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.

中文翻译：

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Sniffer：基于 NoC 的 SoC 中 DoS 攻击本地化的机器学习方法


由于其共享特性以及对所有片上模块的开放访问，基于泛洪的拒绝服务 (DoS) 攻击在片上网络 (NoC) 架构中非常普遍。片上系统 (SoC) 内的恶意知识产权 (MIP) 通过向 NoC 发送无用的数据包来发起此类攻击，从而导致带宽显着减少。找到 MIP 的位置对于恢复正常网络运行和减少系统性能下降至关重要。在这项工作中，我们提出了 Sniffer，这是一种高效的 MIP 定位框架，它采用低开销的机器学习方法来准确跟踪攻击路径并采取集体决策来定位 MIP。实验结果表明，Sniffer 能够为 MIP 定位提供高精度，而不会产生大量开销。