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A Survey of Fast Flux Botnet Detection With Fast Flux Cloud Computing

Author

Listed:
  • Ahmad Al-Nawasrah

    (Taibah University, Saudi Arabia)

  • Ammar Ali Almomani

    (Al-Balqa Applied University, Jordan)

  • Samer Atawneh

    (College of Computing and Informatics, Saudi Electronic University, Saudi Arabia)

  • Mohammad Alauthman

    (Department of Computer Science, Faculty of Information Technology, Zarqa University, Jordan)

Abstract

A botnet refers to a set of compromised machines controlled distantly by an attacker. Botnets are considered the basis of numerous security threats around the world. Command and control (C&C) servers are the backbone of botnet communications, in which bots send a report to the botmaster, and the latter sends attack orders to those bots. Botnets are also categorized according to their C&C protocols, such as internet relay chat (IRC) and peer-to-peer (P2P) botnets. A domain name system (DNS) method known as fast-flux is used by bot herders to cover malicious botnet activities and increase the lifetime of malicious servers by quickly changing the IP addresses of the domain names over time. Several methods have been suggested to detect fast-flux domains. However, these methods achieve low detection accuracy, especially for zero-day domains. They also entail a significantly long detection time and consume high memory storage. In this survey, we present an overview of the various techniques used to detect fast-flux domains according to solution scopes, namely, host-based, router-based, DNS-based, and cloud computing techniques. This survey provides an understanding of the problem, its current solution space, and the future research directions expected.

Suggested Citation

  • Ahmad Al-Nawasrah & Ammar Ali Almomani & Samer Atawneh & Mohammad Alauthman, 2020. "A Survey of Fast Flux Botnet Detection With Fast Flux Cloud Computing," International Journal of Cloud Applications and Computing (IJCAC), IGI Global, vol. 10(3), pages 17-53, July.
  • Handle: RePEc:igg:jcac00:v:10:y:2020:i:3:p:17-53
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    Cited by:

    1. agarwal, shekhar & Gordon, Anna, 2022. "Complexities for the Indian Economy of China's Growing Technological Competence," OSF Preprints fk3r7, Center for Open Science.
    2. Zhang Ling & Zhang Jia Hao, 2022. "Intrusion Detection Using Normalized Mutual Information Feature Selection and Parallel Quantum Genetic Algorithm," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 18(1), pages 1-24, January.
    3. agarwal, shekhar, 2022. "India’s Rising Technology Economy: Sources and Consequences," OSF Preprints x6yzm, Center for Open Science.

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