IMPLEMENTATION OF A HARDWARE TROJAN CHIP DETECTOR MODEL USING ARDUINO MICROCONTROLLER

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.

Hardware Trojans (HTs) are one of the emerging malicious hardware modification attacks that have become a critical threat to the integrity, reliability, security, and trustworthiness of integrated circuits (ICs) applications. These deliberately hidden malicious entities can be inserted into the IC during manufacturing or design, potentially leading to the leakage of secret information or the deactivation or destruction of the entire system that relies on the IC hardware chips. Localizing and detecting hardware Trojans is becoming increasingly challenging as these threats are deeply embedded and electronic systems continue to grow in complexity. Traditional methods, including physical inspections and functional testing, are increasingly inadequate. They are limited in scope and often fall short when confronted with the advanced designs of modern hardware Trojans.This study aims to improve the identification of hardware Trojans (HTs) in integrated circuits by using advanced machine learning algorithms with a unique dataset of power side-channel signals. Various machine learning techniques, such as Support Vector Machines (SVM), neural networks, and decision trees, are applied to classify and identify HTs accurately. This method combines comprehensive feature extraction and model validation to provide high accuracy and reliability in HT identification. This research makes significant contributions to cybersecurity by providing improved techniques for detecting minor anomalies associated with HTs using advanced machine learning algorithms. The results show potential advances in securing electronic systems against these hidden threats, highlighting the practical significance and necessity of our work in maintaining a wide range of applications from consumer electronics to national infrastructure.

Today’s Machine Learning (ML) in a blend with Internet of Things (IoT)-based cloud applications plays a significant role in our everyday life. As indicated by Gartner’s recent study, there are around 25 billion devices and a gadget interfacing with IoT including wearables and automated vehicles to smart homes and smart cities applications. All such connected (smart) devices generate immense data that needs to be examined and analysed, to ensure that they continually learn from the available datasets and better themselves without any manual interference. This is where the prerequisite for machine learning comes into being. Several ML algorithms and techniques are introduced in a short time to easily evaluate big data measurements, increasing the IoT’s productivity. Similarly, special ML techniques, such as decision trees, clustering and neural and Bayesian networks, allow devices and gadgets to discern trends from various sources in different kinds of datasets and take appropriate decisions based on their analysis.It would really be difficult for smart devices to make smart decisions gradually without including and enforcing ML. The IoT helps to interconnect various hardware devices, such as houses, cars, electronic gadgets and other devices that are integrated with actuators, sensors and software, so that data can be collected and shared. As various organizations understand the progressive capability of the IoT, they have begun finding various obstructions they have to deliver to use it productively. Numerous organizations and businesses use ML to exploit the IoT’s latent capacity. This chapter evaluates the different methods of machine learning that deal with the challenges posed in the handling of IoT data. Note that this big data is generated through the communication of Internet of Things/smart devices, and this data stored at cloud. The taxonomy of machine learning algorithms is described in this chapter, explaining how different techniques are applied to data generated using IoT devices. It will also address the future problems of machine learning for IoT data analytics.KeywordsInternet of Things (IoT)-based cloud applicationsMachine learning techniquesArtificial intelligenceClassificationSmart era

With the popularization of machine learning (ML) techniques and the increased chipset’s performance, the application of ML to pedestrian localization systems has received significant attention in the last years. Several survey papers have attempted to provide a state-of-the-art overview, but they usually limit their scope to a particular type of positioning system or technology. In addition, they are written from the point of view of ML techniques and their practice, not from the point of view of the localization system and the specific problems that ML techniques can help to solve. This article is intended to offer a comprehensive state-of-the-art survey of the ML techniques that have been adopted over the last ten years to improve the performance of pedestrian localization systems, addressing the applicability of ML techniques in this domain, along with the main localization strategies. It concludes by indicating the underlying open issues and challenges associated with the existing systems, and possible future directions in which ML techniques could improve the performance of pedestrian localization systems. Among other open issues, most previous authors have focused their attention on position estimation accuracy, which wastes the potential of ML techniques to improve other performance parameters (e.g., response time, computational complexity, robustness, scalability or energy efficiency). This study shows that there is a strong trend towards the application of supervised learning. Consequently, there are many potential research opportunities in the use of other learning types, such as unsupervised and reinforcement learning, to improve the performance of pedestrian localization systems.

