Intellectual intrusion detection system can merely be build if there is accessibility to an effectual data set. A high dimensional quality dataset that imitates the real time traffic facilitates training and testing an intrusion detection system. Since it is complex to scrutinize and extort knowledge from high-dimensional data, it is identified that feature selection is a preprocessing phase during attack defense. It increases the classification accuracy and reduces computational complexity by extracting important features from original data. Optimization schemes can be utilized on the dataset for selecting the features to find the appropriate subspace of features while preserving ample accuracy rate characterized by the inventive feature set. This paper aims at implementing the hybrid algorithm, ABC-LVQ. The bio-inspired algorithm, Artificial Bee Colony (ABC) is adapted to lessen the amount of features to build a dataset on which a supervised classification algorithm, Linear Vector Quantization (LVQ) is applied, thus achieving highest classification accuracy compared to k-NN and LVQ. The NSL-KDD dataset is scrutinized to learn the efficiency of the proposed algorithm in identifying the abnormalities in traffic samples within a specific network.
CANet: An Unsupervised Intrusion Detection System for High Dimensional CAN Bus Data