Mark Jyn-Huey Lim

Detecting Abnormal Changes in E-mail Traffic Using Hierarchical Fuzzy Systems

E-mail traffic analysis is an area of work that focuses on extracting information about the behaviour of e-mail users based on the sender, receiver, and date/time information taken from the header section of e-mail messages. Such work has applications for law enforcement where investigators and analysts require techniques to assist them with finding unusual or suspicious patterns from large amounts of communication log data. This paper describes work using hierarchical fuzzy systems to detect abnormal changes in e-mail traffic behaviour, through the fusion of e-mail traffic behaviour measurements. The paper focuses on the use of three different hierarchical fuzzy system architectures, to determine the effect that input variable groupings have on the abnormality ratings given to the communication links of suspect e-mail accounts. The case study demonstrates the use of the three hierarchical fuzzy system architectures for analysing suspect e-mail accounts belonging to the Enron e-mail corpus.

Personality Trait Based Simulation Model of the E-mail System

Within the area of criminal and terrorist social network analysis, there is little research being done on analysing the communication behavior of criminal and terrorist groups. In this paper, we describe the development of a conceptual simulation model of the e-mail system, which is based on the use of personality trait dimensions to model the e-mail traffic behavior of e-mail users. This conceptual simulation model is being used as a first step for further development in simulating criminal and terrorist communication behavior. We also describe the development of an e-mail traffic analyser system, which uses a decision tree to search for interesting e-mail traffic behavioral patterns, and uses social network and time-series visualisation to provide the details of the interesting traffic patterns. We demonstrate that the personality trait based e-mail system model is useful as a tool for generating e-mail traffic behavioral patterns and that decision trees are useful for finding interesting patterns in the e-mail traffic data.

Email communications analysis: how to use computational intelligence methods and tools?

The paper aims at investigating computational intelligence methodologies for detecting a change in communication behavioral patterns between e-mail subscribers. This change may indicate a change of social status and behavior, which could be used for early discovery of some preparation to antisocial activity, including but not limiting to terrorist attacks. The patterns in the social interactions or contacts between people by e-mail can be analyzed using social network analysis and user behavior analysis. In this paper we provide a review of the work related to the areas of dynamic modeling and link prediction of social networks, and anomaly detection for detecting changes in the behavior of e-mail usage. The feasibility of neural networks and fuzzy logic methodologies applications for a change detection system design is discussed, as well as a discussion about an e-mail simulation model currently being developed

Analysis of E-mail communication activities for detecting patterns of pathological behaviour

E-mail is one of the most popular and widely used form of electronic communication used today. The patterns in the social interactions or contacts between people by e-mail can be analysed using social network analysis and user behaviour analysis. In this paper we provide a review of the work related to the areas of dynamic modelling and link prediction of social networks, and anomaly detection for detecting changes in the behaviour of e-mail usage. We then discuss about the benefits of applying artificial intelligence techniques to these fields.