An agent-based Bayesian method for network intrusion detection

Security is one of the major issues in any network and on the Internet. It encapsulates many different areas, such as protecting individual users against intruders, protecting corporate systems against damage, and protecting data from intrusion. It is obviously impossible to make a network totally secure, as there are so many areas that must be protected. This thesis includes an evaluation of current techniques for internal misuse of computer systems, and tries to propose a new way of dealing with this problem. This thesis proposes that it is impossible to fully protect a computer network from intrusion, and shows how different methods are applied at differing levels of the OSI model. Most systems are now protected at the network and transport layer, with systems such as firewalls and secure sockets. A weakness, though, exists in the session layer that is responsible for user logon and their associated password. It is thus important for any highly secure system to be able to continually monitor a user, even after they have successfully logged into the system. This is because once an intruder has successfully logged into a system, they can use it as a stepping-stone to gain full access (often right up to the system administrator level). This type of login identifies another weakness of current intrusion detection systems, in that they are mainly focused on detecting external intrusion, whereas a great deal of research identifies that one of the main problems is from internal intruders, and from staff within an organisation. Fraudulent activities can often he identified by changes in user behaviour. While this type of behaviour monitoring might not be suited to most networks, it could be applied to high secure installations, such as in government, and military organisations. Computer networks are now one of the most rapidly changing and vulnerable systems, where security is now a major issue. A dynamic approach, with the capacity to deal with and adapt to abrupt changes, and be simple, will provide an effective modelling toolkit. Analysts must be able to understand how it works and be able to apply it without the aid of an expert. Such models do exist in the statistical world, and it is the purpose of this thesis to introduce them and to explain their basic notions and structure. One weakness identified is the centralisation and complex implementation of intrusion detection. The thesis proposes an agent-based approach to monitor the user behaviour of each user. It also proposes that many intrusion detection systems cannot cope with new types of intrusion. It thus applies Bayesian statistics to evaluate user behaviour, and predict the future behaviour of the user. The model developed is a unique application of Bayesian statistics, and the results show that it can improve future behaviour prediction than existing ARIMA models. The thesis argues that the accuracy of long-term forecasting questionable, especially in systems that have a rapid and often unexpected evolution and behaviour. Many of the existing models for prediction use long-term forecasting, which may not be the optimal type for intrusion detection systems. The experiments conducted have varied the number of users and the time interval used for monitoring user behaviour. These results have been compared with ARIMA, and an increased accuracy has been observed. The thesis also shows that the new model can better predict changes in user behaviour, which is a key factor in identifying intrusion detection. The thesis concludes with recommendations for future work, including how the statistical model could be improved. This includes research into changing the specification of the design vector for Bayesian. Another interesting area is the integration of standard agent communication agents, which will make the security agents more social in their approach and be able to gather information from other agents