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Securing Metaverse Platforms Using Machine Learning-Based Intrusion Detection Systems
Abstract
The increasing number of cyber threats requires more sophisticated and intelligent security solutions than the conventional signature-based Intrusion Detection Systems (IDS). Against the background of growing big data, high-speed networks, and dynamically changing attack vectors, Machine Learning (ML) has become a revolutionary technology to augment IDS by allowing systems to learn from past experiences, recognize patterns, and detect previously unseen attacks in real-time. This chapter discusses the application of Machine Learning methods to the development and design of contemporary IDS. When it comes to intrusion detection, ML algorithms such as decision trees, support vector machines, neural networks, k-nearest neighbors, and ensemble methods are really useful and help to identify known and unknown threats. The chapter on intrusion detection takes a look at the different types of learning, supervised, unsupervised, and semi-supervised, and in terms of datasets like NSL-KDD.
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