Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Data mining: concepts and techniques
Data mining: concepts and techniques
A framework for constructing features and models for intrusion detection systems
ACM Transactions on Information and System Security (TISSEC)
Levelwise Search and Borders of Theories in KnowledgeDiscovery
Data Mining and Knowledge Discovery
Special issue on data mining for intrusion detection and threat analysis
ACM SIGMOD Record
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Learning Rules for Anomaly Detection of Hostile Network Traffic
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
A Clustering Approach to Wireless Network Intrusion Detection
ICTAI '05 Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence
Applying a data mining method for intrusion detection
CompSysTech '07 Proceedings of the 2007 international conference on Computer systems and technologies
A hybrid data mining anomaly detection technique in ad hoc networks
International Journal of Wireless and Mobile Computing
A novel Network Intrusion Detection System (NIDS) based on signatures search of data mining
Proceedings of the 1st international conference on Forensic applications and techniques in telecommunications, information, and multimedia and workshop
NeuDetect: a neural network data mining wireless network intrusion detection system
Proceedings of the Fourteenth International Database Engineering & Applications Symposium
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Intrusion detection in wireless networks has become a vital part in wireless network security systems with wide spread use of Wireless Local Area Networks (WLAN). Currently, almost all devices are Wi-Fi (Wireless Fidelity) capable and can access WLAN. This paper proposes an Intrusion Detection System, WiFi Miner, which applies an infrequent pattern association rule mining Apriori technique to wireless network packets captured through hardware sensors for purposes of real time detection of intrusive or anomalous packets. Contributions of the proposed system includes effectively adapting an efficient data mining association rule technique to important problem of intrusion detection in a wireless network environment using hardware sensors, providing a solution that eliminates the need for hard-to-obtain training data in this environment, providing increased intrusion detection rate and reduction of false alarms. The proposed system, WiFi Miner solution approach is to find frequent and infrequent patterns on pre-processed wireless connection records using infrequent pattern finding Apriori algorithm proposed by this paper. The proposed Online Apriori-Infrequent algorithm improves the join and prune step of the traditional Apriori algorithm with a rule that avoids joining itemsets not likely to produce frequent itemsets as their results, there by improving efficiency and run times significantly. An anomaly score is assigned to each packet (record) based on whether the record has more frequent or infrequent patterns. Connection records with positive anomaly scores have more infrequent patterns than frequent patterns and are considered anomalous packets.