Author : Khushnaseeb Roshan
Affiliation : Aligarh Muslim Univesity
Country : India
Category : Soft Computing
Volume, Issue, Month, Year : 13, 6, November, 2021
Abstract :
Machine learning (ML) and Deep Learning (DL) methods are being adopted rapidly, especially in computer network security, such as fraud detection, network anomaly detection, intrusion detection, and much more. However, the lack of transparency of ML and DL based models is a major obstacle to their implementation and criticized due to its black-box nature, even with such tremendous results. Explainable Artificial Intelligence (XAI) is a promising area that can improve the trustworthiness of these models by giving explanations and interpreting its output. If the internal working of the ML and DL based models is understandable, then it can further help to improve its performance. The objective of this paper is to show that how XAI can be used to interpret the results of the DL model, the autoencoder in this case. And, based on the interpretation, we improved its performance for computer network anomaly detection. The kernel SHAP method,
Keyword : Network Anomaly Detection, Network Security, Autoencoder, Shapley Additive Explanation, Explainable AI (XAI), Machine Learning
For More Details:https://allconferencecfpalerts.com/cfp/view-paper.php?eno=5235
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