Error reduction through learning multiple descriptions
Machine Learning
Neuro Fuzzy Systems: Sate-of-the-Art Modeling Techniques
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Connectionist Models of Neurons, Learning Processes and Artificial Intelligence-Part I
Agent-Based Hybrid Intelligent Systems
Agent-Based Hybrid Intelligent Systems
Review: The use of computational intelligence in intrusion detection systems: A review
Applied Soft Computing
Dialog-based payload aggregation for intrusion detection
Proceedings of the 17th ACM conference on Computer and communications security
Community epidemic detection using time-correlated anomalies
RAID'10 Proceedings of the 13th international conference on Recent advances in intrusion detection
A Link Analysis Extension of Correspondence Analysis for Mining Relational Databases
IEEE Transactions on Knowledge and Data Engineering
Intrusion detection using neural based hybrid classification methods
Computer Networks: The International Journal of Computer and Telecommunications Networking
Advances in communication networks for pervasive and ubiquitous applications
The Journal of Supercomputing
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Machine Learning as network attack detection is one of the popular methods researched. Signature based network attack detection is no longer convinced the efficiency in the diversified intrusions (Limmer and Dressler in 17th ACM Conference on Computer and Communication Security, 2010). Moreover, as the various Zero-day attacks, non notified attacks cannot be detected (Wu and Banzhaf in Appl Soft Comput 10(1):1---35, 2010). This paper suggests an effective update method of data set on Machine Learning to detect non notified attacks. In addition, this paper compares and verifies the effects of Machine Learning Detection with updated data set to the former methods.