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Security and privacy in communication networks by Sushil Jajodia, Jianying Zhou

By Sushil Jajodia, Jianying Zhou

This e-book constitutes the completely refereed complaints of the sixth foreign ICST convention, SecureComm 2010, held in Singapore in September 2010. The 28 revised complete papers have been conscientiously reviewed and chosen from 112 submissions. they're equipped in topical sections on malware and e-mail safeguard, anonymity and privateness, instant safety, structures safety, community safety, and safety protocols.

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All four were grouped in one cluster with many different classes of malware, the samples in this cluster exhibited many instances of behaviors B1 , B2 and B7 . The main reason why the four false positives were grouped in this cluster was due to having between 3 and 8 instances of behavior B2 . In each case, we attempted to access several video streams. Many of these were unreachable and analyzing the network activity showed the failed connection attempts to IP addresses of successful DNS queries.

Part of our ongoing research is to combine our findings of the network activity perspective with other perspectives to produce a more complete behavior based malware detection system. 7 Limitations Several protocols such as ARP and SMB were not studied. Their value to enhance our detection accuracy is being analyzed and added to current results. All analysis was done in a virtual machine which forcibly excluded interesting malware samples that are VM aware and ceased to execute or masqueraded as benign upon VM detection.

All analysis was done in a virtual machine which forcibly excluded interesting malware samples that are VM aware and ceased to execute or masqueraded as benign upon VM detection. The data set consisted only of malware samples which are initially executed by a mouse double click. Malware packaged as a dll file, kernel system service, or non-executable were not used. We are developing tools allowing the execution of any malware sample regardless of its format. 8 Conclusion and Future Work This research analyzes known malware and benign samples in an attempt to exploit differences in their network activity behavior to accomplish accurate behavior based malware detection.

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