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Abstract <p>Cybersecurity is the practice of protecting computer systems, networks, programs, and data from digital attacks, damage, or unauthorized access. Cyber security technologies and solutions may not always be compatible with existing hardware and software, which could lead to various security risks. The development of artificial intelligence has led to significant improvements and notable outcomes in identifying cyberattacks. This article reviews various artificial intelligence approaches, including different machine learning, deep learning, and reinforcement learning algorithms, in cyber-attack detection. Various cryptography methods, which are utilized for security enhancement, like symmetric, asymmetric, and homomorphic cryptography, and various authentication schemes for data security, such as multifactor, digital signature, and biometric authentication, are also studied. Several parameters are taken for analysing the performance of those algorithms, such as accuracy, positive predictive value, hit rate, F1 score, error, encryption time, decryption time, and processing time, which have values of 96.24, 96.10, 95.80, 95.95, and 21.33%, 96 and 85 s. According to this review, deep learning-based DNN algorithms outperform traditional methods in cyber-attack detection. For security enhancement, the DES algorithm yields better outcomes. Biometric authentication offers enhanced security and convenience for user verification compared to traditional authentication methods.</p>

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Review Based Cyber Security and Privacy for IoT Network Using AI-Based Approaches and Cryptography Algorithm with Various Authentication Scheme

  • 期刊论文

  • 发表日期：

    2025年10月1日

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  • M. S. Chethan,
  • Dr. Channakrishnaraju

摘要

Abstract

Cybersecurity is the practice of protecting computer systems, networks, programs, and data from digital attacks, damage, or unauthorized access. Cyber security technologies and solutions may not always be compatible with existing hardware and software, which could lead to various security risks. The development of artificial intelligence has led to significant improvements and notable outcomes in identifying cyberattacks. This article reviews various artificial intelligence approaches, including different machine learning, deep learning, and reinforcement learning algorithms, in cyber-attack detection. Various cryptography methods, which are utilized for security enhancement, like symmetric, asymmetric, and homomorphic cryptography, and various authentication schemes for data security, such as multifactor, digital signature, and biometric authentication, are also studied. Several parameters are taken for analysing the performance of those algorithms, such as accuracy, positive predictive value, hit rate, F1 score, error, encryption time, decryption time, and processing time, which have values of 96.24, 96.10, 95.80, 95.95, and 21.33%, 96 and 85 s. According to this review, deep learning-based DNN algorithms outperform traditional methods in cyber-attack detection. For security enhancement, the DES algorithm yields better outcomes. Biometric authentication offers enhanced security and convenience for user verification compared to traditional authentication methods.