Ray Canzanese

Ph.D. Candidate, Drexel University

Ph.D. Defense scheduled for 8 May 2015 at 10:00 in Bossone 709

Title: Detection and Classification of Malicious Processes Using System Call Analysis

Abstract:

Despite efforts to mitigate the malware threat, the proliferation of malware continues, with record-setting numbers of malware samples being discovered each quarter. Malware are any intentionally malicious software, including software designed for extortion, sabotage, and espionage. Traditional malware defenses are primarily signature-based and heuristic-based, and include firewalls, intrusion detection systems, and antivirus software. Such defenses are reactive, performing well against known threats but struggling against new malware variants and zero-day threats. Together, the reactive nature of traditional defenses and the continuing spread of malware motivate the development of new techniques to detect such threats. One promising set of techniques use features extracted from system call traces to infer malicious behaviors.

This thesis studies the problem of detecting and classifying malicious processes using system call trace analysis. The goal of this study is to identify techniques that are ‘lightweight’ enough and exhibit a low enough false positive rate to be deployed in production environments. The major contributions of this work are (1) a study of the effects of feature extraction strategy on malware detection performance; (2) the comparison of signature-based and statistical detection techniques for malware detection and classification; (3) the application of sequential detection techniques for malware detection, with the goal of identifying malicious behaviors as quickly as possible; (4) a study of malware detection performance at very low false positive rates; and (5) an extensive empirical evaluation, wherein the performance of the malware detection and classification systems are evaluated against data collected from production hosts and from the execution of recently discovered malware samples. The outcome of this study is a proof-of-concept system that detects the execution of malicious processes in production environments and classifies them according to their similarity to known malware.

Member

  • Data Fusion Lab, Department of Electrical and Computer Engineering
  • Software Engineering Research Group, College of Computing and Informatics

Advisors

Research Interests

  • Cybersecurity
  • Detection theory
  • Data mining
  • Machine learning
  • Data and decision fusion