The field of cybersecurity is moving towards the development of more sophisticated deception strategies to protect against physical reverse engineering attacks and adversarial threats. Researchers are exploring the application of machine learning and cyber deception principles to enhance the security of integrated circuits and protect critical systems from evasive malware and ransomware attacks. Additionally, there is a growing focus on improving the robustness of AI-based malware detection systems against adversarial examples and temporal data drift. Noteworthy papers in this area include: Designing with Deception, which presents a novel ML-driven methodology for IC camouflage, and Certifiably robust malware detectors by design, which proposes a new model architecture for robust malware detection. Contrastive ECOC also demonstrates superior robustness to adversarial attacks compared to traditional methods.