Abstract Drive-by downloads attack has become the primary attack vehicle for malware distribution in recent years. One existing method of detecting drive-by download attacks is using static analysis technique. However, static detection methods are vulnerable to sophisticated obfuscation and cloaking. Dynamic detection methods are proposed to overcome the shortcomings of static analysis techniques and can get a higher detection rate. But dynamic anomaly detection methods are typically resource intensive and introduce high time overhead. To improve performance of dynamic detection techniques, we designed SafeBrowingCloud, a system based on apache S4, a distributed computing platform. And the system is deployed at edge router. SafeBrowingCloud analyzes network traffic, executes webpages in firefox with modified javascript engine, abstracts javascript strings and detects shellcode with three shellcode detection methods to find malicious web pages. Experimental results show efficiency of the proposed system with the high-speed network traffic.
SafeBrowsingCloud: Detecting Drive-by-Downloads Attack Using Cloud Computing Environment
Internet of Vehicles – Technologies and Services ; 4 ; 292-303
Lecture Notes in Computer Science ; 8662 , 4
2014-01-01
12 pages
Article/Chapter (Book)
Electronic Resource
English
Drive-by download , Apache S4 , Shellcode , Cloud Computer Science , Computer Communication Networks , Information Systems Applications (incl. Internet) , Information Storage and Retrieval , Database Management , Algorithm Analysis and Problem Complexity , Management of Computing and Information Systems
SafeBrowsingCloud: Detecting Drive-by-Downloads Attack Using Cloud Computing Environment
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