IEEE Transactions on Software Engineering - Special issue on computer security and privacy
C4.5: programs for machine learning
C4.5: programs for machine learning
State Transition Analysis: A Rule-Based Intrusion Detection Approach
IEEE Transactions on Software Engineering
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Bayesian classification (AutoClass): theory and results
Advances in knowledge discovery and data mining
Towards a taxonomy of intrusion-detection systems
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue on computer network security
The 1999 DARPA off-line intrusion detection evaluation
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue on recent advances in intrusion detection systems
A framework for constructing features and models for intrusion detection systems
ACM Transactions on Information and System Security (TISSEC)
ACM Transactions on Information and System Security (TISSEC)
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Machine Learning
Artficial Immune Systems and Their Applications
Artficial Immune Systems and Their Applications
Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
Neural Networks: Tricks of the Trade, this book is an outgrowth of a 1996 NIPS workshop
A Case-Based Reasoning Approach to the Resolution of Faults in Communication Networks
Proceedings of the IFIP TC6/WG6.6 Third International Symposium on Integrated Network Management with participation of the IEEE Communications Society CNOM and with support from the Institute for Educational Services
NetSTAT: A Network-Based Intrusion Detection Approach
ACSAC '98 Proceedings of the 14th Annual Computer Security Applications Conference
Intrusion Detection Applying Machine Learning to Solaris Audit Data
ACSAC '98 Proceedings of the 14th Annual Computer Security Applications Conference
Effective Intrusion Detection Using Multiple Sensors in Wireless Ad Hoc Networks
HICSS '03 Proceedings of the 36th Annual Hawaii International Conference on System Sciences (HICSS'03) - Track 2 - Volume 2
SAINT '03 Proceedings of the 2003 Symposium on Applications and the Internet
Identifying key features for intrusion detection using neural networks
ICCC '02 Proceedings of the 15th international conference on Computer communication
Results of the KDD'99 classifier learning
ACM SIGKDD Explorations Newsletter
eXpert-BSM: A Host-Based Intrusion Detection Solution for Sun Solaris
ACSAC '01 Proceedings of the 17th Annual Computer Security Applications Conference
USTAT: A Real-Time Intrusion Detection System for UNIX
SP '93 Proceedings of the 1993 IEEE Symposium on Security and Privacy
Information-Theoretic Measures for Anomaly Detection
SP '01 Proceedings of the 2001 IEEE Symposium on Security and Privacy
Naive Bayes vs decision trees in intrusion detection systems
Proceedings of the 2004 ACM symposium on Applied computing
Ant Colony Optimization
Class imbalances versus small disjuncts
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
HMM profiles for network traffic classification
Proceedings of the 2004 ACM workshop on Visualization and data mining for computer security
Intrusion Detection and Correlation: Challenges and Solutions
Intrusion Detection and Correlation: Challenges and Solutions
Unsupervised anomaly detection in network intrusion detection using clusters
ACSC '05 Proceedings of the Twenty-eighth Australasian conference on Computer Science - Volume 38
Intelligent Bayesian classifiers in network intrusion detection
IEA/AIE'2005 Proceedings of the 18th international conference on Innovations in Applied Artificial Intelligence
On the combination of naive Bayes and decision trees for intrusion detection
CIMCA '05 Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce Vol-1 (CIMCA-IAWTIC'06) - Volume 01
On the combination of naive Bayes and decision trees for intrusion detection
CIMCA '05 Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce Vol-1 (CIMCA-IAWTIC'06) - Volume 01
Efficacy of Hidden Markov Models Over Neural Networks in Anomaly Intrusion Detection
COMPSAC '06 Proceedings of the 30th Annual International Computer Software and Applications Conference - Volume 01
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Learning Greek verb complements: addressing the class imbalance
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Modeling intrusion detection system using hybrid intelligent systems
Journal of Network and Computer Applications - Special issue: Network and information security: A computational intelligence approach
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Comparative Study of Supervised Machine Learning Techniques for Intrusion Detection
CNSR '07 Proceedings of the Fifth Annual Conference on Communication Networks and Services Research
Why machine learning algorithms fail in misuse detection on KDD intrusion detection data set
Intelligent Data Analysis
Processing of massive audit data streams for real-time anomaly intrusion detection
Computer Communications
Author identification: Using text sampling to handle the class imbalance problem
Information Processing and Management: an International Journal
Definition Extraction with Balanced Random Forests
GoTAL '08 Proceedings of the 6th international conference on Advances in Natural Language Processing
Ensemble of One-Class Classifiers for Network Intrusion Detection System
IAS '08 Proceedings of the 2008 The Fourth International Conference on Information Assurance and Security
Intrusion Detection Using Evolutionary Neural Networks
PCI '08 Proceedings of the 2008 Panhellenic Conference on Informatics
Handling class imbalance in customer churn prediction
Expert Systems with Applications: An International Journal
Customer churn prediction using improved balanced random forests
Expert Systems with Applications: An International Journal
Intrusion detection using fuzzy association rules
Applied Soft Computing
Enhancing network based intrusion detection for imbalanced data
International Journal of Knowledge-based and Intelligent Engineering Systems
Ensemble of classifiers for detecting network intrusion
Proceedings of the International Conference on Advances in Computing, Communication and Control
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Learning from imbalanced data in surveillance of nosocomial infection
Artificial Intelligence in Medicine
Expert Systems with Applications: An International Journal
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The KDD Cup '99 data set has been widely used to evaluate intrusion detection prototypes, most based on machine learning techniques, for nearly a decade. The data set served well in the KDD Cup '99 competition to demonstrate that machine learning can be useful in intrusion detection systems. However, there are discrepancies in the findings reported in the literature. Further, some researchers have published criticisms of the data (and the DARPA data from which the KDD Cup '99 data has been derived), questioning the validity of results obtained with this data. Despite the criticisms, researchers continue to use the data due to a lack of better publicly available alternatives. Hence, it is important to identify the value of the data set and the findings from the extensive body of research based on it, which has largely been ignored by the existing critiques. This paper reports on an empirical investigation, demonstrating the impact of several methodological differences in the publicly available subsets, which uncovers several underlying causes of the discrepancy in the results reported in the literature. These findings allow us to better interpret the current body of research, and inform recommendations for future use of the data set.