Ensemble Models for Data-driven Prediction of Malware Infections

Given a history of detected malware attacks, can we predict the number of malware infections in a country? Can we do this for different malware and countries? This is an important question which has numerous implications for cyber security, right from designing better anti-virus software, to designing and implementing targeted patches to more accurately measuring the economic impact of breaches. This problem is compounded by the fact that, as externals, we can only detect a fraction of actual malware infections. In this paper we address this problem using data from

• Type: Journal
• Year: 2016
• Where: WSDM
• Authors: Chanhyun Kang, Noseong Park, B. Aditya Prakash, Edoardo Serra, V. S. Subrahmanian
• Keywords: Data Mining, WSDM 2016