An improved bio-inspired based intrusion detection model for a cyberspace

Abstract Bio-inspired intrusion detection solutions provide better detection accuracy than conventional solutions in securing cyberspace. However, existing bio-inspired anomaly-based intrusion detection systems are still faced with challenges of high false-positive rates because the algorithms were tuned with unpredictable user-defined parameters, which led to premature convergence, exploration and exploitation discrepancies, algorithm complexity, and unrealistic results. In this paper, an intrusion detection system based on the foraging behavior of the social spider was developed. It employed signal transmission variables such as frequency of vibration to achieve a system that can evaluate real-life signals transmitted by computers and computing devices in the cyberspace to detect intrusion. This intrusion detection system was formulated using a social spider colony optimization model to generate a classifier that was tested using the standard NSL-KDD and live network traffic OAUnet datasets. The performance of the proposed intrusion detection system was evaluated by benchmarking it with existing classifiers using detection accuracy, sensitivity, and specificity as performance metrics. Results showed that the proposed model was more effective in terms of higher detection accuracy, sensitivity, specificity, and f-measure with a low false-positive rate. This showed that the spider model is a robust computational scheme that improves intrusion detection with a minimal false-positive rate in cyberspace.