Sniffer: A Machine Learning Approach for DoS Attack Localization in NoC-Based SoCs
| Autor: | Sidhartha Sankar Rout, Setu Gupta, Mitali Sinha, Sujay Deb |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
business.industry
Network packet Computer science 020208 electrical & electronic engineering Bandwidth (signal processing) Location awareness Denial-of-service attack 02 engineering and technology computer.software_genre Machine learning 020202 computer hardware & architecture Flooding (computer networking) Reduction (complexity) Path (graph theory) 0202 electrical engineering electronic engineering information engineering Artificial intelligence Electrical and Electronic Engineering business computer TRACE (psycholinguistics) |
| Zdroj: | IEEE Journal on Emerging and Selected Topics in Circuits and Systems. 11:278-291 |
| ISSN: | 2156-3365 2156-3357 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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