SecureNoC: A Learning-enabled, High-performance, Energy-efficient, and Secure On-chip Communication Framework Design

Ke Wang, Hao Zheng, Yuan Li, Ahmed Louri

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


We propose SecureNoC, a learning-based framework to enhance NoC security against Hardware Trojan (HT) attacks while holistically improving performance and power. The proposed framework enhances NoC security with several architectural innovations, namely a per-router HT detector, multi-function bypass channels (MBCs), and a dynamic data encryption design. Specifically, the threat detector uses an artificial neural network for runtime HT detection with high accuracy. The MBCs consist of a router bypass route and reconfigurable channel buffers which can efficiently isolate malicious nodes and reduce power consumption. The proposed data encryption design adapts to diverse traffic patterns and dynamically deploys novel lightweight encryption techniques for desired security goals with improved latency. Additionally, to balance the trade-offs and handle the dynamic interactions of the proposed dynamic designs, a proactive deep-Q-learning (DQL) control policy is proposed to simultaneously provide optimized NoC security, performance, and power consumption. Simulation studies using PARSEC benchmarks show that the proposed SecureNoC achieves 36% higher HT detection accuracy over state-of-the-art NoC security techniques while reducing network latency by 39% and energy consumption by 46%.

Original languageEnglish (US)
JournalIEEE Transactions on Sustainable Computing
StateAccepted/In press - 2021
Externally publishedYes


  • Computer Architecture
  • Deep Reinforcement Learning
  • Encryption
  • Hardware
  • Network-on-Chip (NoC)
  • Neural Network
  • Neurons
  • NoC Security
  • Power demand
  • Public key
  • Runtime
  • Security

ASJC Scopus subject areas

  • Software
  • Renewable Energy, Sustainability and the Environment
  • Hardware and Architecture
  • Control and Optimization
  • Computational Theory and Mathematics


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