Intrusion Detection with Black Hole Feature Selection

Network intrusion detection datasets are high-dimensional, containing many redundant and noisy features that degrade classifier performance. This paper applies Binary and Real-Coded variants of the Black Hole algorithm to select the most informative feature subsets, evaluated with a Random Forest classifier on three standard intrusion detection benchmarks — NSL-KDD, CIC-IDS2017, and the Aegean Wi-Fi dataset. The Real-Coded variant encodes feature relevance as continuous values rather than hard binary flags, offering finer-grained selection. Both variants outperform conventional feature selection methods, achieving higher detection accuracy with fewer features.

Recommended citation: Kulkarni, S., Ovhal, P., Valadi, J.K. (2023). Intrusion Detection with Black Hole Feature Selection. In: Bansal, J.C., Sharma, H., Chakravorty, A. (eds) Congress on Smart Computing Technologies. CSCT 2022. Smart Innovation, Systems and Technologies, vol 351. Springer, Singapore. https://doi.org/10.1007/978-981-99-2468-4_9 https://link.springer.com/chapter/10.1007/978-981-99-2468-4_9