A Comprehensive Survey of Intrusion Detection Systems using Advanced Technologies

Abstract

Today, every IT business uses Cloud Computing since it's scalable and versatile. Its open and distributed nature makes security and privacy a big problem due to intruders. The Internet of Things (IoT) will impact many aspects of our lives due to its rapid development in household appliances, wearable technology, and intelligent sensors. IoT devices are connected, widespread, and low-powered. By 2020, there will be 50 billion Internet of Things (IoT) devices in use worldwide. There have been more IoT-based cyberattacks as a result of the growth of IoT devices, which now easily outweigh desktop PCs. To solve this challenge, new approaches must be developed for spotting assaults from hacked IoT devices. In this regard, machine learning and deep learning should be used as a detective control against IoT attacks. In addition to an introduction of intrusion detection methods, this paper analyses the technologies, protocols, and architecture of IoT networks and reviews the dangers of hacked IoT devices. This study examines methods for recognizing IoT cyberattacks using deep learning and machine learning. Various optimizer algorithms are discussed to improve the quality, efficiency and accuracy of the model.