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Journal of Electrical and Computer Engineering
Volume 2016 (2016), Article ID 2017373, 11 pages
Research Article

Communication Behaviour-Based Big Data Application to Classify and Detect HTTP Automated Software

Department of Computer Science, National Defense Academy, 1-10-20 Hashirimizu, Yokosuka, Kanagawa 239-0811, Japan

Received 25 December 2015; Revised 25 March 2016; Accepted 26 June 2016

Academic Editor: Jun Bi

Copyright © 2016 Manh Cong Tran and Yasuhiro Nakamura. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


HTTP is recognized as the most widely used protocol on the Internet when applications are being transferred more and more by developers onto the web. Due to increasingly complex computer systems, diversity HTTP automated software (autoware) thrives. Unfortunately, besides normal autoware, HTTP malware and greyware are also spreading rapidly in web environment. Consequently, network communication is not just rigorously controlled by users intention. This raises the demand for analyzing HTTP autoware communication behaviour to detect and classify malicious and normal activities via HTTP traffic. Hence, in this paper, based on many studies and analysis of the autoware communication behaviour through access graph, a new method to detect and classify HTTP autoware communication at network level is presented. The proposal system includes combination of MapReduce of Hadoop and MarkLogic NoSQL database along with xQuery to deal with huge HTTP traffic generated each day in a large network. The method is examined with real outbound HTTP traffic data collected through a proxy server of a private network. Experimental results obtained for proposed method showed that promised outcomes are achieved since 95.1% of suspicious autoware are classified and detected. This finding may assist network and system administrator in inspecting early the internal threats caused by HTTP autoware.