Feature selection techniques for intrusion detection using non-bio-inspired and bio-inspired optimization algorithms

As Internet access widens, IDS (Intrusion Detection System) is becoming a very important component of network security to prevent unauthorized use and misuse of data. An IDS routinely handles massive amounts of data traffc that contain redundant and irrelevant features, which impact the performance of the IDS negatively. Feature selection methods play an important role in eliminating unrelated and redundant features in IDS. Statistical analysis, neural networks, machine learning, data mining techniques, and support vector machine models are employed in some such methods. Good feature selection leads to better classification accuracy. Recently, bio-inspired optimization algorithms have been used for feature selection. This work provides a survey of feature selection techniques for IDS, including bio-inspired algorithms.