A detailed analysis of the KDD CUP 99 data set

By Mahbod Tavallaee, Ebrahim Bagheri, Wei Lu, Ali Akbar Ghorbani. IEEE Symposium on Computational Intelligence for Security and Defense Applications, 2009

4,970 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Signal Processing

View Paper: https://www.semanticscholar.org/paper/fc3eb090e39d71295c362458b8a0c48d2c5d8377

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Citations in the corpus

A comprehensive survey on machine learning for networking: evolution, applications and research opportunities (2018, methodology): “Once more, all these works have been evaluated using KDD dataset, and its enhanced version NSL-KDD [438] dataset.”
Detecting and Preventing Cyber Insider Threats: A Survey (2018, background): “Four popular categories of attack are simulated and have affected the data set, namely DoS, User-to-Root, Remoteto-Local and probing [113].”
Machine Learning: Algorithms, Real-World Applications and Research Directions (2021, background): “These are, for example, cybersecurity datasets such as NSL-KDD [119], UNSW-NB15 [76], ISCX’12 [1], CIC-DDoS2019 [2], Bot-IoT [59], etc.”
A Survey of Deep Learning Methods for Cyber Security (2019, methodology): “[141] 36 Intrusion Detection NSL-KDD [58]”
Federated Learning for Intrusion Detection System: Concepts, Challenges and Future Directions (2021, background): “Tout, Talhi, and Mourad] evaluates the efficiency of federated architecture in IDS through NSL-KDD dataset [Tavallaee et al.(2009)Tavallaee, Bagheri, Lu, and Ghorbani].”
HAST-IDS: Learning Hierarchical Spatial-Temporal Features Using Deep Neural Networks to Improve Intrusion Detection (2018, background): “Niyaz et al. [6] studied the intrusion detection method on the NSL-KDD dataset using deep belief networks.”
Network Anomaly Detection: Methods, Systems and Tools (2014, background): “NSL-KDD Yes No Yes 148517 41 BCTW P C1 [207] DARPA 2000 Yes No No Huge Not known P Raw C2 [214]”
BAT: Deep Learning Methods on Network Intrusion Detection Using NSL-KDD Dataset (2020, methodology): “The NSL-KDD dataset is an enhanced version of KDD cup 1999 dataset [48], [49], which is widely used in intrusion detection experiments.”
TSE-IDS: A Two-Stage Classifier Ensemble for Intelligent Anomaly-Based Intrusion Detection System (2019, methodology): “Most IDS research has focused on the utilization of long-established classification approaches either using individual classifiers, such as naive Bayes [10], [17], decision tree [10], [18]–[20], support vectormachines [10], [21], [22], and naive Bayes tree [10]; or meta classifiers, i.e., bagging [2”
Applying convolutional neural network for network intrusion detection (2017, background): “NSL-KDD is the refined version of KDDCup 99 challenge data set that is contributed by [30].”
A Survey of Machine Learning Techniques Applied to Software Defined Networking (SDN): Research Issues and Challenges (2019, background): “NSL-KDD is a modified version of KDD99 dataset and has solved many inherent problems of KDD99 dataset.”
A survey of deep learning-based network anomaly detection (2017, methodology): “Table 1 NSL-KDD training and testing data set files [38]”
A Comprehensive Review on Malware Detection Approaches (2020, methodology): “• NSL-KDD dataset (2009): It is an updated version of the KDD’99 dataset which consists of approximately 125,000 records and 41 features [22].”