the most promising future direction, but that a rule or state based component is likely to remain, being the most natural approach to the correlation of complex events. The challenge, then, is to mitigate the weaknesses of canonical programmed systems such that learning, generalisation and adaptation are more readily facilitated

Investigating biocomplexity through the agent-based paradigm.

Capturing the dynamism that pervades biological systems requires a computational approach that can accommodate both the continuous features of the system environment as well as the flexible and heterogeneous nature of component interactions. This presents a serious challenge for the more traditional mathematical approaches that assume component homogeneity to relate system observables using mathematical equations. While the homogeneity condition does not lead to loss of accuracy while simulating various continua, it fails to offer detailed solutions when applied to systems with dynamically interacting heterogeneous components. As the functionality and architecture of most biological systems is a product of multi-faceted individual interactions at the sub-system level, continuum models rarely offer much beyond qualitative similarity. Agent-based modelling is a class of algorithmic computational approaches that rely on interactions between Turing-complete finite-state machines--or agents--to simulate, from the bottom-up, macroscopic properties of a system. In recognizing the heterogeneity condition, they offer suitable ontologies to the system components being modelled, thereby succeeding where their continuum counterparts tend to struggle. Furthermore, being inherently hierarchical, they are quite amenable to coupling with other computational paradigms. The integration of any agent-based framework with continuum models is arguably the most elegant and precise way of representing biological systems. Although in its nascence, agent-based modelling has been utilized to model biological complexity across a broad range of biological scales (from cells to societies). In this article, we explore the reasons that make agent-based modelling the most precise approach to model biological systems that tend to be non-linear and complex

SANA - Network Protection through artificial Immunity

Current network protection systems use a collection of intelligent components - e.g. classifiers or rule-based firewall systems to detect intrusions and anomalies and to secure a network against viruses, worms, or trojans. However, these network systems rely on individuality and support an architecture with less collaborative work of the protection components. They give less administration support for maintenance, but offer a large number of individual single points of failures - an ideal situation for network attacks to succeed. In this work, we discuss the required features, the performance, and the problems of a distributed protection system called SANA. It consists of a cooperative architecture, it is motivated by the human immune system, where the components correspond to artificial immune cells that are connected for their collaborative work. SANA promises a better protection against intruders than common known protection systems through an adaptive self-management while keeping the resources efficiently by an intelligent reduction of redundant tasks. We introduce a library of several novel and common used protection components and evaluate the performance of SANA by a proof-of-concept implementation.Comment: 5 page

DATA DRIVEN INTELLIGENT AGENT NETWORKS FOR ADAPTIVE MONITORING AND CONTROL

To analyze the characteristics and predict the dynamic behaviors of complex systems over time, comprehensive research to enable the development of systems that can intelligently adapt to the evolving conditions and infer new knowledge with algorithms that are not predesigned is crucially needed. This dissertation research studies the integration of the techniques and methodologies resulted from the fields of pattern recognition, intelligent agents, artificial immune systems, and distributed computing platforms, to create technologies that can more accurately describe and control the dynamics of real-world complex systems. The need for such technologies is emerging in manufacturing, transportation, hazard mitigation, weather and climate prediction, homeland security, and emergency response. Motivated by the ability of mobile agents to dynamically incorporate additional computational and control algorithms into executing applications, mobile agent technology is employed in this research for the adaptive sensing and monitoring in a wireless sensor network. Mobile agents are software components that can travel from one computing platform to another in a network and carry programs and data states that are needed for performing the assigned tasks. To support the generation, migration, communication, and management of mobile monitoring agents, an embeddable mobile agent system (Mobile-C) is integrated with sensor nodes. Mobile monitoring agents visit distributed sensor nodes, read real-time sensor data, and perform anomaly detection using the equipped pattern recognition algorithms. The optimal control of agents is achieved by mimicking the adaptive immune response and the application of multi-objective optimization algorithms. The mobile agent approach provides potential to reduce the communication load and energy consumption in monitoring networks. The major research work of this dissertation project includes: (1) studying effective feature extraction methods for time series measurement data; (2) investigating the impact of the feature extraction methods and dissimilarity measures on the performance of pattern recognition; (3) researching the effects of environmental factors on the performance of pattern recognition; (4) integrating an embeddable mobile agent system with wireless sensor nodes; (5) optimizing agent generation and distribution using artificial immune system concept and multi-objective algorithms; (6) applying mobile agent technology and pattern recognition algorithms for adaptive structural health monitoring and driving cycle pattern recognition; (7) developing a web-based monitoring network to enable the visualization and analysis of real-time sensor data remotely. Techniques and algorithms developed in this dissertation project will contribute to research advances in networked distributed systems operating under changing environments

N-grams Based Supervised Machine Learning Model for Mobile Agent Platform Protection against Unknown Malicious Mobile Agents

From many past years, the detection of unknown malicious mobile agents before they invade the Mobile Agent Platform has been the subject of much challenging activity. The ever-growing threat of malicious agents calls for techniques for automated malicious agent detection. In this context, the machine learning (ML) methods are acknowledged more effective than the Signature-based and Behavior-based detection methods. Therefore, in this paper, the prime contribution has been made to detect the unknown malicious mobile agents based on n-gram features and supervised ML approach, which has not been done so far in the sphere of the Mobile Agents System (MAS) security. To carry out the study, the n-grams ranging from 3 to 9 are extracted from a dataset containing 40 malicious and 40 non-malicious mobile agents. Subsequently, the classification is performed using different classifiers. A nested 5-fold cross validation scheme is employed in order to avoid the biasing in the selection of optimal parameters of classifier. The observations of extensive experiments demonstrate that the work done in this paper is suitable for the task of unknown malicious mobile agent detection in a Mobile Agent Environment, and also adds the ML in the interest list of researchers dealing with MAS security

Proceedings of the 2nd International Workshop on Security in Mobile Multiagent Systems

This report contains the Proceedings of the Second Workshop on Security on Security of Mobile Multiagent Systems (SEMAS2002). The Workshop was held in Montreal, Canada as a satellite event to the 5th International Conference on Autonomous Agents in 2001. The far reaching influence of the Internet has resulted in an increased interest in agent technologies, which are poised to play a key role in the implementation of successful Internet and WWW-based applications in the future. While there is still considerable hype concerning agent technologies, there is also an increasing awareness of the problems involved. In particular, that these applications will not be successful unless security issues can be adequately handled. Although there is a large body of work on cryptographic techniques that provide basic building-blocks to solve specific security problems, relatively little work has been done in investigating security in the multiagent system context. Related problems are secure communication between agents, implementation of trust models/authentication procedures or even reflections of agents on security mechanisms. The introduction of mobile software agents significantly increases the risks involved in Internet and WWW-based applications. For example, if we allow agents to enter our hosts or private networks, we must offer the agents a platform so that they can execute correctly but at the same time ensure that they will not have deleterious effects on our hosts or any other agents / processes in our network. If we send out mobile agents, we should also be able to provide guarantees about specific aspects of their behaviour, i.e., we are not only interested in whether the agents carry out-out their intended task correctly. They must defend themselves against attacks initiated by other agents, and survive in potentially malicious environments. Agent technologies can also be used to support network security. For example in the context of intrusion detection, intelligent guardian agents may be used to analyse the behaviour of agents on a firewall or intelligent monitoring agents can be used to analyse the behaviour of agents migrating through a network. Part of the inspiration for such multi-agent systems comes from primitive animal behaviour, such as that of guardian ants protecting their hill or from biological immune systems