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ICSE
2004
IEEE-ACM
14 years 5 months ago
Finding Latent Code Errors via Machine Learning over Program Executions
This paper proposes a technique for identifying program properties that indicate errors. The technique generates machine learning models of program properties known to result from...
Yuriy Brun, Michael D. Ernst
GECCO
2009
Springer
162views Optimization» more  GECCO 2009»
13 years 9 months ago
On the appropriateness of evolutionary rule learning algorithms for malware detection
In this paper, we evaluate the performance of ten well-known evolutionary and non-evolutionary rule learning algorithms. The comparative study is performed on a real-world classiï...
M. Zubair Shafiq, S. Momina Tabish, Muddassar Faro...
KDD
2007
ACM
566views Data Mining» more  KDD 2007»
14 years 5 months ago
IMDS: intelligent malware detection system
The proliferation of malware has presented a serious threat to the security of computer systems. Traditional signature-based antivirus systems fail to detect polymorphic and new, ...
Yanfang Ye, Dingding Wang, Tao Li, Dongyi Ye
GECCO
2009
Springer
138views Optimization» more  GECCO 2009»
13 years 11 months ago
IMAD: in-execution malware analysis and detection
The sophistication of computer malware is becoming a serious threat to the information technology infrastructure, which is the backbone of modern e-commerce systems. We, therefore...
Syed Bilal Mehdi, Ajay Kumar Tanwani, Muddassar Fa...
GECCO
2009
Springer
161views Optimization» more  GECCO 2009»
13 years 11 months ago
Are evolutionary rule learning algorithms appropriate for malware detection?
In this paper, we evaluate the performance of ten well-known evolutionary and non-evolutionary rule learning algorithms. The comparative study is performed on a real-world classiï...
M. Zubair Shafiq, S. Momina Tabish, Muddassar Faro...