@inproceedings{tavallaee_detailed_2009,
	title = {A detailed analysis of the {KDD} {CUP} 99 data set},
	doi = {10.1109/CISDA.2009.5356528},
	abstract = {During the last decade, anomaly detection has attracted the attention of many researchers to overcome the weakness of signature-based IDSs in detecting novel attacks, and KDDCUP'99 is the mostly widely used data set for the evaluation of these systems. Having conducted a statistical analysis on this data set, we found two important issues which highly affects the performance of evaluated systems, and results in a very poor evaluation of anomaly detection approaches. To solve these issues, we have proposed a new data set, NSL-KDD, which consists of selected records of the complete KDD data set and does not suffer from any of mentioned shortcomings.},
	booktitle = {2009 {IEEE} {Symposium} on {Computational} {Intelligence} for {Security} and {Defense} {Applications}},
	author = {Tavallaee, Mahbod and Bagheri, Ebrahim and Lu, Wei and Ghorbani, Ali A.},
	month = jul,
	year = {2009},
	note = {ISSN: 2329-6275},
	keywords = {Application software, Computational intelligence, Computer aided manufacturing, Computer networks, Computer security, Data security, Intrusion detection, KDD CUP 99 data set analysis, Learning systems, Statistical analysis, Testing, anomaly detection, attack detection, security of data, signature-based intrusion detection system, statistical analysis},
	pages = {1--6},
}

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