The field of cybersecurity is rapidly evolving, with a growing focus on developing innovative solutions to detect and prevent complex threats. Recent research has highlighted the importance of explainable AI methods in identifying and diagnosing poisoning attacks in swarming systems and cyber-physical attacks in critical infrastructures. Moreover, the integration of chaos theory and manifold learning has shown promising results in detecting backdoor attacks via data poisoning. Another notable trend is the application of machine learning and deep learning techniques to analyze traffic flow patterns and identify potential cyber threats. Noteworthy papers in this area include the introduction of a novel metric, Precision Matrix Dependency Score, for detecting poisoned samples and the development of an Event-Triggered GAT-LSTM framework for attack detection in HVAC systems. Additionally, researchers have explored the use of inverse reinforcement learning to model behavioral preferences of cyber adversaries and tensor networks for explainable anomaly detection. These advancements demonstrate the ongoing efforts to improve the accuracy and efficiency of threat detection systems, ultimately enhancing the security and resilience of critical systems.