A Hardware Trojan Detection and Diagnosis Method for Gate-Level Netlists Based on Different Machine Learning Algorithms

Hardware Trojans (HTs) pose a serious threat to integrated circuit (IC) security. Detection of HTs is extremely challenging due to their stealthy nature. Side-channel analysis techniques have emerged as promising approaches for HT detection by observing anomalies in physical parameters like power or delay. More recently, machine learning (ML) methods have been explored to enhance the accuracy and efficiency of side-channel based HT detection. Background: This paper presents a novel approach using machine learning techniques to detect stealthy hardware Trojans through side-channel analysis. Hardware Trojans, malicious modifications inserted into integrated circuits during manufacturing, pose significant threats to the integrity and security of electronic systems. Traditional methods of detecting these Trojans often rely on known signatures or specific patterns, making them ineffective against subtle and sophisticated attacks. Method: This paper provides a comprehensive review of research advancements in applying ML for side-channel based HT detection. First, an overview of HT attacks, their classification, threat models and detection challenges is presented. Next, various side-channel parameters like power, temperature, delay and electromagnetic emanations used for HT detection are discussed along with their merits and demerits. Furthermore, the application of supervised, unsupervised and semi-supervised ML algorithms for automated feature extraction and intelligent decision making is elucidated in detail. Result: Specifically, the data collection strategies, feature extraction techniques, ML models and performance evaluation metrics adopted in existing literature are critically reviewed. In addition, the limitations of current approaches and promising future research directions like on-chip ML implementation, hierarchical ML and explainable ML models tailored for HT detection are highlighted. Conclusion: Case studies on benchmark circuits are also presented to demonstrate the efficacy of ML-based side-channel HT detection methods. Through an extensive literature review and incisive analysis, this paper provides contemporary insights on the advancement of ML techniques to enable robust side-channel based HT detection for securing next-generation ICs.

Text mining is a special case of data mining which explore unstructured or semi-structured text documents, to establish valuable patterns and rules that indicate trends and significant features about specific topics. Text mining has been in pattern recognition, predictive studies, sentiment analysis and statistical theories in many areas of research, medicine, financial analysis, social life analysis, and business intelligence. Text mining uses concept of natural language processing and machine learning. Machine learning algorithms have been used and reported to give great results, but their performance of machine learning algorithms is affected by factors such as dataset domain, number of classes, length of the corpus, and feature selection techniques used. Redundant attribute affects the performance of the classification algorithm, but this can be reduced by using different feature selection techniques and dimensionality reduction techniques. Feature selection is a data preprocessing step that chooses a subset of input variable while eliminating features with little or no predictive information. Feature selection techniques are Information gain, Term Frequency, Term Frequency-Inverse document frequency, Mutual Information, and Chi-Square, which can use a filters, wrappers, or embedded approaches. To get the most value from machine learning, pairing the best algorithms with the right tools and processes is necessary. Little research has been done on the effect of feature selection techniques on classification accuracy for pairing of these algorithms with the best feature selection techniques for optimal results. In this research, a text classification experiment was conducted using incident management dataset, where incidents were classified into their resolver groups. Support vector machine (SVM), K-Nearest Neighbors (KNN), Naïve Bayes (NB) and Decision tree (DT) machine learning algorithms were examined. Filtering approach was used on the feature selection techniques, with different ranking indices applied for optimal feature set and classification accuracy results analyzed. The classification accuracy results obtained using TF were, 88% for SVM, 70% for NB, 79% for Decision tree, and KNN had 55%, while Boolean registered 90%, 83%, 82% and 75%, for SVM, NB, DT, and KNN respectively. TF-IDF, had 91%, 83%, 76%, and 56% for SVM, NB, DT, and KNN respectively. The results showed that algorithm performance is affected by feature selection technique applied. SVM performed best, followed by DT, KNN and finally NB. In conclusion, presence of noisy data leads to poor learning performance and increases the computational time. The classifiers performed differently depending on the feature selection technique applied. For optimal results, the classifier that performed best together with the feature selection technique with the best feature subset should be applied for all types of data for accurate classification performance. Keywords: Text Classification, Supervised Machine Learning, Feature Selection DOI : 10.7176/JIEA/9-3-06 Publication date :May 31 st 2019

