使用集成机器学习技术的最近邻方法快速准确识别门级网表中的硬件木马位置
Fast and Accurate Identification of Hardware Trojan Locations in Gate-Level Netlist using Nearest Neighbour Approach integrated with Machine Learning Technique
AI总结：
本研究提出一种基于机器学习和路径回溯的方法识别门级网表中的硬件木马，结合最近邻（NN）方法与图神经网络（GNN）进行图与节点分类，实验表明NN方法在准确率上显著优于GNN，第二最近邻准确率高达97.7%。
摘要：
在不断演进的集成电路设计领域，在多实体设计周期中检测硬件木马（HTs）面临重大挑战。本研究提出了一种基于机器学习的创新方法，用于识别门级网表中的恶意逻辑门。该方法聚焦于路径回溯算法。该方法在三个不同案例中得到了验证，每个案例采用不同的机器学习模型来分类HTs。案例I利用决策树算法进行节点到节点的比较，通过集成主成分分析（PCA）显著提高了检测准确性。案例II引入了使用图神经网络（GNN）模型的图到图分类，能够区分正常电路设计和感染木马的电路设计。案例III应用基于GNN的节点分类来识别单个受损节点及其位置。此外，在案例II中将最近邻（NN）方法与GNN图到图分类结合，在案例III中将NN方法与GNN节点到节点分类结合。尽管GNN模型图到图分类具有潜力，但NN方法表现出更优的性能，第一最近邻（1st NN）达到了73.2%的准确率，第二最近邻（2nd NN）方法达到了97.7%的准确率。相比之下，GNN模型的准确率为62.8%。类似地，在GNN模型节点到节点分类中，NN方法表现出更优的性能，1st NN达到了93%的准确率，2nd NN方法达到了97.7%的准确率。相比之下，GNN模型的准确率为79.8%。然而，越来越高的NN会导致HTs识别的代码覆盖率变大。
英文摘要：
In the evolving landscape of integrated circuit design, detecting Hardware Trojans (HTs) within a multi entity based design cycle presents significant challenges. This research proposes an innovative machine learning-based methodology for identifying malicious logic gates in gate-level netlists. By focusing on path retrace algorithms. The methodology is validated across three distinct cases, each employing different machine learning models to classify HTs. Case I utilizes a decision tree algorithm for node-to-node comparisons, significantly improving detection accuracy through the integration of Principal Component Analysis (PCA). Case II introduces a graph-to-graph classification using a Graph Neural Network (GNN) model, enabling the differentiation between normal and Trojan-infected circuit designs. Case III applies GNN-based node classification to identify individual compromised nodes and its location. Additionally, nearest neighbor (NN) method has been combined with GNN graph-to-graph in Case II and GNN node-to-node in Case III. Despite the potential of GNN model graph-to-graph classification, NN approach demonstrated superior performance, with the first nearest neighbor (1st NN) achieving 73.2% accuracy and the second nearest neighbor (2nd NN) method reaching 97.7%. In comparison, the GNN model achieved an accuracy of 62.8%. Similarly, GNN model node-to-node classification, NN approach demonstrated superior performance, with the 1st NN achieving 93% accuracy and the 2nd NN method reaching 97.7%. In comparison, the GNN model achieved an accuracy of 79.8%. However, higher and higher NN will lead to large code coverage for the identification of HTs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
