The field of cyber-physical system security and estimation is rapidly evolving, with a focus on developing innovative methods to detect and prevent attacks on critical infrastructure. Recent research has emphasized the importance of integrating machine learning algorithms with traditional techniques, such as Kalman filters, to improve the accuracy and robustness of intrusion detection systems. Additionally, there is a growing interest in developing adaptive estimation approaches that can overcome the challenges of uncertain system dynamics and noisy measurements. Notable papers in this area include those that propose novel frameworks for attack detection in vehicles, iteratively saturated Kalman filtering for outlier robustness, and adaptive ensemble sparse learning for reduced-order modeling of lithium-ion batteries. Some noteworthy papers are: An Adaptive Estimation Approach based on Fisher Information to Overcome the Challenges of LFP Battery SOC Estimation, which proposes an adaptive fisher information fusion strategy for accurate state of charge estimation. Data-Driven Intrusion Detection in Vehicles, which integrates unscented Kalman filter with machine learning for effective attack detection in vehicles.