Name
Title | ||
|---|---|---|
Adaptive Rule-Based Malware Detection Employing Learning Classifier Systems: A Proof of Concept |
Abstract | ||
|---|---|---|
Efficient and accurate malware detection is increasingly becoming a necessity for society to operate. Existing malware detection systems have excellent performance in identifying known malware for which signatures are available, but poor performance in anomaly detection for zero day exploits for which signatures have not yet been made available or targeted attacks against a specific entity. The primary goal of this paper is to provide evidence for the potential of learning classifier systems to improve the accuracy of malware detection. A proof of concept is presented for adaptive rule-based malware detection employing learning classifier systems, which combines a rule-based expert system with evolutionary algorithm based reinforcement learning, thus creating a self-training adaptive malware detection system which dynamically evolves detection rules. Experimental results are presented which demonstrate the system's ability to learn effective rules from repeated presentations of a tagged training set and show the degree of generalization achieved on an independent test set. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/COMPSACW.2011.28 | Computer Software and Applications Conference Workshops |
Keywords | Field | DocType |
adaptive rule-based malware detection,anomaly detection,rule-based expert system,malware detection,malware detection system,reinforcement learning,accurate malware detection,classifier system,evolves detection rule,employing learning classifier systems,self-training adaptive malware detection,rule based,expert system,learning artificial intelligence,learning classifier system,malware,feature extraction,expert systems,evolutionary computation,testing,test set,training set,proof of concept,accuracy,evolutionary algorithm,measurement | Data mining,Anomaly detection,Rule-based system,Computer science,Expert system,Proof of concept,Artificial intelligence,Classifier (linguistics),Malware,Machine learning,Reinforcement learning,Test set | Conference |
ISBN | Citations | PageRank |
978-0-7695-4459-5 | 8 | 0.51 |
References | Authors | |
4 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jonathan J. Blount | 1 | 8 | 0.51 |
| Daniel R. Tauritz | 2 | 148 | 23.27 |
| Samuel A. Mulder | 3 | 33 | 2.12 |