Neural networks and the bias/variance dilemma
Neural Computation
Training with noise is equivalent to Tikhonov regularization
Neural Computation
A large scale distributed intrusion detection framework based on attack strategy analysis
Computer Networks: The International Journal of Computer and Telecommunications Networking
Parallel data mining for association rules on shared memory systems
Knowledge and Information Systems
Combining Classifiers with Meta Decision Trees
Machine Learning
Parallel Implementation of Decision Tree Learning Algorithms
EPIA '01 Proceedings of the10th Portuguese Conference on Artificial Intelligence on Progress in Artificial Intelligence, Knowledge Extraction, Multi-agent Systems, Logic Programming and Constraint Solving
Clustering intrusion detection alarms to support root cause analysis
ACM Transactions on Information and System Security (TISSEC)
New unsupervised clustering algorithm for large datasets
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
ACM Computing Surveys (CSUR)
Multi-scaling sampling: an adaptive sampling method for discovering approximate association rules
Journal of Computer Science and Technology
Learning DFA representations of HTTP for protecting web applications
Computer Networks: The International Journal of Computer and Telecommunications Networking
Journal of Network and Computer Applications - Special issue: Network and information security: A computational intelligence approach
A new intrusion detection system using support vector machines and hierarchical clustering
The VLDB Journal — The International Journal on Very Large Data Bases
A scalable decision tree system and its application in pattern recognition and intrusion detection
Decision Support Systems
GP ensemble for distributed intrusion detection systems
ICAPR'05 Proceedings of the Third international conference on Advances in Pattern Recognition - Volume Part I
Feature selection algorithm for data with both nominal and continuous features
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Best subset feature selection for massive mixed-type problems
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
An alert data mining framework for network-based intrusion detection system
WISA'05 Proceedings of the 6th international conference on Information Security Applications
Network intrusion detection using genetic algorithm to find best DNA signature
WSEAS TRANSACTIONS on SYSTEMS
Improving the performance of neural networks with random forest in detecting network intrusions
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II
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One of the most challenging problems in anomaly detection is to develop scalable algorithms which are capable of dealing with large audit data, network traffic data, or alter data. In this paper a distributed neural network based on Hebb rule is presented to improve the speed and scalability of inductive learning. The speed is improved by randomly splitting a large data set into disjoint subsets and each subset data is presented to an independent neural network, these networks can be trained in distributed and each one in parallel. The analysis of completeness and risk bounds of competitive Hebb learning proof that the distributed Hebb neural network can avoid the accuracy being degraded as compared to running a single algorithm with the entire data. The experiments are performed on the KDD'99 Data set, which is a standard intrusion detection benchmark. Comparisons with other approaches on the same benchmark demonstrate the effectiveness and applicability of the proposed method.