Securing IoT Ecosystems: Machine Learning Approaches for Intrusion Detection and Anomaly Detection
Main Article Content
Abstract
The proliferation of Internet of Things (IoT) devices presents unique security challenges, including increased vulnerabilities and potential attack surfaces. This paper investigates the application of machine learning for intrusion detection and anomaly detection within IoT ecosystems. By analyzing device communication patterns and behavioral metrics, our model identifies deviations that may indicate security breaches or malicious activities. Experimental results show a significant improvement in detection rates compared to traditional security measures, highlighting the importance of adaptive security solutions in safeguarding IoT networks from evolving threats.
Downloads
Article Details
How to Cite
References
Balantrapu, S. S. (2022). Evaluating AI-Enhanced Cybersecurity Solutions Versus Traditional Methods: A Comparative Study. International Journal of Sustainable Development Through AI, ML and IoT, 1(1), 1-15.
Balantrapu, S. S. (2022). Ethical Considerations in AI-Powered Cybersecurity. International Machine learning journal and Computer Engineering, 5(5).
Balantrapu, S. S. (2021). The Impact of Machine Learning on Incident Response Strategies. International Journal of Management Education for Sustainable Development, 4(4), 1-17.
Balantrapu, S. S. (2019). Adversarial Machine Learning: Security Threats and Mitigations. International Journal of Sustainable Development in Computing Science, 1(3), 1-18.
Pillai, S. E. V. S., Polimetla, K., Avacharmal, R., & Perumal, A. P. (2022). Mental health in the tech industry: Insights from surveys and NLP analysis. JOURNAL OF RECENT TRENDS IN COMPUTER SCIENCE AND ENGINEERING (JRTCSE), 10(2), 22-33.
Deekshith, A. (2020). AI-Enhanced Data Science: Techniques for Improved Data Visualization and Interpretation. International Journal of Creative Research In Computer Technology and Design, 2(2).
Deekshith, A. (2019). Integrating AI and Data Engineering: Building Robust Pipelines for Real-Time Data Analytics. International Journal of Sustainable Development in Computing Science, 1(3), 1-35.
Boppiniti, S. T. (2022). Exploring the Synergy of AI, ML, and Data Analytics in Enhancing Customer Experience and Personalization. International Machine learning journal and Computer Engineering, 5(5).
Boppiniti, S. T. (2021). Real-Time Data Analytics with AI: Leveraging Stream Processing for Dynamic Decision Support. International Journal of Management Education for Sustainable Development, 4(4).
Boppiniti, S. T. (2019). Machine Learning for Predictive Analytics: Enhancing Data-Driven Decision-Making Across Industries. International Journal of Sustainable Development in Computing Science, 1(3).