IEEE Transactions on Software Engineering - Special issue on computer security and privacy
Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Algorithms for association rule mining — a general survey and comparison
ACM SIGKDD Explorations Newsletter
Anomaly Detection over Noisy Data using Learned Probability Distributions
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
STING: A Statistical Information Grid Approach to Spatial Data Mining
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
A Scalable Parallel Subspace Clustering Algorithm for Massive Data Sets
ICPP '00 Proceedings of the Proceedings of the 2000 International Conference on Parallel Processing
Anomaly Detection Using Real-Valued Negative Selection
Genetic Programming and Evolvable Machines
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Unsupervised learning techniques for an intrusion detection system
Proceedings of the 2004 ACM symposium on Applied computing
Data mining approaches for intrusion detection
SSYM'98 Proceedings of the 7th conference on USENIX Security Symposium - Volume 7
Description of bad-signatures for network intrusion detection
ACSW Frontiers '06 Proceedings of the 2006 Australasian workshops on Grid computing and e-research - Volume 54
Authentication anomaly detection: a case study on a virtual private network
Proceedings of the 3rd annual ACM workshop on Mining network data
Detecting anomalous records in categorical datasets
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Building intrusion pattern miner for Snort network intrusion detection system
Journal of Systems and Software
Classifier ensembles: Select real-world applications
Information Fusion
Local anomaly detection for mobile network monitoring
Information Sciences: an International Journal
Network Anomalous Attack Detection Based on Clustering and Classifier
Computational Intelligence and Security
Unsupervised Anomaly Detection Using HDG-Clustering Algorithm
Neural Information Processing
A Clustering Method for Improving Performance of Anomaly-Based Intrusion Detection System
IEICE - Transactions on Information and Systems
Implementing IDS Management on Lock-Keeper
ISPEC '09 Proceedings of the 5th International Conference on Information Security Practice and Experience
Application of Clustering Techniques in a Network Security Testing System
Proceedings of the 2005 conference on Artificial Intelligence Research and Development
Multiobjective Evolutionary Clustering Approach to Security Vulnerability Assesments
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
Anomaly detection inspired by immune network theory: a proposal
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A detailed analysis of the KDD CUP 99 data set
CISDA'09 Proceedings of the Second IEEE international conference on Computational intelligence for security and defense applications
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
The use of artificial intelligence based techniques for intrusion detection: a review
Artificial Intelligence Review
Exploring discrepancies in findings obtained with the KDD Cup '99 data set
Intelligent Data Analysis
sub-space clustering and evidence accumulation for unsupervised network anomaly detection
TMA'11 Proceedings of the Third international conference on Traffic monitoring and analysis
UNADA: unsupervised network anomaly detection using sub-space outliers ranking
NETWORKING'11 Proceedings of the 10th international IFIP TC 6 conference on Networking - Volume Part I
Analysis of vulnerability assessment results based on CAOS
Applied Soft Computing
Proceedings of the 7th International Conference on Network and Services Management
Unsupervised Network Intrusion Detection Systems: Detecting the Unknown without Knowledge
Computer Communications
Cohesion factors: improving the clustering capabilities of consensus
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
An effective unsupervised network anomaly detection method
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
Toward a more practical unsupervised anomaly detection system
Information Sciences: an International Journal
Service-independent payload analysis to improve intrusion detection in network traffic
AusDM '08 Proceedings of the 7th Australasian Data Mining Conference - Volume 87
Network Anomaly Detection Using Co-clustering
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Data summarization for network traffic monitoring
Journal of Network and Computer Applications
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Most current network intrusion detection systems employ signature-based methods or data mining-based methods which rely on labelled training data. This training data is typically expensive to produce. Moreover, these methods have difficulty in detecting new types of attack. Using unsupervised anomaly detection techniques, however, the system can be trained with unlabelled data and is capable of detecting previously "unseen" attacks. In this paper, we present a new density-based and grid-based clustering algorithm that is suitable for unsupervised anomaly detection. We evaluated our methods using the 1999 KDD Cup data set. Our evaluation shows that the accuracy of our approach is close to that of existing techniques reported in the literature, and has several advantages in terms of computational complexity.