Name
Abstract | ||
|---|---|---|
The sophistication of modern computer malware demands run-time malware detection strategies which are not only efficient but also robust to obfuscation and evasion attempts. In this paper, we investigate the suitability of recently proposed Dendritic Cell Algorithms (DCA), both classical DCA (cDCA) and deterministic DCA (dDCA), for malware detection at run-time. We have collected API call traces of real malware and benign processes running on Windows operating system. We evaluate the accuracy of cDCA and dDCA for classifying between malware and benign processes using API call sequences. Moreover, we also study the effects of antigen multiplier and time-windows on the detection accuracy of both algorithms. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-03246-2_22 | ICARIS '09 Proceedings of the 8th International Conference on Artificial Immune Systems |
Keywords | Field | DocType |
deterministic DCA,malware detection,API call sequence,API call trace,detection accuracy,classical DCA,Windows Processes,real malware,modern computer malware demand,malware detection strategy,benign process | Microsoft Windows,Artificial immune system,Dendritic cell algorithm,Computer science,Artificial intelligence,Obfuscation,Malware,Machine learning | Conference |
Volume | ISSN | Citations |
5666 | 0302-9743 | 9 |
PageRank | References | Authors |
0.67 | 11 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Salman Manzoor | 1 | 9 | 1.01 |
| M. Zubair Shafiq | 2 | 546 | 43.41 |
| S. Momina Tabish | 3 | 119 | 6.05 |
| Muddassar Farooq | 4 | 1221 | 83.47 |