IEEE Transactions on Software Engineering - Special issue on computer security and privacy
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Particle swarm based Data Mining Algorithms for classification tasks
Parallel Computing - Special issue: Parallel and nature-inspired computational paradigms and applications
Intrusion detection using an ensemble of intelligent paradigms
Journal of Network and Computer Applications - Special issue on computational intelligence on the internet
Application of SVM and ANN for intrusion detection
Computers and Operations Research
IDEAS: Intrusion Detection based on Emotional Ants for Sensors
ISDA '05 Proceedings of the 5th International Conference on Intelligent Systems Design and Applications
A new discrete particle swarm algorithm applied to attribute selection in a bioinformatics data set
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Feature selection based on rough sets and particle swarm optimization
Pattern Recognition Letters
An Improved Ant-based Classifier for Intrusion Detection
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 04
Biologically-inspired Complex Adaptive Systems approaches to Network Intrusion Detection
Information Security Tech. Report
A hybrid artificial immune system and Self Organising Map for network intrusion detection
Information Sciences: an International Journal
Using a hybrid meta-evolutionary rule mining approach as a classification response model
Expert Systems with Applications: An International Journal
Data mining-based intrusion detectors
Expert Systems with Applications: An International Journal
Intrusion detection using fuzzy association rules
Applied Soft Computing
Expert Systems with Applications: An International Journal
An efficient hybrid data clustering method based on K-harmonic means and Particle Swarm Optimization
Expert Systems with Applications: An International Journal
An Improved PSO-Based Rule Extraction Algorithm for Intrusion Detection
CINC '09 Proceedings of the 2009 International Conference on Computational Intelligence and Natural Computing - Volume 02
Review: The use of computational intelligence in intrusion detection systems: A review
Applied Soft Computing
An evolutionary memetic algorithm for rule extraction
Expert Systems with Applications: An International Journal
Computers and Operations Research
Feature selection with Intelligent Dynamic Swarm and Rough Set
Expert Systems with Applications: An International Journal
Classifier design for static security assessment using particle swarm optimization
Applied Soft Computing
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Data mining with an ant colony optimization algorithm
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Information Technology in Biomedicine
Local search techniques for large high school timetabling problems
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Review: Intrusion detection system: A comprehensive review
Journal of Network and Computer Applications
A distance sum-based hybrid method for intrusion detection
Applied Intelligence
Hi-index | 0.00 |
The network intrusion detection techniques are important to prevent our systems and networks from malicious behaviors. However, traditional network intrusion prevention such as firewalls, user authentication and data encryption have failed to completely protect networks and systems from the increasing and sophisticated attacks and malwares. In this paper, we propose a new hybrid intrusion detection system by using intelligent dynamic swarm based rough set (IDS-RS) for feature selection and simplified swarm optimization for intrusion data classification. IDS-RS is proposed to select the most relevant features that can represent the pattern of the network traffic. In order to improve the performance of SSO classifier, a new weighted local search (WLS) strategy incorporated in SSO is proposed. The purpose of this new local search strategy is to discover the better solution from the neighborhood of the current solution produced by SSO. The performance of the proposed hybrid system on KDDCup 99 dataset has been evaluated by comparing it with the standard particle swarm optimization (PSO) and two other most popular benchmark classifiers. The testing results showed that the proposed hybrid system can achieve higher classification accuracy than others with 93.3% and it can be one of the competitive classifier for the intrusion detection system.