In recent years, with the prosperity and globalization of the semiconductor industry, hardware security issues have become another major problem besides software security. To reduce the development costs, integrated circuits are often outsourced to various external vendors. This provides malicious third-party vendors a good chance to implant Trojans in the circuit design and manufacture phases. It is crucial to find effective solution to hardware Trojan detection. This paper proposes a hybrid hardware Trojan detection method for gate-level netlist based on XGBoost algorithm. The detection method affirms each network as a node and aims to detect all Trojan networks existing in the circuit. Firstly, according to the difference between the features of the Trojan circuit and the normal circuit, the effective features of the circuit are extracted. Then the XGBoost algorithm is applied to train the optimum feature set and classify the circuit into suspicious Trojan circuits and normal circuits. To obtain a higher accuracy, the dynamic detection method is applied to make up for the shortcomings of the static detection. The experimental results on the Trust-HUB benchmarks show that the detection method can achieve accurate detection of hardware Trojan circuits. The detection method can finally achieve 90.9% average True Positive Rate (TPR) and 99.0% average True Negative Rate (TNR).

Hardware Trojans (HTs), maliciously inserted in an integrated circuit during untrusted design or fabrication process pose critical threat to the system security. With the ever increasing capabilities of an adversary to subvert the system during run-time, it is imperative to detect the manifested Trojans in order to reinforce the trust in hardware. In this regard, Machine Learning (ML) algorithms, with their intrinsic capability to execute feature engineering at high learning rates, are emerging as promising candidates to be utilized by system defenders. In this paper, we explore Trojan detection mechanisms that are based on ML, and thereby investigate the prowess of the ML algorithms in bolstering system security. Furthermore, we analyze the efficiency of each proposed Trojan detection strategy based on the underlying ML algorithm. Finally, we underline some problems with existing Trojan detection approaches and discuss future research in the interest of improved performance of the employed ML algorithms, thus aiding in enhancing the intended hardware security.

An intrusion detection system (IDS) is an important protection instrument for detecting complex network attacks. Various machine learning (ML) or deep learning (DL) algorithms have been proposed for implementing anomaly-based IDS (AIDS). Our review of the AIDS literature identifies some issues in related work, including the randomness of the selected algorithms, parameters, and testing criteria, the application of old datasets, or shallow analyses and validation of the results. This paper comprehensively reviews previous studies on AIDS by using a set of criteria with different datasets and types of attacks to set benchmarking outcomes that can reveal the suitable AIDS algorithms, parameters, and testing criteria. Specifically, this paper applies 10 popular supervised and unsupervised ML algorithms for identifying effective and efficient ML-AIDS of networks and computers. These supervised ML algorithms include the artificial neural network (ANN), decision tree (DT), k-nearest neighbor (k-NN), naive Bayes (NB), random forest (RF), support vector machine (SVM), and convolutional neural network (CNN) algorithms, whereas the unsupervised ML algorithms include the expectation-maximization (EM), k-means, and self-organizing maps (SOM) algorithms. Several models of these algorithms are introduced, and the turning and training parameters of each algorithm are examined to achieve an optimal classifier evaluation. Unlike previous studies, this study evaluates the performance of AIDS by measuring the true positive and negative rates, accuracy, precision, recall, and F-Score of 31 ML-AIDS models. The training and testing time for ML-AIDS models are also considered in measuring their performance efficiency given that time complexity is an important factor in AIDSs. The ML-AIDS models are tested by using a recent and highly unbalanced multiclass CICIDS2017 dataset that involves real-world network attacks. In general, the k-NN-AIDS, DT-AIDS, and NB-AIDS models obtain the best results and show a greater capability in detecting web attacks compared with other models that demonstrate irregular and inferior results.

In brain–computer interfaces (BCIs), it is crucial to process brain signals to improve the accuracy of the classification of motor movements. Machine learning (ML) algorithms such as artificial neural networks (ANNs), linear discriminant analysis (LDA), decision tree (D.T.), K-nearest neighbor (KNN), naive Bayes (N.B.), and support vector machine (SVM) have made significant progress in classification issues. This paper aims to present a signal processing analysis of electroencephalographic (EEG) signals among different feature extraction techniques to train selected classification algorithms to classify signals related to motor movements. The motor movements considered are related to the left hand, right hand, both fists, feet, and relaxation, making this a multiclass problem. In this study, nine ML algorithms were trained with a dataset created by the feature extraction of EEG signals.The EEG signals of 30 Physionet subjects were used to create a dataset related to movement. We used electrodes C3, C1, CZ, C2, and C4 according to the standard 10-10 placement. Then, we extracted the epochs of the EEG signals and applied tone, amplitude levels, and statistical techniques to obtain the set of features. LabVIEW™2015 version custom applications were used for reading the EEG signals; for channel selection, noise filtering, band selection, and feature extraction operations; and for creating the dataset. MATLAB 2021a was used for training, testing, and evaluating the performance metrics of the ML algorithms. In this study, the model of Medium-ANN achieved the best performance, with an AUC average of 0.9998, Cohen’s Kappa coefficient of 0.9552, a Matthews correlation coefficient of 0.9819, and a loss of 0.0147. These findings suggest the applicability of our approach to different scenarios, such as implementing robotic prostheses, where the use of superficial features is an acceptable option when resources are limited, as in embedded systems or edge computing devices.

