Intrusion Detection System using Artificial Immune Systems: A Case Study

Abstract

Abstract-Networks are working at their apical efficiency and are increasing in size by every second; emergence of various threats becomes hindrance in the growth and privacy of the users. The network is vulnerable to security breaches, due to malicious nodes. Intrusion detection systems aim at removing this vulnerability. In this paper, intrusion detection mechanisms for large-scale dynamic networks are investigated. Artificial immune system is a concept that works to protect a network the way immune systems of vertebrates work in nature. This paper also illustrates this artificial immune system, the integration of bio-inspired algorithms, and its functionality with the computer networks.Keywords-Intrusion Detection, Artificial Immune System, Negative Selection Algorithm, R Contiguous bits, Network Security I. INTRODUCTIONHumans made computers to automate our routine tasks. The outset of computer systems eased a lot of pain on mankind and therefore it should be our duty to care for our helper as one of our own. In the last few decades, a lot of efforts have been put in the operational welfare of computer systems. The aim is to work with an intrusion detection system like, the immune system of humans that protects the systems from intrusions and remove anomalies. In this paper, existing intrusion detection system models are enunciated. An intrusion detection system (IDS) is responsible for monitoring visitors on a network and units that approach the network, for suspicious activities and informs administrator responsible, about the anomalies. There are completely network-based (NIDS) and completely host-based (HIDS) intrusion detection structures. There are IDS that genuinely alert and reveal about the anomalies and there are IDS that carry out an action or moves in response to a detected risk. IDS may be a high-quality tool for proactively tracking and shielding your network from the malicious activity, but, they are also at risk of false alarms. Just like our immune systems.So, models of artificial immune systems that work as an efficient intrusion detection mechanism for computer networks are scope of studying.The main issue that is being reviewed is the identification of detectors which are an integral unit of Artificial Immune System (AIS). A constructive approach to understanding the formation and identification of detectors and their functionality that involve concepts of machine learning and data analysis is taken. Although the mechanisms are still raw as compared to their biological counterparts, it has thus been only possible to analyze the functional behavior of the AIS model and evaluating the value of the process.The goal of this paper is make the biological connections more concrete and emphasize the adaptive systems framework in which is the basis of our implementation. In next section (II), the basic description of Artificial Immune System is given. In section III, a gist of working of human immune system is given where the immune response is described in sub-section A and structure of immune syst...