A Novel Packet Hiding Approach for Preventing Jamming Attacks in Internet of Things Environment

Abstract

The rapid proliferation of Internet of Things (IoT) devices has significantly increased the reliance on wireless communication, thereby exposing IoT networks to a wide range of security threats, particularly selective and intelligent jamming attacks. Such attacks exploit vulnerabilities in existing IoT communication protocols by targeting critical packets, leading to severe degradation in network performance, reliability, and availability. This research aims to analyze the inherent weaknesses of current IoT communication mechanisms and proposes a novel packet hiding approach to mitigate jamming attacks effectively. The proposed methodology integrates adaptive packet transmission strategies with machine learning–based detection mechanisms to obscure packet characteristics such as timing, size, and transmission patterns, making them indistinguishable to adversarial jammers. The system dynamically adjusts transmission parameters based on real-time network conditions and detected threat levels, thereby enhancing resilience against both static and adaptive jamming attacks. Performance evaluation is conducted using simulation-based experiments, comparing the proposed approach with existing anti-jamming techniques in terms of packet delivery ratio, throughput, latency, energy efficiency, and attack detection accuracy. The expected outcomes demonstrate significant improvements in secure packet transmission, robustness against adversarial interference, and overall network reliability. In conclusion, this work provides a comprehensive and practical security framework for IoT environments, contributing to both academic research and real-world deployment by strengthening the resilience of critical wireless communication systems against jamming attacks.