An Improved Explainable Artificial Intelligence for Intrusion Detection System in Cloud Environment
Keywords:
NIDS, Dataset, Deep Learning (DL), XAI, Intrusion DetectionAbstract
Automated Anomaly Detection (AD) systems that can spot suspicious activities are perfect for cloud computing applications. Previous research has not solved the open challenge of irregularity discovery in cloud computing. The usual operations of a cloud server must be characterised, malicious anomalies must be distinguished from benign ones, and false alarms must be avoided at all costs to prevent alert fatigue. Various industrial applications showcase the growing potential of the cloud infrastructure. The Network Intrusion Detection System (NIDS) is seen as a crucial part of data transmission security, which seems to be in jeopardy. Upcoming advances in intellectual IDS have made use of Explainable Artificial Intelligence (XAI) methods. The bulk of IDS is based on either supervised or uncontrolled XAI methods. The lowered model's capacity to identify attack patterns is diminished due to the fact that NIDS relies on supervised learning using labelled data. Also, the unsupervised model can't deliver a reasonable result. Later, to enhance the performance of unsupervised learning, a high-quality feature set will be selected using an effective feature selection technique. Finally, a Machine Learning (ML) classifier based on XAI will be used for classification. The optimal generalizability capacity for data training is shown by this strategy. Analyses are also performed on the unlabeled data. In this step, we filter away the dataset's noisy and redundant samples. The anticipated technique outperforms other existing methods in terms of accuracy, according to the experimental data.
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