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

@article{Kulkarni2016RealTimeAD,
  title={Real-Time Anomaly Detection Framework for Many-Core Router through Machine-Learning Techniques},
  author={Amey M. Kulkarni and Youngok Pino and Matthew French and Tinoosh Mohsenin},
  journal={JETC},
  year={2016},
  volume={13},
  pages={10:1-10:22}
}
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… CONTINUE READING
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