Role of Machine and Deep Learning Algorithms in Secure Intrusion Detection Systems (IDS) for IOT & Smart Cities

Abstract

In this study the authors have examines various machine learning algorithms that could be used in IDS for making secure IoT and Smart Cities. The study examines various deep learning architectures of supervised, unsupervised, and semi-supervised learning methods to improve security and resource usage. Federated learning, edge computing, explainable AI, adversarial machine learning defense, and transfer learning are also explored for smart farming and IoT challenges. Machine learning has the potential to improve security and agricultural sustainability, but it must be researched and developed.