Dynamic Binary Instrumentation-Based Framework for Malware Defense
DIMVA '08 Proceedings of the 5th international conference on Detection of Intrusions and Malware, and Vulnerability Assessment
Unknown Malcode Detection Using OPCODE Representation
EuroISI '08 Proceedings of the 1st European Conference on Intelligence and Security Informatics
A Chronological Evaluation of Unknown Malcode Detection
PAISI '09 Proceedings of the Pacific Asia Workshop on Intelligence and Security Informatics
Information Security Tech. Report
Malicious Code Detection Using Active Learning
Privacy, Security, and Trust in KDD
Proceedings of the 47th Annual Southeast Regional Conference
A survey of data mining techniques for malware detection using file features
Proceedings of the 46th Annual Southeast Regional Conference on XX
Malware detection using statistical analysis of byte-level file content
Proceedings of the ACM SIGKDD Workshop on CyberSecurity and Intelligence Informatics
Proceedings of the 48th Annual Southeast Regional Conference
Using randomized projection techniques to aid in detecting high-dimensional malicious applications
Proceedings of the 49th Annual Southeast Regional Conference
An immune concentration based virus detection approach using particle swarm optimization
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part I
Feature reduction to speed up malware classification
NordSec'11 Proceedings of the 16th Nordic conference on Information Security Technology for Applications
Mal-ID: automatic malware detection using common segment analysis and meta-features
The Journal of Machine Learning Research
Applying static analysis to high-dimensional malicious application detection
Proceedings of the 51st ACM Southeast Conference
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Anti-virus systems traditionally use signatures to detect malicious executables, but signatures are over-fitted features that are of little use in machine learning. Other more heuristic methods seek to utilize more general features, with some degree of success. In this paper, we present a data mining approach that conducts an exhaustive feature search on a set of computer viruses and strives to obviate over-fitting. We also evaluate the predictive power of a classifier by taking into account dependence relationships that exist between viruses, and we show that our classifier yields high detection rates and can be expected to perform as well in real-world conditions.