IoT Device Authentication Using Adversarial Machine Learning

Anshul Goel; Ashwin Sharma; Deepak Kejriwal

IoT Device Authentication Using Adversarial Machine Learning

Author(s)	Anshul Goel, Ashwin Sharma, Deepak Kejriwal
Country	India
Abstract	The security of IoT networks depends on correctly authenticating devices to allow safe communication and stop unauthorized devices. The enemy of machine learning can break past security measures to enter unauthorized systems. Raising security levels demands the creation of authentication tools that work beyond manipulation from threatening entities specifically through body traits or user actions. This research looks into how incorporating advanced anti-attack methods in machine learning would improve system security from serious hacking threats. Our system creates an authentication tool made of adversarial machine learning methods to find abnormal device actions during verification steps. Our authentication system training processes immune defense by learning from both normal user and adversary actions. Our research tests multiple machine learning methods especially GANs and SVMs to understand their ability in detecting legitimate devices from security threats. Our early findings show that adversarial machine learning makes IoT network security better by better finding unauthorized users. This study explains the safety advantages of integrating adversarial machine learning with present authentication systems. These models show how to spot risks instantly through their flexibility for adjusting to new security dangers. Our study demonstrates that using adaptive computer learning strengthens both IoT device login security and total IoT security defense systems. This research helps develop better IoT security by showing how device authentication should be upgraded to combat new cyber threats.
Keywords	Iot, Device Authentication, Secure Communication, Malicious Devices, Adversarial Machine Learning, Unauthorized Access, Advanced Authentication, Biometric Authentication, Behavioral Authentication, Security, Authentication Schemes, Anomaly Detection, Generative Adversarial Networks, Support Vector Machines, Resilience, Accuracy, Threat Detection, Iot Ecosystems, Data Integrity, Privacy, Network Security, Model Training, Adversarial Manipulation, Real-Time Detection, Machine Learning Techniques, Security Posture, Connected Devices, Vulnerability, Threat Landscape, Iot Systems, Resilience
Field	Engineering
Published In	Volume 15, Issue 2, July-December 2024
Published On	2024-09-11
Cite This	IoT Device Authentication Using Adversarial Machine Learning - Anshul Goel, Ashwin Sharma, Deepak Kejriwal - IJAIDR Volume 15, Issue 2, July-December 2024.

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IoT Device Authentication Using Adversarial Machine Learning

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