Globalization exacerbates intellectual property (IP) piracy and hardware Trojan (HT) insertion in circuit design. Existing machine learning methods for HT detection can spot malicious insertions but often overlook the issue of generating low-quality synthetic samples in the face of sample imbalance. Moreover, using a single detection model limits the ability to detect a variety of HT. In this paper, we first propose six new HT features. Then, to address the sample imbalance issue and generate higher-quality synthetic samples, we employ the borderline synthetic minority over-sampling technique (Borderline-SMOTE) to generate synthetic samples, selecting the top 90% of synthetic samples for in-depth analysis. Furthermore, this study uses neural network technology for the efficient fusion of sample features, forming an advanced feature containing more comprehensive information, which is then applied as another new feature in our detection model. Finally, we use the light gradient boosting machine (LightGBM) algorithm for the ultimate HT detection. Through a series of Trust-HUB benchmark tests, our method has demonstrated outstanding performance, with the average true positive rate (TPR) of 92.83%, achieving 100% in several circuit tests. Additionally, the average true negative rate (TNR) is 90.26%, with the average accuracy of 90.86%.

The recent advance in information technology has created a new era named the Internet of Things (IoT). This new technology allows objects (things) to be connected to the Internet, such as smart TVs, printers, cameras, smartphones, smartwatches, etc. This trend provides new services and applications for many users and enhances their lifestyle. The rapid growth of the IoT makes the incorporation and connection of several devices a predominant procedure. Although there are many advantages of IoT devices, there are different challenges that come as network anomalies. In this research, the current studies in the use of deep learning (DL) in DDoS intrusion detection have been presented. This research aims to implement different Machine Learning (ML) algorithms in WEKA tools to analyze the detection performance for DDoS attacks using the most recent CICDDoS2019 datasets. CICDDoS2019 was found to be the model with best results. This research has used six different types of ML algorithms which are K_Nearest_Neighbors (K-NN), super vector machine (SVM), naïve bayes (NB), decision tree (DT), random forest (RF) and logistic regression (LR). The best accuracy result in the presented evaluation was achieved when utilizing the Decision Tree (DT) and Random Forest (RF) algorithms, 99% and 99%, respectively. However, the DT is better than RF because it has a shorter computation time, 4.53 s and 84.2 s, respectively. Finally, open issues for further research in future work are presented.

This study presents a comparative analysis of the classification performance of facial and emotion recognition systems using Machine Learning (ML) and Deep Learning (DL) algorithms. The primary objective of this work is to evaluate the applicability of emotion recognition in fields such as psychotherapy and crime analysis, using the FER-2013 dataset.The study was conducted with ML algorithms such as Support Vector Machines (SVM), Random Forest (RF), K-Nearest Neighbor (KNN), Decision Trees, and Gradient Boosting, as well as the DL algorithm, Convolutional Neural Network (CNN). Supported by the application of feature selection, preprocessing, and feature extraction techniques, the model’s performance was measured using standard metrics such as accuracy, precision, F1-score, and AUC-ROC.The experimental results showed that the highest classification accuracy (46.61%) was achieved with the CNN model. While the ML models generally offered lower accuracy, they provided advantages in terms of computational efficiency in specific scenarios.This study aims to contribute to the literature by providing a comparative analysis of ML and DL algorithms and by highlighting the effect of data preprocessing on performance. The findings set targets for future work, such as real-time system integration and hybrid model development.

Recent advances in machine learning techniques have significantly influenced the development of early diabetes detection systems. In this paper, different machine learning (ML) algorithms are employed for diabetes detection based on hand movement. The spatial and temporal information for horizontal and vertical hand movement deviations for both diabetic and non-diabetic subjects are obtained using a data acquisition system. A dataset is constructed based on the obtained hand movement information along with other related attributes such as age, gender, weight and vision health. Binary classification of diabetic and non-diabetic subjects is achieved using several machine learning algorithms. These algorithms include Naïve Bayes (NB), Logistic Regression (LR), Linear Discriminate Analysis (LDA), Classification and Regression Tree (CART), k-Nearest Neighbors (KNN), Support Vector Machine (SVM) and Multi-layer Perceptron (MLP). The considered ML algorithms are evaluated and ranked according to a set of key performance metrics that include accuracy, precision, recall and F-1 score. Algorithm ranking results demonstrate that LR, CART, KNN and MLP algorithms have achieved higher ranking positions as compared to other algorithms. The best ranked ML algorithms are then validated and thoroughly investigated to prove their ability to discriminate between diabetic and non-diabetic subjects. Validation results show that the best-ranked ML algorithms have achieved high classification accuracy, precision, recall and F-1 scores. Therefore, the considered machine learning algorithms can serve as efficient tools for diabetes detection based on the constructed dataset.

