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Deep learning techniques have been widely adopted for cyber defence applications such as malware detection and anomaly detection. The ever-changing nature of cyber threats has made cyber defence a constantly evolving field. Smart manufacturing is critical to the broader thrust towards Industry 4.0 and 5.0. Developing advanced technologies in smart manufacturing requires enabling a paradigm shift in manufacturing, while cyber-attacks significantly threaten smart manufacturing. For example, a cyber attack (e.g., backdoor) occurs during the model’s training process. Cyber attack affects the models and impacts the resultant output to be misled. Therefore, this paper proposes a novel and comprehensive framework for smart cyber defence in deep learning security. The framework collectively incorporates a threat model, data, and model security. The proposed framework encompasses multiple layers, including privacy and protection of data and models. In addition to statistical and intelligent model techniques for maintaining data privacy and confidentiality, the proposed framework covers the structural perspective, i.e., policies and procedures for securing data. The study then offers different methods to make the models robust against attacks coupled with a threat model. Along with the model security, the threat model helps defend the smart systems against attacks by identifying potential or actual vulnerabilities and putting countermeasures and control in place. Moreover, based on our analysis, the study provides a taxonomy of the backdoor attacks and defences. In addition, the study provides a qualitative comparison of the existing backdoor attacks and defences. Finally, the study highlights the future directions for backdoor defences and provides a possible way for further research. |
