Intrusion Detection and Prevention on Flow of Big Data Using Bacterial Foraging
Khaleel Ahmad, Gaurav Kumar, Abdul Wahid, Mudasir Manzoor Kirmani · Advances in information security, privacy, and ethics book series · 2014
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.