Classification of Trojan Nets Based on SCOAP Values using Supervised Learning

Kok, Chee Hoo; Ooi, Chia Yee; Moghbel, Mehrdad; Ismail, Nordinah; Choo, Hau Sim; Inoue, Michiko

doi:10.1109/iscas.2019.8702462

Cited by 33 publications

(33 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [6], a K-means clustering-based HT detection approach that analyses controllability and observability features of nets has been proposed. [7] tested the same features in [6] on supervised ML algorithms and obtained a better classification result using a Bagged Trees model. However, with the limitation of computing power, the controllability and observability of the nets with deep depth in the netlist cannot be calculated correctly.…”

Section: Previous Researchmentioning

confidence: 99%

“…The weights (W ) are defined according to the number (N i ) of feature traces in each class. According to equation ( 5), the new cost function is rewritten as (7).…”

Section: Parameter Controlmentioning

confidence: 99%

“…For example, machine learning (ML)-based and neural network (NN)based HT detection methods have been proposed to detect and prevent HT-insertion at design-time without involving any extra complicated pre-processing or introducing additional overheads. In these approaches, HT related features are directly extracted from circuit designs and fed into ML/NN models to train HT detection models [4], [5], [6], [7], [8], [9], [10], [11]. Existing ML/NN-based HT detection methods have to depend on the pre-understanding of the characteristics of a circuit netlist (called knowledge-driven) to extract effective features for training ML/NN models.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Deep Learning-Based Hardware Trojan Detection With Block-Based Netlist Information Extraction

Liu

et al. 2022

IEEE Trans. Emerg. Topics Comput.

View full text Add to dashboard Cite

With the globalization of the semiconductor industry, hardware Trojans (HTs) are an emergent security threat in modern integrated circuit (IC) production. Research is now being conducted into designing more accurate and efficient methods to detect HTs. Recently, a number of machine learning (ML)-based HT detection approaches have been proposed; however, most of them still use knowledge-driven approaches to design features and often use engineering intuition to carefully craft the detection model to improve accuracy. Therefore, in this work, we propose a data-driven HT detection system based on gate-level netlists. The system consists of four main parts: 1) Information extraction from netlist block; 2) Natural language processing (NLP) for translating netlist information; 3) Deel learning (DL)-based HT detection model; 4) HT component final voter. In the experiments, both a long short-term memory networks (LSTM) model and convolutional neural network (CNN) model are used as our detection models. We performed the experiments on the HT benchmarks from Trust-hub and K-fold crossing verification has been applied to evaluate different parameter settings in the training procedure. The experimental results show that the proposed HT detection system can achieve 79.29% TPR, 99.97% TNR, 87.75% PPV and 99.94% NPV for combinational Trojan detection and 93.46% TPR, 99.99% TNR, 98.92% PPV and 99.92% NPV for sequential Trojan detection after voting-based optimization using the LEDA library-based HT benchmarks (logic level=4, upsampling, LSTM, 5 epochs).

show abstract

Section: Previous Researchmentioning

confidence: 99%

“…The weights (W ) are defined according to the number (N i ) of feature traces in each class. According to equation ( 5), the new cost function is rewritten as (7).…”

Section: Parameter Controlmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Deep Learning-Based Hardware Trojan Detection With Block-Based Netlist Information Extraction

Liu

et al. 2022

IEEE Trans. Emerg. Topics Comput.

View full text Add to dashboard Cite

show abstract

“…The work in [13] proposed a Trojan net detection method by training supervised classifiers using both combinational and sequential testability values as features in gate-level netlists with considerably large circuit size. Then the model is learned based on four popular supervised machine learning algorithms for binary-class classification: Fine Tree, Weighted k-NN, Fine Gaussian SVM and Bagged Trees.…”

Section: Prior Work On Static Hardware Trojan Detectionmentioning

confidence: 99%

SC-COTD: Hardware Trojan Detection Based on Sequential/Combinational Testability Features using Ensemble Classifier

2021

View full text Add to dashboard Cite

Security against Hardware Trojans (HT) is an important concern in integrated circuits (IC) design and fabrication. Most of the current HT detection methods are based on the golden model of circuit design. Further, some approaches require test pattern for HTs activation. In this paper, we propose SC-COTD (Sequential/Combinational Controllability and Observability features for hardware Trojan Detection), an effective hardware Trojan detection to get rid of both golden chip and test pattern limitations. SC-COTD uses both sequential and combinational testability measures to detect and locate HT signals by a machine learning approach. This method deploys an ensemble classifier based on k-means clustering. The clustering models have diverse variety in testability features along with size of clustering which inspect and reveal different aspects of netlist conventional for a collaborative scheme. The clustering results are filtered and then fed into a decision-making procedure based on majority voting to eliminate the limited flaws of each model. The evaluation results on TrustHUB benchmarks demonstrate that, SC-COTD can detect and locate HTs with 100% without any false negative, i.e., Recall = 1. Although our method has a limited number of false positive, it has the best performance in comparison to well-known previous approaches.

show abstract

“…Including these samples in the minority training set eliminates the original imbalance problem, and therefore removes the classification bias towards the majority. Frequently used synthetic algorithms such as SMOTE [21] and ADASYN [22], have exhibited some advantages in real-world applications of preterm diagnosis [10] and others [17,23].…”

Section: Introductionmentioning

confidence: 99%

Realistic preterm prediction based on optimized synthetic sampling of EHG signal

Chen

Zhang

et al. 2021

Computers in Biology and Medicine

View full text Add to dashboard Cite

Classification of Trojan Nets Based on SCOAP Values using Supervised Learning

Cited by 33 publications

References 10 publications

Deep Learning-Based Hardware Trojan Detection With Block-Based Netlist Information Extraction

Deep Learning-Based Hardware Trojan Detection With Block-Based Netlist Information Extraction

SC-COTD: Hardware Trojan Detection Based on Sequential/Combinational Testability Features using Ensemble Classifier

Realistic preterm prediction based on optimized synthetic sampling of EHG signal

Contact Info

Product

Resources

About