BAIT: Behavior aided Intruder Testimony Technique for Attacker Intention Prediction in Business Data Handling

Abstract

During business transactions there are lot of opportunity for data theft and data misinterpretation. Mostly, the legitimate users act like malicious users and try to misuse their privileges. So, it is very important to know their intentions and different strategies they apply for business data theft. In this paper, we develop an information analytics based technique for inferring attacker intent objectives and strategies (AIOS). The input to the model is the alert logs in real-world attack-defense scenario and output are the discovered attack strategies or patterns. The implementation of this model is done on a real-world attack-defence scenario to increase the learning efficiency of the technique. Experimental results on expected impact and attack path shows that the technique provides better results than conventional intrusion detection systems.