A Novel Signature Generation Approach for Polymorphic Worms
Author:
Publisher
Springer International Publishing
Link
http://link.springer.com/content/pdf/10.1007/978-3-319-27137-8_28
Reference18 articles.
1. Kaur, R., Singh, M.: A survey on zero-day polymorphic worm detection techniques. IEEE Commun. Surv. Tutorials 16(3), 1520–1549 (2014)
2. Bayoglu, B., Sogukpinar, L.: Graph based signature classes for detecting polymorphic worms via content analysis. Comput. Netw. 56(2), 832–844 (2012)
3. Mohammed, M.M.Z.E., Chan, H.A., Ventura, N., Pathan, A.S.K.: An automated signature generation method for zero-day polymorphic worms based on multilayer perceptron model. In: Proceedings of 2013 International Conference on Advanced Computer Science Applications and Technologies (ACSAT), Kuching, pp. 450–455, December 2013
4. Comar, P.M., Liu, L., Saha, S., Tan, P.N., Nucci, A.: Combining supervised and unsupervised learning for zero-day malware detection. In: Proceedings of 32nd Annual IEEE International Conference on Computer Communications (INFOCOM 2013), Turin, Italy, pp. 2022–2030, April 2013
5. Kaur, R., Singh, M.: Efficient hybrid technique for detecting zero-day polymorphic worms. In: Proceedings of 2014 IEEE International on Advance Computing Conference (IACC), pp. 95–100, February 2014
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