Unsupervised Feature Selection Using Feature Similarity
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evolving data into mining solutions for insights
Communications of the ACM - Evolving data mining into solutions for insights
Machine Learning
Naive Bayes vs decision trees in intrusion detection systems
Proceedings of the 2004 ACM symposium on Applied computing
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
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
Decision tree classifier for network intrusion detection with GA-based feature selection
Proceedings of the 43rd annual Southeast regional conference - Volume 2
IEEE Transactions on Knowledge and Data Engineering
NeC4.5: Neural Ensemble Based C4.5
IEEE Transactions on Knowledge and Data Engineering
Network Intrusion Detection System Using Neural Networks
ICNC '08 Proceedings of the 2008 Fourth International Conference on Natural Computation - Volume 05
A Classifier Ensemble Approach to Intrusion Detection for Network-Initiated Attacks
Proceedings of the 2007 conference on Emerging Artificial Intelligence Applications in Computer Engineering: Real Word AI Systems with Applications in eHealth, HCI, Information Retrieval and Pervasive Technologies
Improvement in intrusion detection with advances in sensor fusion
IEEE Transactions on Information Forensics and Security
Neural network based intrusion detection system for critical infrastructures
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
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
Hierarchical Kohonenen net for anomaly detection in network security
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Evolutionary neural networks for anomaly detection based on the behavior of a program
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
AdaBoost-Based Algorithm for Network Intrusion Detection
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An Optimum-Path Forest framework for intrusion detection in computer networks
Engineering Applications of Artificial Intelligence
International Journal of Decision Support System Technology
D0M-WLAN: a traffic analysis based approach for detecting malicious activities on wireless networks
Proceedings of the 6th International Conference on Security of Information and Networks
A novel intrusion detection system based on feature generation with visualization strategy
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
The objective of this paper is to construct a lightweight Intrusion Detection System (IDS) aimed at detecting anomalies in networks. The crucial part of building lightweight IDS depends on preprocessing of network data, identifying important features and in the design of efficient learning algorithm that classify normal and anomalous patterns. Therefore in this work, the design of IDS is investigated from these three perspectives. The goals of this paper are (i) removing redundant instances that causes the learning algorithm to be unbiased (ii) identifying suitable subset of features by employing a wrapper based feature selection algorithm (iii) realizing proposed IDS with neurotree to achieve better detection accuracy. The lightweight IDS has been developed by using a wrapper based feature selection algorithm that maximizes the specificity and sensitivity of the IDS as well as by employing a neural ensemble decision tree iterative procedure to evolve optimal features. An extensive experimental evaluation of the proposed approach with a family of six decision tree classifiers namely Decision Stump, C4.5, Naive Baye's Tree, Random Forest, Random Tree and Representative Tree model to perform the detection of anomalous network pattern has been introduced.