Published Paper Details:

Intrusion-Detection Systems (IDSs) is an evolving technology for protecting computer networks. For instance, in earlier day's Denial-of-Service (DoS) attack cannot cause serious disasters, but today, successful DoS attacks can cause great financial loss to organizations. The goal of intrusion-detection systems is to detect anomalous or misuse behavior of system and notify to network administrators about the activities. Many intrusions-detection tools have security weaknesses such as failing to encrypt the log files, ignoring access control, and failing to perform integrity checks, etc., An IDS is more secure than other security tools, such as firewalls [2]. Earlier research system based on two major concepts known as anomaly detection and signature detection based on abnormal behavior of the system. Initially IDS consists of collection of audit data from the observed system. Then this data is either preprocessed or directly applied to the detector to generate an alarm. The main aim of IDS is to increase detection rate and to reduce false alarm rate in detecting attacks [5]. Recently, the researcher mainly focused on anomaly detection based on proposed methodologies such as data mining, SVM, Fuzzy Genetic in detecting intrusive behavior of the system.