Title: BAIT: behaviour aided intruder testimony technique for attacker intention prediction in business data handling
Authors: K. Narasimha Mallikarjunan; S. Mercy Shalinie; A. Bhuvaneshwaran
Addresses: CSE Department, Thiagarajar College of Engineering, Madurai, India ' CSE Department, Thiagarajar College of Engineering, Madurai, India ' CSE Department, Thiagarajar College of Engineering, Madurai, India
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.
Keywords: attacker behaviour analysis; information analytics; AIOS; attacker category; pattern mining.
International Journal of Business Intelligence and Data Mining, 2019 Vol.14 No.1/2, pp.177 - 198
Received: 30 May 2017
Accepted: 14 Dec 2017
Published online: 16 Nov 2018 *