BackgroundCoronavirus disease 2019 (COVID-19), a global public health crisis, continues to pose challenges despite preventive measures. The daily rise in COVID-19 cases is concerning, and the testing process is both time-consuming and costly. While several models have been created to predict mortality in COVID-19 patients, only a few have shown sufficient accuracy. Machine learning algorithms offer a promising approach to data-driven prediction of clinical outcomes, surpassing traditional statistical modeling. Leveraging machine learning (ML) algorithms could potentially provide a solution for predicting mortality in hospitalized COVID-19 patients in Ethiopia. Therefore, the aim of this study is to develop and validate machine-learning models for accurately predicting mortality in COVID-19 hospitalized patients in Ethiopia.MethodsOur study involved analyzing electronic medical records of COVID-19 patients who were admitted to public hospitals in Ethiopia. Specifically, we developed seven different machine learning models to predict COVID-19 patient mortality. These models included J48 decision tree, random forest (RF), k-nearest neighborhood (k-NN), multi-layer perceptron (MLP), Naïve Bayes (NB), eXtreme gradient boosting (XGBoost), and logistic regression (LR). We then compared the performance of these models using data from a cohort of 696 patients through statistical analysis. To evaluate the effectiveness of the models, we utilized metrics derived from the confusion matrix such as sensitivity, specificity, precision, and receiver operating characteristic (ROC).ResultsThe study included a total of 696 patients, with a higher number of females (440 patients, accounting for 63.2%) compared to males. The median age of the participants was 35.0 years old, with an interquartile range of 18–79. After conducting different feature selection procedures, 23 features were examined, and identified as predictors of mortality, and it was determined that gender, Intensive care unit (ICU) admission, and alcohol drinking/addiction were the top three predictors of COVID-19 mortality. On the other hand, loss of smell, loss of taste, and hypertension were identified as the three lowest predictors of COVID-19 mortality. The experimental results revealed that the k-nearest neighbor (k-NN) algorithm outperformed than other machine learning algorithms, achieving an accuracy of 95.25%, sensitivity of 95.30%, precision of 92.7%, specificity of 93.30%, F1 score 93.98% and a receiver operating characteristic (ROC) score of 96.90%. These findings highlight the effectiveness of the k-NN algorithm in predicting COVID-19 outcomes based on the selected features.ConclusionOur study has developed an innovative model that utilizes hospital data to accurately predict the mortality risk of COVID-19 patients. The main objective of this model is to prioritize early treatment for high-risk patients and optimize strained healthcare systems during the ongoing pandemic. By integrating machine learning with comprehensive hospital databases, our model effectively classifies patients' mortality risk, enabling targeted medical interventions and improved resource management.Among the various methods tested, the K-nearest neighbors (KNN) algorithm demonstrated the highest accuracy, allowing for early identification of high-risk patients. Through KNN feature identification, we identified 23 predictors that significantly contribute to predicting COVID-19 mortality. The top five predictors are gender (female), intensive care unit (ICU) admission, alcohol drinking, smoking, and symptoms of headache and chills.This advancement holds great promise in enhancing healthcare outcomes and decision-making during the pandemic. By providing services and prioritizing patients based on the identified predictors, healthcare facilities and providers can improve the chances of survival for individuals. This model provides valuable insights that can guide healthcare professionals in allocating resources and delivering appropriate care to those at highest risk.

Worldwide, cancer is the most frequent cause of passing away for women. Any development in predicting and diagnosing cancer is crucial for a healthy life. As such, vital cancer accuracy in predicting patients' survival parameters and treatment aspects is necessary. Machine learning methods significantly impact breast cancer diagnosis and early diagnosis. This study aims to increase prediction accuracy using a novel statistical feature selection technique. This article examines the classification test accuracy, standard data precision, and the process performance of multiple machine learning (ML) algorithms, such as Random; using the Wisconsin Diagnostic Breast Cancer (WDBC) dataset, the following models were used: Support Vector Machine, Logistic Regression, Decision Tree (C4.5), Forest Naïve Bayes (NB), Linear Regression (LR), k-nearest-neighbors (KNN), and Multilayer Perceptron (MLP). The data set is partitioned to use the machine learning algorithm: 20% is used during the test phase, while 80% is used throughout training. Hyper-parameters that are manually assigned are utilized to modify the classifier. When applied to a subset of data, it showed that combining SVM and model in machine learning reached the maximum accuracy of up to 90%, which was noticeably superior to the other ML model.

