Journal of Chemical and Pharmaceutical Research (ISSN : 0975-7384)

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Original Articles: 2016 Vol: 8 Issue: 6

Detection on Hidden Markov Model and Intention Prediction Techniques

Abstract

This paper discusses two approaches In the first three features namely ongoing attacks, autonomic prevention actions, and risk measure are Integrated to our Autonomic Cloud Intrusion Detection Framework (ACIDF) as most of the current security technologies do not provide the essential security features for cloud systems such as early warnings about future ongoing attacks, autonomic prevention actions, and risk measure. The early warnings are signaled through a new finite State Hidden Markov prediction model that captures the interaction between the attackers and cloud assets. The risk assessment model measures the potential impact of a threat on assets given its occurrence probability. The estimated risk of each security alert is updated dynamically as the alert is correlated to prior ones. This enables the adaptive risk metric to evaluate the cloud’s overall security state. The prediction system raises early warnings about potential attacks to the autonomic component, controller. Thus, the controller can take proactive corrective actions before the attacks pose a serious security risk to the system.