There is an urgent need for globally competitive products with more variability, better and dependable quality, reduced cost, and shorter life cycles. The espousal of the IoT in industrial applications leverages communication between people, data analytics, and intelligent machines designed to fulfill emerging market demands while continuing to realize their business goals. An IDS is an indispensable cog widely used in IoT networks to recognize malevolent network activities. IDS models sense malevolent instances and create a healthful environment for business and API Security [17]. Even though DL-based IDSs perform better in identifying new cyber-attacks, they are frequently hampered by some restrictions, including higher false alarm rates, deprived reliability, ineffective against cutting-edge cyber-attacks, and lower prediction performance owing to class imbalance problems. Therefore, an efficient IDS model is inevitable in the IoT environment to handle these problems. This study proposes three IDS models by applying different DL and optimization algorithms for identifying and classifying security breaches while preserving the privacy of sensitive user data in IoT networks.
Mayank Hindka
Texas A&M University, United States of America
Internet of Things (IoT), Intrusion Detection System (IDS), Cyber Security, API Security, Privacy Preserving Technique
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| Published By :
ICTACT
Published In :
ICTACT Journal on Data Science and Machine Learning
( Volume: 5 , Issue: 3 , Pages: 636 - 640 )
Date of Publication :
June 2024
Page Views :
126
Full Text Views :
19
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