Intrusion Detection System (IDS) is a vital component of any network in today’s world of Internet. IDS are an effective way to detect different kinds of attacks in interconnected network. An effective Intrusion Detection System requires high accuracy and detection rate as well as low false alarm rate. To tackle this growing trend in computer attacks and respond threats, industry professionals and academics are joining forces in order to build Intrusion Detection Systems (IDS) that combine high accuracy with low complexity and time efficiency. With the tremendous growth of usage of internet and development in web applications running on various platforms are becoming the major targets of attack. Security and privacy of a system is compromised, when an intrusion happens. Intrusion Detection System (IDS) plays vital role in network security as it detects various types of attacks in network. Implementation of an IDS is distinguishes between the traffic coming from clients and the traffic originated from the attackers or intruders, in an attempt to simultaneously mitigate the problems of throughput, latency and security of the network. Data mining based IDS can effectively identify intrusions. The proposed scheme is one of the recent enhancements of naive bayes algorithm. It solves the problem of independence by averaging all models generated by traditional one dependence estimator and is well suited for incremental learning. Empirical results show that proposed model based on SADE is efficient with low FAR and high DR.
Intrusion detection, Data Mining, SADE, NSL-KDD data set, False Alarm Ratio, Detection Rate
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