Sniffer: A Machine Learning Approach for DoS Attack Localization in NoC-Based SoCs

Flooding-based Denial-of-service (DoS) attacks have been prevalent in Network-on-Chip (NoC) architectures, due to its shared nature and open access to all the on-chip modules. A Malicious Intellectual Property (MIP) within a System-on-Chip (SoC) creates such an attack by flooding the NoC with useles...

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Bibliographic Details
Published in:IEEE journal on emerging and selected topics in circuits and systems 2021-06, Vol.11 (2), p.278-291
Main Authors: Sinha, Mitali, Gupta, Setu, Rout, Sidhartha Sankar, Deb, Sujay
Format: Article
Language:English
Subjects:
attack localization
Bandwidth
Degradation
Denial of service attacks
Denial-of-service
Denial-of-service attack
Flooding
Flooding attack
Localization
Location awareness
Machine learning
network-on-chip
Performance degradation
Security
System on chip
Voltage-controlled oscillators
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Summary:Flooding-based Denial-of-service (DoS) attacks have been prevalent in Network-on-Chip (NoC) architectures, due to its shared nature and open access to all the on-chip modules. A Malicious Intellectual Property (MIP) within a System-on-Chip (SoC) creates such an attack by flooding the NoC with useless packets resulting in significant bandwidth reduction. Finding the location of an MIP is crucial to restore regular network operations and curtail system performance degradation. In this work, we propose Sniffer, an efficient MIP localization framework which employs a low-overhead machine learning approach to accurately trace the attack path and take a collective decision to locate the MIPs. Experimental results show that Sniffer is able to provide high accuracy for MIP localization without incurring significant overheads.
ISSN:2156-3357
2156-3365
DOI:10.1109/JETCAS.2021.3083289