Abstract
The Internet of Things (IoT) presents unprecedented opportunities for connectivity and efficiency across various sectors. However, its decentralized nature and the vast number of interconnected devices also pose significant security challenges, making IoT networks vulnerable to cyber-attacks. In this paper, we propose a predictive defense algorithm leveraging Differential Privacy (DP) techniques to enhance IoT security. By analyzing historical attack data while preserving the privacy of sensitive information, our algorithm predicts potential cyber threats and proactively mitigates them before they manifest. We present the theoretical framework of the DP algorithm, its implementation in IoT environments, and evaluate its effectiveness through simulations and real-world experiments. Our results demonstrate the algorithm’s ability to accurately predict and prevent a wide range of attacks, thus bolstering the resilience of IoT ecosystems against evolving cyber threats.
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