State Transition Analysis: A Rule-Based Intrusion Detection Approach
IEEE Transactions on Software Engineering
Detecting masquerades in intrusion detection based on unpopular commands
Information Processing Letters
Specification-based anomaly detection: a new approach for detecting network intrusions
Proceedings of the 9th ACM conference on Computer and communications security
Parzen-Window Network Intrusion Detectors
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
A new unsupervised anomaly detection framework for detecting network attacks in real-time
CANS'05 Proceedings of the 4th international conference on Cryptology and Network Security
A distributed hebb neural network for network anomaly detection
ISPA'07 Proceedings of the 5th international conference on Parallel and Distributed Processing and Applications
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Bioinformatics is part of computer science that joins between computer programming and molecular biology. DNA consists of long sequence of nucleotides which formulates the genome. Our method is to generate normal signature sequence and alignment threshold value from processing the system training data and encode observed network connection into corresponding DNA nucleotides sequence, then to align the signature sequence with observed sequence to find similarity degree value and decide whether the connection is attack or normal. Number of DNA sequences makes up each population, and then new generations are produced to select the Signature with best alignment value with normal network connection sequences. This paper ends up with accuracy value and threshold score for detecting the network anomalies that no known conditions exist for them to be discovered in addition for percentage of generating false positive and true negative alarms.