Earticle

초록

영어

In this paper, a machine learning-based technique is presented for detecting compromised IoT devices in edge computing networks. The model profiles device behavior using parameters such as CPU usage, network traffic, and power data, detecting anomalies that suggest an attack may be in progress. The lightweight framework can achieve high detection accuracy at low computational cost and is capable of processing in realtime.

목차

Abstract
I. INTRODUCTION
II. LITERATURE REVIEW
III. METHODOLOGY
IV. Results
V. CONCLUSION
References

저자

• Sundus Munir [ Department of Criminology Lahore Garrison University Lahore, Pakistan ]
• Maria Tariq [ Department of Computer Science Lahore Garrison University Lahore, Pakistan ]
• Khushbu Khalid Butt [ Department of Information Technology Lahore Garrison University Lahore, Pakistan ]
• Roshaan Fatima [ School of Computing, Horizon University College, Ajman, UAE ]
• Hussain Dawood [ School of Computing, Horizon University College, Ajman, UAE ]
• Muhammad Adnan Khan [ School of Computing, Horizon University College, Ajman, UAE ] Corresponding Author

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