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Addressing IoT Security Challenges through AI Solutions

by Md Shihab Uddin
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 45
Year of Publication: 2024
Authors: Md Shihab Uddin
10.5120/ijca2024924107

Md Shihab Uddin . Addressing IoT Security Challenges through AI Solutions. International Journal of Computer Applications. 186, 45 ( Oct 2024), 50-55. DOI=10.5120/ijca2024924107

@article{ 10.5120/ijca2024924107,
author = { Md Shihab Uddin },
title = { Addressing IoT Security Challenges through AI Solutions },
journal = { International Journal of Computer Applications },
issue_date = { Oct 2024 },
volume = { 186 },
number = { 45 },
month = { Oct },
year = { 2024 },
issn = { 0975-8887 },
pages = { 50-55 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number45/addressing-iot-security-challenges-through-ai-solutions/ },
doi = { 10.5120/ijca2024924107 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-10-26T00:55:48.968717+05:30
%A Md Shihab Uddin
%T Addressing IoT Security Challenges through AI Solutions
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 45
%P 50-55
%D 2024
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The Internet of Things (IoT) is a widely recognized technology that profoundly influences various sectors, including connectivity, work, healthcare, and the economy. IoT holds the potential to enhance daily life across different environments, from smart cities to educational settings, by automating processes, boosting efficiency, and reducing stress. However, the rise of cyberattacks and threats poses significant challenges to the security of intelligent IoT applications. Traditional methods for securing IoT are becoming increasingly ineffective due to emerging threats and vulnerabilities. To maintain robust security protocols, future IoT systems will require the integration of AI-powered machine learning and deep learning techniques. Leveraging the capabilities of artificial intelligence, particularly through machine learning and deep learning, is essential for equipping next-generation IoT systems with dynamic and adaptive security mechanisms. This paper explores IoT security intelligence from various perspectives, proposing an innovative approach that utilizes machine learning and deep learning to extract insights from raw data, thereby protecting IoT devices against a wide range of cyberattacks. It also discusses how these technologies can be employed to detect attack patterns in unstructured data and enhance the security of IoT devices.

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Index Terms

Computer Science
Information Sciences

Keywords

Internet of things; cyberattacks; anomalies; deep learning; machine learning; security; data security; network security

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