A comprehensive review on detection of plant disease using machine learning and deep learning approaches

Machine Learning (ML) techniques are pervasive tools in various emerging commercial applications, but have to be accommodated by powerful computer systems to process very large data. Although general-purpose CPUs and GPUs have provided straightforward solutions, their energy-efficiencies are limited due to their excessive supports for flexibility. Hardware accelerators may achieve better energy-efficiencies, but each accelerator often accommodates only a single ML technique (family). According to the famous No-Free-Lunch theorem in the ML domain, however, an ML technique performs well on a dataset may perform poorly on another dataset, which implies that such accelerator may sometimes lead to poor learning accuracy. Even if regardless of the learning accuracy, such accelerator can still become inapplicable simply because the concrete ML task is altered, or the user chooses another ML technique.In this study, we present an ML accelerator called PuDianNao, which accommodates seven representative ML techniques, including k-means, k-nearest neighbors, naive bayes, support vector machine, linear regression, classification tree, and deep neural network. Benefited from our thorough analysis on computational primitives and locality properties of different ML techniques, PuDianNao can perform up to 1056 GOP/s (e.g., additions and multiplications) in an area of 3.51 mm^2, and consumes 596 mW only. Compared with the NVIDIA K20M GPU (28nm process), PuDianNao (65nm process) is 1.20x faster, and can reduce the energy by 128.41x.

Machine learning (ML) techniques have numerous applications in many fields, including healthcare, medicine, finance, marketing, and cyber security. For example, ML techniques are being applied to determine whether to give a loan to a customer or whether the computing system has been attacked. However, the ML techniques themselves may be subject to attacks and may discriminate when determining who should get the loan. Therefore, the ML techniques have to be secure, ensure privacy of the individuals, incorporate fairness and be accurate. Such collection of ML techniques has come to be known as trustworthy machine learning (trustworthy ML). This article describes an architecture to support scalable trustworthy ML and describes the features that have to be incorporated into the ML techniques to ensure that they are trustworthy.

Cyber-physical systems rely on sensors, communication, and computing, all powered by integrated circuits (ICs). These ICs are vulnerable to malicious hardware attacks, with hardware Trojans being one of the stealthiest threats. Trojans are malicious implants in the circuitry, which are often inserted during design or fabrication stages. This stealthy addition remains dormant until triggered and might cause functional disruptions or sensitive information leakage once triggered. Traditional IC validation methods, such as functional testing and logic analysis, usually fail to capture these subtle anomalies because hardware Trojans are intentionally designed to mimic normal circuit behavior. They often remain dormant under standard test vectors, and the changes they bring into power consumption, timing, and area are minimal. We present a dual-domain feature extraction strategy that combines time-domain features with frequency-domain characteristics of power traces. For the detection, we created an artificial intelligence (AI) based robust Trojan detection framework that integrates traditional machine learning models, such as random forest (RF), gradient boosting (GB), naïve bayes (NB), and deep learning models, such as deep neural network (DNN), long short-term memory (LSTM), and graph neural network (GNN). In this study, we consider these models as baseline AI models to detect Trojan-infected circuits via side-channel power analysis. We employed a stacked ensemble classifier that integrates the distinct strengths of the six baseline models used in this study. After evaluating our stacking ensemble-based detection method on the advanced encryption standard (AES)-Trojan benchmark, which covers diverse Trojan types, the results demonstrate that the ensemble method consistently outperformed all six baseline models. The ensemble-based detection method achieved a macro-averaged area under the receiver operating characteristic (ROC) curve (AUC) of 0.987, while remaining golden-chip-free, meaning it does not rely on a trusted reference IC for baseline comparison. Instead it detects anomalies directly from observable characteristics of untrusted chips, such as side-channel emissions.

