source code for intrusion detection using kdd
#1

I want source code for intrusion detection using kdd in matlab. I don't know how to use kdd cup 99 dataset in matlab
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#2

The rapid development of business and other systems of transactions through the Internet makes computer security a critical problem. In recent times, data mining and automatic learning have been the subject of extensive investigation in the detection of intruders with an emphasis on improving the accuracy of the detection classifier. But the selection of important characteristics of the input data leads to a simplification of the problem, faster detection rates and more accurate. In this paper we present the relevance of each feature in the intrusion detection data set KDD '99 for the detection of each class. The degree of dependence and the dependency ratio of each class were used to determine the most discriminating characteristics for each class. The empirical results show that seven characteristics were not relevant in the detection of any kind.


As the Internet continues to grow at an exponential rate, so are cybercrime attacks exploiting flaws in Internet protocols, operating system, and application software. Various protection measures such as the firewall have been put in place to check the activities of intruders who can not guarantee the full protection of the system. Therefore, the need for a more dynamic mechanism such as intrusion detection system (IDS) as the second line of defense. Intrusion detection is the process of monitoring events occurring on a computer system or network and analyzing them for signs of intrusion.

The KDD 99 intrusion detection datasets, which are based on the DARPA 98 dataset, provide tagged data for investigators working in the field of intrusion detection and are the only publicly available tagged data set. Numerous investigators used the datasets in the KDD 99 intrusion detection datasets to study the use of automated learning for intrusion detection and detection rates up to 91% with false-positive rates of less than 1%. To substantiate the performance of the detectors based on machine learning that are trained in the KDD 99 training data; We investigate the relevance of each feature in the KDD 99 intrusion detection datasets. To this end, the information gain is used to determine the most discriminating characteristics for each class.
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