Here, the focus is on the effective early detection scheme for distinguishing Distributed Denial of Service (DDoS) attack traffic from normal flash crowd traffic. This paper surveys with the emerging research on various methods to identify the legitimate/illegitimate traffic on the network. The experimentalresults clearly show that the proposed system achieved higher precision in identifying whether the records are normal or attack one. The experiments and evaluations of the proposed intrusion detection system are performed with the KDD Cup 99 intrusion detection dataset. Here, we have used automated strategy for generation of fuzzy rules, which are obtained from the definite rules using frequent items. The proposed fuzzy logic-based system can be able to detect an intrusion behavior of the networks since the rule base contains a better set of rules. In the proposed system, we have designed fuzzy logic-based system for effectively identifying the intrusion activities within a network. To reduce this dependence, variousdata-mining and machine learning techniques have been used in the literature. In general, the traditional intrusion detection relies on the extensive knowledge of security experts, in particular, on their familiarity with the computer system to be protected.

IDS which are increasingly a key part of system defense are used to identify abnormal activities in a computer system.

Ventrilo rcon command iptrace