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Intrusion Detection and Prevention on Flow of Big Data Using Bacterial Foraging
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Author(s): Khaleel Ahmad (Maulana Azad National Urdu University, India), Gaurav Kumar (Swami Vivekananda Subharti University, India), Abdul Wahid (Maulana Azad National Urdu University, India)and Mudasir M. Kirmani (Sher-e-Kashmir University of Agricultural Science and Technology of Kashmir, India)
Copyright: 2015
Pages: 26
Source title:
Handbook of Research on Securing Cloud-Based Databases with Biometric Applications
Source Author(s)/Editor(s): Ganesh Chandra Deka (Ministry of Labour and Employment, India)and Sambit Bakshi (National Institute of Technology Rourkela, India)
DOI: 10.4018/978-1-4666-6559-0.ch018
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Abstract
Rapid connectivity and exchange of information across the globe with extension of computer networks during the past decade has led to security threats in network communication and has become a critical concern for network management. It is necessary to retain high security measures to ensure safe and trusted communication across the network. Diverse soft-computing-based methods have been devised in the past for the perfection of intrusion detection systems on host-based and host-independent systems. This chapter discusses the flow-based anomaly detector for intrusion in network by self-learning process with characteristics of bacterial forging approach. This approach handles the network-flow and attack on network traffic in an automated fashion. This approach works on host-independent systems and on stream of network rather than payload length where data behavior of flow in network is analyzed. This model provides a cataloging of attacks and resistance mechanism techniques to avoid intrusion.
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