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 Symantec covering more than 1.4 million hosts and 50 malware spread across 2 years and multiple countries. We first carefully design domain-based features from both malware and machine-hosts perspectives. Secondly, inspired by epidemiological and information diffusion models, we design a novel temporal non-linear model for malware spread and detection. Finally we present ESM, an ensemble-based approach which combines both these methods to construct a more accurate algorithm. Using extensive experiments spanning multiple malware and countries, we show that ESM can effectively predict malware infection ratios over time (both the actual number and trend) upto 4 times better compared to several baselines on various metrics. Furthermore, ESM's performance is stable and robust even when the number of detected infections is low.
|Title of host publication||WSDM 2016 - Proceedings of the 9th ACM International Conference on Web Search and Data Mining|
|Publisher||Association for Computing Machinery, Inc|
|Number of pages||10|
|Publication status||Published - 2016 Feb 8|
|Event||9th ACM International Conference on Web Search and Data Mining, WSDM 2016 - San Francisco, United States|
Duration: 2016 Feb 22 → 2016 Feb 25
|Name||WSDM 2016 - Proceedings of the 9th ACM International Conference on Web Search and Data Mining|
|Conference||9th ACM International Conference on Web Search and Data Mining, WSDM 2016|
|Period||16/2/22 → 16/2/25|
Bibliographical noteFunding Information:
This paper is based on work partially supported by the Maryland Procurement Office under Contract No. H98230-14-C-0137, by the NEH under Grant No. HG-229283-15, by ORNL under Task Order 4000143330, by the VT College of Engineering, and a Facebook faculty gift. We thank Symantec for providing access to the wine platform. Other researchers may reproduce and verify our results by analyzing the reference data set we recorded in wine (WINE-2013-001) after signing a research agreement with Symantec.
© 2016 ACM.
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Computer Networks and Communications