Leveraging Standards and Deep Learning Approaches to Secure Internet of Things (IoT) Devices from Cyber Attack

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This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2026 | Volume : 13 | 01 | Page :
    By

    Bhagyashri N Chaudhari,

  • Sayaram N Shingote,

  • Snehal B. Phatangare,

  • Santosh S.Kale,

  1. Assistant Professor, Computer Engineering Dept. AVCOE, Sangamner, Maharashtra, India
  2. Assistant Professor, Computer Engineering Dept. AVCOE, Sangamner, Maharashtra, India
  3. Assistant Professor, Computer Engineering Dept. AVCOE, Sangamner, Maharashtra, India
  4. Assistant Professor, Computer Engineering Dept. AVCOE, Sangamner, Maharashtra, India

Abstract

The widespread adoption of Internet of Things (IoT) devices between 2019 and 2024 has significantly grows in various sectors in Japan, including healthcare, manufacturing, and the development of smart cities. Although this growth offers many advantages, it also makes these devices more vulnerable to cyber threats. High-profile security breaches in Japan have sparked discussions about the requirement for enhanced security measures to protect the rapidly evolving IoT technologies. This study seeks to assess the level of security of IoT devices in Japan during this period, focusing on the integration of advanced standards and deep learning techniques. In addition, a survey of industry professionals is conducted to gauge their perspectives on the evolving security landscape, their preparedness for AI-based solutions, and the emerging trends in IoT security. Methodology: The research utilizes both survey and technical address to detect the challenges associated with IoT security in Japan. Experts in IoT security and key influencers were consulted to evaluate the status of IoT devices from 2019 to 2024. This includes reviewing standards such as IEEE 802.15.11 for high-power wireless networks and examining the physical layer specifications for Wi-Fi networks. In addition, deep learning-based Intrusion Detection Systems (IDS), such as Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, are employed for network security analysis. This mechanism specifically analyzes IoT network data from sectors like healthcare and smart cities, examining how these technologies confronted emerging cyber risks during the defined period.

Keywords: IoT Security, Convolutional Neural Networks, Long Short-Term Memory, AI Driven Security, DDoS Attacks, Intrusion Detection Systems, Smart Cities

How to cite this article:
Bhagyashri N Chaudhari, Sayaram N Shingote, Snehal B. Phatangare, Santosh S.Kale. Leveraging Standards and Deep Learning Approaches to Secure Internet of Things (IoT) Devices from Cyber Attack. Journal of Artificial Intelligence Research & Advances. 2026; 13(01):-.
How to cite this URL:
Bhagyashri N Chaudhari, Sayaram N Shingote, Snehal B. Phatangare, Santosh S.Kale. Leveraging Standards and Deep Learning Approaches to Secure Internet of Things (IoT) Devices from Cyber Attack. Journal of Artificial Intelligence Research & Advances. 2026; 13(01):-. Available from: https://journals.stmjournals.com/joaira/article=2026/view=237210


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Ahead of Print Subscription Review Article
Volume 13
01
Received 25/03/2025
Accepted 21/07/2025
Published 19/02/2026
Publication Time 331 Days
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