年度112
等級
書名Conversational Artificial Intelligence-Ch20
部分章節An intrusion detection system, often known as IDS, is primarily used to gather and analyze data regarding security events that occur in computer systems and networks. Its subsequent purpose is to either prevent these events from happening or notify them to the administrator of the system. As a result of the increasing number of attacks carried out by attackers, the users’ level of mistrust on the Internet has increased. Attacks that cause denial of service are a major violation of security. This article presents a particle swarm optimization and AdaBoost-based intrusion detection system. In this system, chatbot receives network traffic as input, and features of input dataset are selected using particle swarm optimization algorithm. A classification model is trained and tested. AdaBoost, KNN, and naïve Bayes algorithm are used to classify and detect malware-related records. NSL KDD dataset is used in the experimental work. PSO-AdaBoost achieves the highest accuracy, precision, and recall for intrusion detection and classification. The output of a chatbot is a language that is either normal or benign.
出版社John Wiley & Sons, Inc.
全部作者Chen, Tzu-chia
出版日期2024-01-27
ISSN(ISBN)9781394200566
所屬計畫案Conversational Artificial Intelligence-Ch20
備註專書