Attack Intention Analysis Model for Network Forensics

In network forensics, attack intentions analyses play a major role to help and accelerate decision–making for apprehending the real perpetrator. In fact, attack intention analysis is a prediction factor to help investigators to conclude a case with high accuracy. However, current techniques in attack intention analysis only focus on recognizing an alert correlation for certain evidence and predicting future attacks. In reality, more prediction factors should be used by the investigators to come to a more concise decision such as attack intention, incident path …, etc. This paper will propose an attack intention analysis model, which focus on reasoning of attacks under uncertainty intention. A new model will be introduced using a combination of a mathematical Dempster‐Shafer (D‐ S) evidence theory with a probabilistic technique through a causal network to predict an attack intention. We found that by analyzing the attacker's intention, forensic investigation agents will be able to audit and perform evidence in an efficient way. Experiments were performed on samples of probability of attack intentions to evaluate the proposed model. Arguably, attack intention analysis model may produce a clear and impact factor for investigator decision‐making.