NDLI: Detecting hiding malicious website using network traffic mining approach

Content Provider	IEEE Xplore Digital Library
Author	Han-Wei Hsiao Deng-Neng Chen Tsung Ju Wu
Copyright Year	2010
Description	Author affiliation: National Pingtung University of Science and Technology, Depart. of Management Information Systems, Taiwan (Deng-Neng Chen) \|\| National University of Kaohsiung, Depart. of Information Management, Taiwan (Han-Wei Hsiao; Tsung Ju Wu)
Abstract	As the Internet continues to broaden its coverage worldwide, it has leaded to a spread of data searching, learning, entertaining, information exchanging, financing, commercial activities and so on via Internet. This tendency makes a serious situation that is the users of the Internet become attacking targets. There are many kind of network attack such as viruses, worms, and many other malicious codes were implemented to get the illegal benefits or for some particular purpose. In recent years, firewall techniques were being used to reject the anomaly Internet connections. And this has made the spreading of malwares gradually shifted from the traditional “Push-based” method to the “Pull-based” method. Therefore, how to prevent the illegitimate access from the attacker and maintaining the quality of service of network becomes an important issue of the network manager. In 2008, there was a new kind malware be found, that have some new features in comparison of the traditional malwares. Further, those codes can be self-updated by Internet. There are many malicious websites propose new version malicious code for the malware infect other computers under the same LAN to download and execute the malicious program automatically. These kinds of malicious websites cannot be easily detected in traditional firewall defense systems. This research proposed a malicious website detection system architecture and use spatial-temporal aggregating variables method to build a detection module from the NetFlow data. In our empirical evaluation results show this module has good performance to detect the malicious web sites. The results are helpful to improve the management of the large range network environment.
File Size	896142
File Format	PDF
ISBN	9781424463671
e-ISBN	9781424463701
DOI	10.1109/ICETC.2010.5530064
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-06-22
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computer worms Spatial-Temporal Pattern Telecommunication traffic Quality of service Network Security NetFlow Malicious Website Detection Environmental management Computer architecture Malware Internet Computer network management Viruses (medical) Local area networks Quality management
Content Type	Text
Resource Type	Article

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