ARTIFICIAL INTELLIGENCE (AI) AND MACHINE LEARNING (ML) FOR REAL-TIME IOT TRAFFIC ANALYSIS WITH CYBERSECURITY



Authors

  • Smita Vempati, Dr. Nalini N

DOI:

https://doi.org/10.15282/jmes.17.1.2023.10.0759


Keywords:

IoT Security, Machine Learning Algorithms, Anomaly Detection, Privacy-Preserving AI and Regulations and Compliance


Abstract

The integration of Artificial Intelligence (AI) and Machine Learning (ML) into the Internet of Things (IoT) has ushered in a new era of real-time traffic analysis and cybersecurity. This article provides a comprehensive exploration of the role of AI and ML in enhancing IoT cybersecurity, with a focus on real-time traffic analysis. The IoT landscape has witnessed explosive growth, with billions of interconnected devices generating massive volumes of data. While this interconnectedness offers unprecedented opportunities, it also exposes IoT networks to a myriad of cybersecurity threats. AI and ML emerge as critical tools to proactively identify and mitigate these threats. The article delves into the historical development of IoT, the evolution of cybersecurity threats, and the parallel progress of AI and ML technologies. It elucidates how AI and ML are seamlessly integrated into IoT environments to bolster cybersecurity measures. Real-time traffic analysis, a core component of this integration, is explored in depth, emphasizing its significance in identifying anomalies and potential threats. Technological aspects are meticulously examined, including AI/ML techniques such as deep learning, neural networks, and reinforcement learning, with real-world case studies illustrating their practical applications. Challenges unique to this domain, including data privacy and model accuracy, are analyzed alongside solutions and best practices. The article also presents compelling statistics that underscore the growth of IoT, the surge in cybersecurity threats, and successful AI/ML implementations. These statistics are discussed in the context of the broader topic, providing insights into the current landscape. Real-world case studies across sectors such as smart cities, healthcare, and industrial IoT highlight successful implementations of AI/ML for traffic analysis, offering valuable lessons and insights. Ethical considerations surrounding privacy, bias, transparency, and the delicate balance between security and individual rights are addressed in detail. The future outlook of AI/ML in enhancing IoT cybersecurity is optimistic, with predictions of increased autonomy, advanced anomaly detection, and privacy-preserving AI. However, it acknowledges emerging threats and emphasizes the role of regulations and ethical guidelines in responsible AI/ML deployment.



Published

2024-03-11

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