Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Fuzzy set theory—and its applications (3rd ed.)
Fuzzy set theory—and its applications (3rd ed.)
Mining fuzzy association rules in databases
ACM SIGMOD Record
Improving intrusion detection performance using keyword selection and neural networks
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue on recent advances in intrusion detection systems
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
A Neural Network Component for an Intrusion Detection System
SP '92 Proceedings of the 1992 IEEE Symposium on Security and Privacy
On Dataset Biases in a Learning System with Minimum A Priori Information for Intrusion Detection
CNSR '04 Proceedings of the Second Annual Conference on Communication Networks and Services Research
H∞ estimation for fuzzy membership function optimization
International Journal of Approximate Reasoning
Structural damage detection using fuzzy cognitive maps and Hebbian learning
Applied Soft Computing
Neural visualization of network traffic data for intrusion detection
Applied Soft Computing
Exploring discrepancies in findings obtained with the KDD Cup '99 data set
Intelligent Data Analysis
Testing ensembles for intrusion detection: On the identification of mutated network scans
CISIS'11 Proceedings of the 4th international conference on Computational intelligence in security for information systems
Mining the change of customer behavior in fuzzy time-interval sequential patterns
Applied Soft Computing
Computational intelligence algorithms analysis for smart grid cyber security
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part II
A hybrid network intrusion detection system using simplified swarm optimization (SSO)
Applied Soft Computing
RT-MOVICAB-IDS: Addressing real-time intrusion detection
Future Generation Computer Systems
Journal of Network and Computer Applications
A fuzzy coherent rule mining algorithm
Applied Soft Computing
A combined mining-based framework for predicting telecommunications customer payment behaviors
Expert Systems with Applications: An International Journal
An effective parallel approach for genetic-fuzzy data mining
Expert Systems with Applications: An International Journal
A distance sum-based hybrid method for intrusion detection
Applied Intelligence
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Vulnerabilities in common security components such as firewalls are inevitable. Intrusion Detection Systems (IDS) are used as another wall to protect computer systems and to identify corresponding vulnerabilities. In this paper, a novel framework based on data mining techniques is proposed for designing an IDS. In this framework, the classification engine, which is actually the core of the IDS, uses Association Based Classification (ABC). The proposed classification algorithm uses fuzzy association rules for building classifiers. Particularly, the fuzzy association rulesets are exploited as descriptive models of different classes. The compatibility of any new sample (which is to be classified) with different class rulesets is assessed by the use of some matching measures and the class corresponding to the best matched ruleset is declared as the label of the sample. A new method is also proposed to speed up the rule induction algorithm via reducing items that may be included in extracted rules. KDD-99 dataset is used to evaluate the proposed framework. Although results on unseen attacks are not so promising, total detection rate and detection rate of known attacks is significant while false positive rate is kept low. Results are compared with some recent works in the literature using the same dataset. Generally, the proposed approach outperforms other methods, specially in terms of false positive rate.