DFA: Dynamic Frame Alteration For Video Manipulation Attack in IoT Environments

The advent of Internet of Things (IoT) technology has revolutionized disaster management, providing real-time monitoring and enhanced response capabilities for critical situations like floods. Despite these advances, IoT systems are increasingly vulnerable to cyber-attacks, particularly data manipulation attacks that target video feeds. This paper presents a novel attack technique named Dynamic Frame Alternation (DFA) aimed at evading standard detection algorithms. Unlike traditional attacks like replay, frame injection, and video stream hijacking that modify frames in a linear or bulk approach, DFA strategically alters frames based on real-time changes in video attributes, such as color consistency and object presence. Leveraging metrics like the Structural Similarity Index Measure (SSIM) to detect optimal moments for frame manipulation, DFA enhances attack stealth by maintaining low detectability and resource usage. Experimental results, obtained from implementations on an embedded board platform, demonstrate that DFA consistently achieves lower detection rates when compared against traditional attacks while applying popular detection algorithms.