research-article

Real-Time Anomaly Detection Framework for Many-Core Router through Machine-Learning Techniques

Authors:

Matthew French,

Tinoosh MohseninAuthors Info & Claims

ACM Journal on Emerging Technologies in Computing Systems (JETC), Volume 13, Issue 1

Article No.: 10, Pages 1 - 22

https://doi.org/10.1145/2827699

Published: 16 June 2016 Publication History

Abstract

In this article, we propose a real-time anomaly detection framework for an NoC-based many-core architecture. We assume that processing cores and memories are safe and anomaly is included through a communication medium (i.e., router). The article targets three different attacks, namely, traffic diversion, route looping, and core address spoofing attacks. The attacks are detected by using machine-learning techniques. Comprehensive analysis on machine-learning algorithms suggests that Support Vector Machine (SVM) and K-Nearest Neighbor (K-NN) have better attack detection efficiency. It has been observed that both algorithms have accuracy in the range of 94% to 97%. Additional hardware complexity analysis advocates SVM to be implemented on hardware. To test the framework, we implement a condition-based attack insertion module; attacks are performed intra- and intercluster. The proposed real-time anomaly detection framework is fully placed and routed on Xilinx Virtex-7 FPGA. Postplace and -route implementation results show that SVM has 12% to 2% area overhead and 3% to 1% power overhead for the quad-core and 16-core implementation, respectively. It is also observed that it takes 25% to 18% of the total execution time to detect an anomaly in transferred packets for quad-core and 16-core, respectively. The proposed framework achieves 65% reduction in area overhead and is 3 times faster compared to previous published work.

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Cited By

Pan ZMishra P(2024)TD-Zero: Automatic Golden-Free Hardware Trojan Detection Using Zero-Shot LearningIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2024.335488943:7(1998-2011)Online publication date: 22-Jan-2024
https://dl.acm.org/doi/10.1109/TCAD.2024.3354889
Dal Zotto AMoraes F(2024)A Machine Learning Approach for Traffic Anomaly Detection in NoC-based Manycores2024 37th SBC/SBMicro/IEEE Symposium on Integrated Circuits and Systems Design (SBCCI)10.1109/SBCCI62366.2024.10703991(1-5)Online publication date: 2-Sep-2024
https://doi.org/10.1109/SBCCI62366.2024.10703991
Ding RZhang YXia SWang ZDeng JChen X(2024)Detection of Hardware Attacks in Network on Chip Based on Machine Learning2024 IEEE 7th International Conference on Electronic Information and Communication Technology (ICEICT)10.1109/ICEICT61637.2024.10671248(440-445)Online publication date: 31-Jul-2024
https://doi.org/10.1109/ICEICT61637.2024.10671248
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Index Terms

Real-Time Anomaly Detection Framework for Many-Core Router through Machine-Learning Techniques
1. Hardware
  1. Communication hardware, interfaces and storage
2. Networks

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Published In

cover image ACM Journal on Emerging Technologies in Computing Systems

ACM Journal on Emerging Technologies in Computing Systems Volume 13, Issue 1

Special Issue on Secure and Trustworthy Computing

January 2017

208 pages

ISSN:1550-4832

EISSN:1550-4840

DOI:10.1145/2917757

Editor:
Yuan Xie
University of California, Santa Barbara, USA

Issue’s Table of Contents

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 16 June 2016

Accepted: 01 September 2015

Revised: 01 July 2015

Received: 01 December 2014

Published in JETC Volume 13, Issue 1

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Cited By

Pan ZMishra P(2024)TD-Zero: Automatic Golden-Free Hardware Trojan Detection Using Zero-Shot LearningIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2024.335488943:7(1998-2011)Online publication date: 22-Jan-2024
https://dl.acm.org/doi/10.1109/TCAD.2024.3354889
Dal Zotto AMoraes F(2024)A Machine Learning Approach for Traffic Anomaly Detection in NoC-based Manycores2024 37th SBC/SBMicro/IEEE Symposium on Integrated Circuits and Systems Design (SBCCI)10.1109/SBCCI62366.2024.10703991(1-5)Online publication date: 2-Sep-2024
https://doi.org/10.1109/SBCCI62366.2024.10703991
Ding RZhang YXia SWang ZDeng JChen X(2024)Detection of Hardware Attacks in Network on Chip Based on Machine Learning2024 IEEE 7th International Conference on Electronic Information and Communication Technology (ICEICT)10.1109/ICEICT61637.2024.10671248(440-445)Online publication date: 31-Jul-2024
https://doi.org/10.1109/ICEICT61637.2024.10671248
Prasanna DTripathi S(2024)A Review on Tongue Based Assistive Technology, Devices and FPGA Processors Using Machine Learning ModuleWireless Personal Communications: An International Journal10.1007/s11277-024-10897-8134:1(151-170)Online publication date: 27-Feb-2024
https://dl.acm.org/doi/10.1007/s11277-024-10897-8
Otamendi JZubizarreta APortillo E(2023)Machine learning-based gait anomaly detection using a sensorized tip: an individualized approachNeural Computing and Applications10.1007/s00521-023-08601-135:24(17443-17459)Online publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1007/s00521-023-08601-1
Jayabharathi SIlango V(2023)Anomaly Detection Using Machine Learning Techniques: A Systematic ReviewAdvances in Data-Driven Computing and Intelligent Systems10.1007/978-981-99-3250-4_42(553-572)Online publication date: 4-Aug-2023
https://doi.org/10.1007/978-981-99-3250-4_42
William PParganiha VPardeshi D(2023)Security Aspects of Quantum Machine LearningQuantum Computing in Cybersecurity10.1002/9781394167401.ch12(201-216)Online publication date: 18-Oct-2023
https://doi.org/10.1002/9781394167401.ch12
Kundu SGhosh SSavidis ISasan AThapliyal HDeMara R(2022)Security Aspects of Quantum Machine Learning: Opportunities, Threats and DefensesProceedings of the Great Lakes Symposium on VLSI 202210.1145/3526241.3530833(463-468)Online publication date: 6-Jun-2022
https://dl.acm.org/doi/10.1145/3526241.3530833
Salmani H(2022)Gradual-N-Justification (GNJ) to Reduce False-Positive Hardware Trojan Detection in Gate-Level NetlistIEEE Transactions on Very Large Scale Integration (VLSI) Systems10.1109/TVLSI.2022.314334930:4(515-525)Online publication date: Apr-2022
https://doi.org/10.1109/TVLSI.2022.3143349
Kornaros G(2022)Hardware-Assisted Machine Learning in Resource-Constrained IoT Environments for Security: Review and Future ProspectiveIEEE Access10.1109/ACCESS.2022.317904710(58603-58622)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3179047
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