An Artificial Intelligence-Based Intrusion Detection System using Optimization and Deep Learning

Abstract

The Intrusion Detection System (IDS) was developed to resolve the malicious activities on the networking. However, the existing IDS models face issues in achieving high accuracy, and it is prone to detection error. Hence, to address these challenges, an innovative Vulture-based Deep Belief System (VbDBNS) for enhancing the performance of detecting intrusion activity by monitoring their behaviour and features. This study utilized the NSL-KDD database for training and validating the system. Henceforth, pre-processing is employed to standardize the dataset using Min-Max normalization, and 1-N encoding, which transforms the categorical data variables. Moreover, feature extraction was utilized to capture the most significant 42 attributes from the pre-processed database. Further, a classification module was designed using VbDBN to categorize normal and malicious data in which the fitness of vulture was updated, which enables it to monitor and detect the intrusion continuously. The experimental results of the study illustrate that the designed methodology achieved better outcomes than the conventional techniques in terms of recall, accuracy, precision, F1-score, etc.