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Knowledge Computational Intelligence in Network Intrusion Detection Systems

Chapter

Abstract

An Intrusion Detection System (IDS) is acting as first line of intrusion detection for providing network security in various areas like defence, e-commerce, autonomous systems etc. For network administrator, it plays an important role in understanding details about packet arriving and numerous activities involved within network. It helps the administrator in taking decisions at every stage of network life cycle. It can never access the IDS without knowing the performance of measure. Two most popular issues involved to exploit security are virus and attacker, generally termed as hacker. Generally, hacker is stimulated by adventure of importance. Hacker societies are strong in abstraction and their status is firm by their ability level. Gentle intruder gets through assets and slows the outcomes for genuine user like IDS and intrusion prevention systems (IPSs) are intended to help in frustrating hacker terrorization which can restrict remote logons to specific IP addresses and can utilize within a virtual private network technology (VPN). Many intrusion detection methodologies are proposed so far to resolve such issues but the main problem was performance of network and accuracy in detection of intrusion. To achieve this, there should be a strong mechanism for having true knowledge about the data which is flowing over the network. Proposed work done with study and analysis of various existing intrusion detection techniques and found lack in true prediction of intrusion. Considering these challenges, proposed work focused on handling both types of intrusion either anomaly based or signature based as complete hybrid model. Through knowledge extraction using soft computing and minimizing false alarm problem, proposed hybrid model found as true result-oriented intrusion detector.

Keywords

NIDS FLC MLP KDD99 Alarm Attack ANFIS Genetic Expert system Soft computing 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringBirla Institute of TechnologyMesraIndia

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