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

CBR and MBR techniques: review for an application in the emergencies domain

The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system. RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to: a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location. In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations. This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version

Knowledge based system development as an engineering process

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Knowledge Based System (KBS) development is a difficult and challenging task, in particular in knowledge intensive domains. The traditional view of knowledge engineering is one of mining experts' knowledge and somehow transforming it into a machine usable form. This process, in general, suffers from insufficient or misconstrued representation of experts' problem solving behaviour. It is also unstructured and unduly biased at an early stage by design and implementation issues - normally in the form of incremental prototyping. We believe that both knowledge acquisition and KBS development for real life applications will require a 'structured' approach. This approach should harness a KBS developer's ability in extracting knowledge and developing systems. The structure should also be sufficiently flexible to allow the knowledge engineer to use his sense of creativity in developing a KBS. This thesis puts forward such a structured approach, in which KBS development is carried out in an engineering fashion. A process in which the worker is provided with an environment for developing knowledge based systems as an engineering process, as opposed to that of an artform or crafting. The main emphasis of this work is that part of the process which deals with the analysis and design phases in developing KBS. The analysis is performed at an 'epistemological' level, not coloured by design or implementation issues. The output of this phase captures both an expert's problem solving capability, and the business constraints placed upon the intended system. This is then used by the design process in order to create an optimal, workable, and elegant design architecture for the ultimate system.Commission for the European Communities' ESPRIT programme (Project Number 1098

Multi-perspective modelling for knowledge management and knowledge engineering

ii It seems almost self-evident that “knowledge management ” and “knowledge engineering” should be related disciplines that may share techniques and methods between them. However, attempts by knowledge engineers to apply their techniques to knowledge management have been praised by some and derided by others, who claim that knowledge engineers have a fundamentally wrong concept of what “knowledge management” is. The critics also point to specific weaknesses of knowledge engineering, notably the lack of a broad context for the knowledge. Knowledge engineering has suffered some criticism from within its own ranks, too, particularly of the “rapid prototyping ” approach, in which acquired knowledge was encoded directly into an iteratively developed computer system. This approach was indeed rapid, but when used to deliver a final system, it became nearly impossible to verify and validate the system or to maintain it. A solution to this has come in the form of knowledge engineering methodology, and particularly in the CommonKAD

Cloud engineering is search based software engineering too

Many of the problems posed by the migration of computation to cloud platforms can be formulated and solved using techniques associated with Search Based Software Engineering (SBSE). Much of cloud software engineering involves problems of optimisation: performance, allocation, assignment and the dynamic balancing of resources to achieve pragmatic trade-offs between many competing technical and business objectives. SBSE is concerned with the application of computational search and optimisation to solve precisely these kinds of software engineering challenges. Interest in both cloud computing and SBSE has grown rapidly in the past five years, yet there has been little work on SBSE as a means of addressing cloud computing challenges. Like many computationally demanding activities, SBSE has the potential to benefit from the cloud; ‘SBSE in the cloud’. However, this paper focuses, instead, of the ways in which SBSE can benefit cloud computing. It thus develops the theme of ‘SBSE for the cloud’, formulating cloud computing challenges in ways that can be addressed using SBSE

Third International Symposium on Artificial Intelligence, Robotics, and Automation for Space 1994

The Third International Symposium on Artificial Intelligence, Robotics, and Automation for Space (i-SAIRAS 94), held October 18-20, 1994, in Pasadena, California, was jointly sponsored by NASA, ESA, and Japan's National Space Development Agency, and was hosted by the Jet Propulsion Laboratory (JPL) of the California Institute of Technology. i-SAIRAS 94 featured presentations covering a variety of technical and programmatic topics, ranging from underlying basic technology to specific applications of artificial intelligence and robotics to space missions. i-SAIRAS 94 featured a special workshop on planning and scheduling and provided scientists, engineers, and managers with the opportunity to exchange theoretical ideas, practical results, and program plans in such areas as space mission control, space vehicle processing, data analysis, autonomous spacecraft, space robots and rovers, satellite servicing, and intelligent instruments