Intrusion detection system using statistical query tree with hierarchical clustering approach

The internet has become a major part of everyone's life. When no proper protection is provided, intruders misuse the access provided by the internet, leading to an increased risk of sensitive data leakage. To have a trade-off between scalability and precision, this research introduces a novel two-stage screening framework for intrusion detection systems (IDS) to identify the attacks and their types. The first stage aims to identify suspicious internet protocol (IP) addresses based on the abrupt deviation from the normal activity pattern. The second screening stage aims to analyse the packets received from suspicious IP addresses by applying a recently developed single-phase statistical hierarchical clustering (SHiC) algorithm designed for clustering and outlier detection. The data packets are classified as outliers based on their higher statistic distance to the existing components or clusters identified. The complete IDS framework is developed and applied to two benchmark datasets and compared with the results produced by several outlier detection algorithms. The proposed framework is found to be consistently more accurate in detecting attacks.