Lateral Movement

Lateral movement research encompasses the study of sideways motion across diverse domains, from vehicle dynamics and robotics to cybersecurity and neuroscience. Current efforts focus on developing accurate models of lateral movement, employing techniques like stochastic processes, deep learning (e.g., convolutional and recurrent neural networks), and model predictive control, to improve prediction, control, and anomaly detection. These advancements have significant implications for autonomous systems (e.g., safer self-driving cars, improved underwater robot navigation), cybersecurity (e.g., enhanced threat detection), and a deeper understanding of biological systems (e.g., insect collision avoidance). Ultimately, improved modeling and detection of lateral movement enhances safety, efficiency, and situational awareness across various fields.