In the era of artificial intelligence, machine learning methods are successfully used in various fields. Machine learning has attracted extensive attention from investors in the financial market, especially in stock price prediction. However, one argument for the machine learning methods used in stock price prediction is that they are black-box models which are difficult to interpret. In this paper, we focus on the future stock price prediction with the historical stock price by machine learning and deep learning methods, such as support vector machine (SVM), random forest (RF), Bayesian classifier (BC), decision tree (DT), multilayer perceptron (MLP), convolutional neural network (CNN), bi-directional long-short term memory (BiLSTM), the embedded CNN, and the embedded BiLSTM. Firstly, we manually design several financial time series where the future price correlates with the historical stock prices in pre-designed modes, namely the curve-shape-feature (CSF) and the non-curve-shape-feature (NCSF) modes. In the CSF mode, the future prices can be extracted from the curve shapes of the historical stock prices. Conversely, in the NCSF mode, they can’t. Secondly, we apply various algorithms to those pre-designed and real financial time series. We find that the existing machine learning and deep learning algorithms fail in stock price prediction because in the real financial time series, less information of future prices is contained in the CSF mode, and perhaps more information is contained in the NCSF. Various machine learning and deep learning algorithms are good at handling the CSF in historical data, which are successfully applied in image recognition and natural language processing. However, they are inappropriate for stock price prediction on account of the NCSF. Therefore, accurate stock price prediction is the key to successful investment, and new machine learning algorithms handling the NCSF series are needed.

Rainfall prediction is the highest research priority in flood-prone areas across the world. This work assesses the abilities of the Decision Tree (DT), Distributed Decision Tree (DDT), Naïve Bayes (NB), Random Forest (RF), Support Vector Machine (SVM), K Nearest Neighbour (KNN), and Fuzzy Logic Decision Tree (FDTs) machine learning algorithms for the rainfall prediction across the Kashmir province of the Union Territory of Jammu & Kashmir. On application of Machine learning algorithms on geographical datasets gave performance accuracy varying from (78.61–81.53)%. Further again machine learning algorithms were reapplied on the dataset without season variable yet again performance ranged in between (77.5–81)%. Vigorous analysis has established that these machine learning models are robust and our study has established that the dataset reaches performance stagnation and thus resulting in performance capping. The stagnation is irrespective of the choice of algorithm and the performance shall not improvise beyond a specific value irrespective of the choice of the machine learning algorithm.KeywordsDecision treeGeographical dataRandom forestNaïve BayesDistribute decision tree

Handwritten signature identification is a process that determines an individual’s true identity by analyzing their signature. This is an important task in various applications such as financial transactions, legal document verification, and biometric systems. Various techniques have been developed for signature identification, including feature-based methods and machine learning-based methods. However, verifying handwritten signatures in digital transactions and remote document authentication is still challenging. The inherent variety in people’s signatures, which may occur due to factors such as mood, exhaustion, or even the writing tool used, contributes to the problem. Furthermore, the proliferation of sophisticated forgery methods, such as freehand mimicking and sophisticated picture manipulation, necessitates the development of reliable and precise tools for identifying authentic signatures from fake ones.The present paper suggests a method for identifying signatures based on integrating static (off-line) handwritten signature data. This is done by fusing three types of signature features: Linear Discriminant Analysis (LDA) as appearance-based features, Fast Fourier Transform (FFT) as frequency-features, and Gray-Level Co-occurrence Matrix (GLCM) as texture-features. Then, these fused features are inputted into four types of machine learning algorithms: Naive Bayes, K-Nearest Neighbor, Decision Tree, and AdaBoost classifiers, to identify each person and to find the most robust algorithm in terms of accuracy and precision and recall. For experiments, we have used two famous datasets: SigComp2011 and CEDAR. After training datasets, the highest accuracy achieved was 100% on the CEDAR dataset and 94.43% on the SigComp2011 dataset using a Naive Bayes classifier. Index Terms— Fast Fourier Transform, Gray-Level Co-occurrence Matrix, Handwritten Signature, Linear Discriminant Analysis, Machine Learning.