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Discovering Periodicity in Network Flows for Security Monitoring

Discovering Periodicity in Network Flows for Security Monitoring

Neminath Hubballi, Deepanshu Goyal
Copyright: © 2016 |Pages: 20
ISBN13: 9781522501053|ISBN10: 1522501053|EISBN13: 9781522501060
DOI: 10.4018/978-1-5225-0105-3.ch008
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MLA

Hubballi, Neminath, and Deepanshu Goyal. "Discovering Periodicity in Network Flows for Security Monitoring." Handbook of Research on Modern Cryptographic Solutions for Computer and Cyber Security, edited by Brij Gupta, et al., IGI Global, 2016, pp. 180-199. https://doi.org/10.4018/978-1-5225-0105-3.ch008

APA

Hubballi, N. & Goyal, D. (2016). Discovering Periodicity in Network Flows for Security Monitoring. In B. Gupta, D. Agrawal, & S. Yamaguchi (Eds.), Handbook of Research on Modern Cryptographic Solutions for Computer and Cyber Security (pp. 180-199). IGI Global. https://doi.org/10.4018/978-1-5225-0105-3.ch008

Chicago

Hubballi, Neminath, and Deepanshu Goyal. "Discovering Periodicity in Network Flows for Security Monitoring." In Handbook of Research on Modern Cryptographic Solutions for Computer and Cyber Security, edited by Brij Gupta, Dharma P. Agrawal, and Shingo Yamaguchi, 180-199. Hershey, PA: IGI Global, 2016. https://doi.org/10.4018/978-1-5225-0105-3.ch008

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Abstract

Increasingly system software and user applications are becoming automated and thus many of inter machine communications are not user action driven. Some of these automated communications like OS updates, database synchronization will not pose security threats, while others can have malicious behavior. Automated communications pose a threat to the security of systems if initiated by unwanted programs like keyloggers and Botnets. As these applications are programmed to contact a peer host regularly, most of these communications are periodic in nature. In this chapter we describe a method for detecting periodic communications by analyzing network flows for security monitoring. In particular we use a clustering technique to identify periodic communications between hosts. We experiment with both simulated and real world data to evaluate the efficacy of method.

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