Network intrusion detection using genetic algorithm to find best DNA signature

Abstract

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.