GUARDIANS OF THE INTERNET OF THINGS: A MACHINE LEARNING APPROACH FOR VULNERABILITY DETECTION IN IOT NETWORKS
Author(s):
B.SHIVA, Dr.MB.BRAMARAMBIKA
Keywords:
Internet Of Things (Iot), Machine Learning Techniques, Security Challenges.
Abstract
The proliferation of Internet of Things (IoT) devices has revolutionized numerous domains, from smart homes to industrial automation. However, the rapid expansion of IoT ecosystems has also introduced unprecedented security challenges. Traditional security mechanisms struggle to keep pace with the dynamic and diverse nature of IoT networks, leaving them vulnerable to various threats. In this paper, we propose a novel machine learning approach termed Guardians of the Internet of Things (GoIoT) for detecting vulnerabilities in IoT networks. The GoIoT framework leverages machine learning algorithms to analyze network traffic patterns and identify potential security vulnerabilities. By extracting features from network packets and employing supervised learning techniques, GoIoT can discern normal traffic behavior from anomalous activities indicative of potential threats or vulnerabilities. Furthermore, the framework incorporates a feedback loop mechanism, continuously adapting its models to evolving network conditions and emerging attack vectors. In conclusion, Guardians of the Internet of Things represents a significant advancement in the realm of IoT security, offering a proactive and adaptive solution for vulnerability detection. By harnessing the power of machine learning, GoIoT empowers organizations and individuals to safeguard their IoT deployments effectively, ensuring the integrity, confidentiality, and availability of connected devices and services.
Article Details
Unique Paper ID: 165910

Publication Volume & Issue: Volume 11, Issue 1

Page(s): 1949 - 1960
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