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<p>Internet of Things (IoT) has significantly influenced the development of smart gadgets and electronic devices that aim to improve user comfort and quality of life. The IoT application are widely adopted across many smart devices and various domains. In the modern smart environment, the Message Queuing Telemetry Transport (MQTT) protocol is regarded as one of the leading and widely acknowledged communication protocols in the IoT domain, frequently utilized for transmitting and managing data between connected devices. It plays a significant role in a wide range of smart applications. Nevertheless, it does not include built-in security features, rendering it susceptible to numerous threats, including man-in-the-middle (MitM) attacks, buffer overflow exploits, pre-shared key vulnerabilities, brute-force authentication attempts, malformed data injections, distributed denial-of-service (DDoS) attacks, and MQTT publish flooding attacks. The common way to assure the reliability of the transmitted data while using MQTT is to adopt high-level security algorithms and treat system entities as trusted components. The adoption of these algorithms introduces higher computational overhead, which may prevent IoT devices from achieving strong security protection against emerging attacks. In this context, this paper proposes the network-aware Chaotic Swarm Evoked ASCON-based cryptographic technique to safeguard the data against MQTT-related attacks. The entire experimental setup was implemented in Python version 3.9, employing the Charm-Crypto framework for realizing the proposed scheme within an IoT-based environment. Extensive experimental results demonstrate the resilience of the recommended approach to various threats with a reduced computation time of 0.45&#xa0;s, and it outperforms existing security models. Theoretical and experimental findings validate the model’s effectiveness in strengthening privacy measures and demonstrating robust performance in countering various attacks in smart IoT systems.</p>

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A novel secured and reliable chaotic metaheuristic technique for data sharing in IoT fog environment

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月2日

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  • G. Prabhakar,
  • Meeravali Shaik

摘要

Internet of Things (IoT) has significantly influenced the development of smart gadgets and electronic devices that aim to improve user comfort and quality of life. The IoT application are widely adopted across many smart devices and various domains. In the modern smart environment, the Message Queuing Telemetry Transport (MQTT) protocol is regarded as one of the leading and widely acknowledged communication protocols in the IoT domain, frequently utilized for transmitting and managing data between connected devices. It plays a significant role in a wide range of smart applications. Nevertheless, it does not include built-in security features, rendering it susceptible to numerous threats, including man-in-the-middle (MitM) attacks, buffer overflow exploits, pre-shared key vulnerabilities, brute-force authentication attempts, malformed data injections, distributed denial-of-service (DDoS) attacks, and MQTT publish flooding attacks. The common way to assure the reliability of the transmitted data while using MQTT is to adopt high-level security algorithms and treat system entities as trusted components. The adoption of these algorithms introduces higher computational overhead, which may prevent IoT devices from achieving strong security protection against emerging attacks. In this context, this paper proposes the network-aware Chaotic Swarm Evoked ASCON-based cryptographic technique to safeguard the data against MQTT-related attacks. The entire experimental setup was implemented in Python version 3.9, employing the Charm-Crypto framework for realizing the proposed scheme within an IoT-based environment. Extensive experimental results demonstrate the resilience of the recommended approach to various threats with a reduced computation time of 0.45 s, and it outperforms existing security models. Theoretical and experimental findings validate the model’s effectiveness in strengthening privacy measures and demonstrating robust performance in countering various attacks in smart IoT systems.