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<p>The security and efficiency of Internet of Things (IoT) Closed-Circuit Television (CCTV) devices become crucial with the increased popularity of CCTV surveillance systems. Security is provided by traditional encryption techniques but with the large computational overhead that makes it infeasible and impractical for resource constrained IoT devices. Therefore, to secure critical video frames and ensure the authenticity of the video data, a selective encryption algorithm has been proposed in this study that uses an Advanced Encryption Standard (AES) in Galois Counter Mode (GCM) method. For optimizing storage, H.264 compression is integrated with the encryption without compromising the security. The method selectively encrypts every nth frame with authentication, and reducing computational overhead without significantly compromising the security. Experiments were conducted on multiple CCTV videos with varying resolutions, bitrates, and lighting conditions to validate the effectiveness of the proposed approach. Standard security metrics such as entropy, correlation coefficient, Number of Pixels Change Rate (NPCR), and Unified Average Changing Intensity (UACI), along with encoding/decoding frame rates, are evaluated to verify real-time feasibility. Experimental results demonstrate that the proposed scheme achieves approximately 60% in file size while maintaining a low encryption overhead of 1.1%, making it suitable for real-time CCTV surveillance applications. The proposed approach achieves high randomness, low correlation, and strong differential attack resistance. The suggested selective encryption technique maintains video quality, improved security characteristics, and drastically lowers computational costs as compared to the traditional full encryption techniques. Ensuring security and efficiency in video surveillance systems makes it a practical and effective solution.</p>

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Development of an authenticated cryptography algorithm to enhance the security of Internet of Things closed-circuit television devices

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年8月2日

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  • Kawalpreet Kaur,
  • Amanpreet Kaur,
  • Simi Kamini Bajaj,
  • Malvinder Singh Bali

摘要

The security and efficiency of Internet of Things (IoT) Closed-Circuit Television (CCTV) devices become crucial with the increased popularity of CCTV surveillance systems. Security is provided by traditional encryption techniques but with the large computational overhead that makes it infeasible and impractical for resource constrained IoT devices. Therefore, to secure critical video frames and ensure the authenticity of the video data, a selective encryption algorithm has been proposed in this study that uses an Advanced Encryption Standard (AES) in Galois Counter Mode (GCM) method. For optimizing storage, H.264 compression is integrated with the encryption without compromising the security. The method selectively encrypts every nth frame with authentication, and reducing computational overhead without significantly compromising the security. Experiments were conducted on multiple CCTV videos with varying resolutions, bitrates, and lighting conditions to validate the effectiveness of the proposed approach. Standard security metrics such as entropy, correlation coefficient, Number of Pixels Change Rate (NPCR), and Unified Average Changing Intensity (UACI), along with encoding/decoding frame rates, are evaluated to verify real-time feasibility. Experimental results demonstrate that the proposed scheme achieves approximately 60% in file size while maintaining a low encryption overhead of 1.1%, making it suitable for real-time CCTV surveillance applications. The proposed approach achieves high randomness, low correlation, and strong differential attack resistance. The suggested selective encryption technique maintains video quality, improved security characteristics, and drastically lowers computational costs as compared to the traditional full encryption techniques. Ensuring security and efficiency in video surveillance systems makes it a practical and effective solution.