A survey about deep learning and federated Learning in cyberse-curity

Abstract

Advances in Artificial Intelligence (AI) technology have led to the strengthening of traditional systems' cybersecurity capabilities in a variety of applications. However, these embedded machine learning models have exposed these systems to a new set of vulnerabilities known as AI assaults. These systems are now attractive targets for cyberattacks, jeopardizing the security and safety of bigger systems that include them. As a result, DL approaches are critical to transitioning network and system protection from providing safe communication between systems to intelligence systems in security. Federated learning (FL) is a new kind of AI based on heterogeneous datasets and decentralized training. FL is a unique research topic that is currently in its early phases. It has not yet gained wide acceptance in the community, owing mostly to privacy and security considerations. In this research, we first shed light on its privacy and security risks that must be discovered, analyzed, and recorded. FL is favored in scenarios where privacy and security are paramount is-sues. An extensive understanding of risk factors allows an FL adopter and implementer to construct a safe environment successfully while giving researchers a clear perspective of possible study domains. The survey in this paper intends to include an analysis of cybersecurity and DL approaches and modern advances to improve enhanced protection methods. It proposes a complete examination of FL's security and privacy issues to assist in bridging the gap between the current level of federated AI and a future in which broad adoption is achievable. We also propose a range of cybersecurity datasets and the most recently used rating standards.