Cyber Profiles Based Risk Prediction of Application Systems for Effective Access Control Application systems maintain critical sensitive information of an enterprise and especially huge number of data with specific ownership. Unauthorized modification or deletion of data caused by cyber attacks may bring tremendous loses for enterprises. To reduce the damage of cyber attacks, existing techniques have been proposed to predict the potential risk of external attacks at the level of an enterprise, a user, or a machine. However, risk prediction has not been conducted at the level of application systems, which may suffer from external attacks or insider threats. This paper proposes a model based on machine learning to predict whether the application systems of an enterprise have the risk of unauthorized access by using a cyber profile. In particular, the cyber profile is composed of features extracted from the information of the three domains in cyberspace: Information Infrastructure domain, Data domain, and Application domain. The core idea of the model selects the most significant features that have a large impact on the occurrence of unauthorized access to application systems. At last, by using a limited number of selected features, high forecast accuracy is achieved. These results verify the effectiveness of the prediction model, which can potentially be exploited to guide the adjustment of access control policies for effective access control.

Distributed Network Behaviour Prediction Using Machine Learning and AgentBased Micro Simulation Distributed network behaviour is increasingly attracting huge attention both in academics and industrial initiatives, and most recently machine learning has been used in every possible field, leveraging its advantages.Typically, a distributed networking allows for the execution of distributed applications which results in complex behaviours among connected systems. This complexity in itself can create grey areas and vulnerabilities in the securities of these networks, therefore predicting the behaviour of these systems both at the macro and micro level has become essential. In the past most researchers have predicted network behaviour and network attack patterns by using aggregated data, but in this paper we focus on the application of machine learning at the individual user level such that the prediction of the individual network user behaviour pattern at the micro level becomes a substasive tool in creating a realistic agent based simulation of the whole distributed network, which in turn can serve as a test bed for predicting whatif scenarios such as network attacks on the target system or exposing vulnerabilities within the target system. The simulation result was validated by comparing the simulated interaction within the simulated network to the data logged in the server logs within the real life network system. This produced a correlation above 0.8, indicating a realistic model.