Intrusion Detection and Prevention on Flow of Big Data Using Bacterial Foraging

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.