Instance-Based Learning Algorithms
Machine Learning
A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
C4.5: programs for machine learning
C4.5: programs for machine learning
Machine learning of rules and trees
Machine learning, neural and statistical classification
Machine Learning
Computer virus-antivirus coevolution
Communications of the ACM
Machine Learning
Data Mining Methods for Detection of New Malicious Executables
SP '01 Proceedings of the 2001 IEEE Symposium on Security and Privacy
ISSTA '04 Proceedings of the 2004 ACM SIGSOFT international symposium on Software testing and analysis
Learning to detect malicious executables in the wild
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
The Art of Computer Virus Research and Defense
The Art of Computer Virus Research and Defense
Data Structures and Algorithms in Java
Data Structures and Algorithms in Java
Normalizing Metamorphic Malware Using Term Rewriting
SCAM '06 Proceedings of the Sixth IEEE International Workshop on Source Code Analysis and Manipulation
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Code Normalization for Self-Mutating Malware
IEEE Security and Privacy
A scalable multi-level feature extraction technique to detect malicious executables
Information Systems Frontiers
Polymorphic worm detection using structural information of executables
RAID'05 Proceedings of the 8th international conference on Recent Advances in Intrusion Detection
An online cross view difference and behavior based kernel rootkit detector
ACM SIGSOFT Software Engineering Notes
Cloud-based malware detection for evolving data streams
ACM Transactions on Management Information Systems (TMIS)
Aspect-Oriented runtime monitor certification
TACAS'12 Proceedings of the 18th international conference on Tools and Algorithms for the Construction and Analysis of Systems
Design and Implementation of a Data Mining System for Malware Detection
Journal of Integrated Design & Process Science
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We propose a technique for defeating signature-based malware detectors by exploiting information disclosed by antivirus interfaces. This information is leveraged to reverse engineer relevant details of the detector's underlying signature database, revealing binary obfuscations that suffice to conceal malware from the detector. Experiments with real malware and antivirus interfaces on Windows operating systems justify the effectiveness of our approach.