Code smells are considered symptoms of poor implementation choices, which may hamper the software maintainability. Hence, code smells should be detected as early as possible to avoid software quality degradation. Unfortunately, detecting code smells is not a trivial task. Some preliminary studies investigated and concluded that machine learning (ML) techniques are a promising way to better support smell detection. However, these techniques are hard to be customized to promote an early and accurate detection of specific smell types. Yet, ML techniques usually require numerous code examples to be trained (composing a relevant dataset) in order to achieve satisfactory accuracy. Unfortunately, such a dependency on a large validated dataset is impractical and leads to late detection of code smells. Thus, a prevailing challenge is the early customized detection of code smells taking into account the typical limited training data. In this direction, this paper reports a study in which we collected code smells, from ten active projects, that were actually refactored by developers, differently from studies that rely on code smells inferred by researchers. These smells were used for evaluating the accuracy regarding early detection of code smells by using seven ML techniques. Once we take into account such smells that were considered as important by developers, the ML techniques are able to customize the detection in order to focus on smells observed as relevant in the investigated systems. The results showed that all the analyzed techniques are sensitive to the type of smell and obtained good results for the majority of them, especially JRip and Random Forest. We also observe that the ML techniques did not need a high number of examples to reach their best accuracy results. This finding implies that ML techniques can be successfully used for early detection of smells without depending on the curation of a large dataset.

Landslide disaster risk reduction necessitates the investigation of different geotechnical causal factors for slope failures. Machine learning (ML) techniques have been proposed to study causal factors across many application areas. However, the development of ensemble ML techniques for identifying the geotechnical causal factors for slope failures and their subsequent prediction has lacked in literature. The primary goal of this research is to develop and evaluate novel feature selection methods for identifying causal factors for slope failures and assess the potential of ensemble and individual ML techniques for slope failure prediction. Twenty-one geotechnical causal factors were obtained from 60 sites (both landslide and non-landslide) spread across a landslide-prone area in Mandi, India. Relevant causal factors were evaluated by developing a novel ensemble feature selection method that involved an average of different individual feature selection methods like correlation, information-gain, gain-ratio, OneR, and F-ratio. Furthermore, different ensemble ML techniques (Random Forest (RF), AdaBoost (AB), Bagging, Stacking, and Voting) and individual ML techniques (Bayesian network (BN), decision tree (DT), multilayer perceptron (MLP), and support vector machine (SVM)) were calibrated to 70% of the locations and tested on 30% of the sites. The ensemble feature selection method yielded six major contributing parameters to slope failures: relative compaction, porosity, saturated permeability, slope angle, angle of the internal friction, and in-situ moisture content. Furthermore, the ensemble RF and AB techniques performed the best compared to other ensemble and individual ML techniques on test data. The present study discusses the implications of different causal factors for slope failure prediction.

Intended malicious modification in the integrated circuits is referred to as Hardware Trojans, which has emerged as major security threat. The earlier approach, to keep check to these threats, like logic testing also known as functional testing is proved to be no longer effective for detecting large sequential Trojans which are very rarely triggered. Side channel analysis has been an effective approach for detection of such large sequential Trojans but the increasing process variations and decreasing Trojan size resulted in the reduction of Trojan detection sensitivity using this approach. All these approaches also require golden IC. In this paper, we propose leakage power analysis approach which does not require golden IC and the issue of process variations does not affect the detection sensitivity.

Quantum random number generators (QRNG) provide a strong assurance for the quality of the generated random numbers by using the intrinsic, indeterministic nature of quantum systems. In practise, a subtle correlation gone undetected is enough to greatly compromise the security of an RNG. This is especially so with the advent of state-of-art machine learning (ML) techniques. An adversary familiar with ML techniques could potentially use them to gain much more information about a seemingly random source, causing a significant risk for security. In this presentation, we turn ML techniques from being a potential adversary to a powerful tool for the diagnostics of a QRNG. We demonstrate the capability of ML in finding correlations in a random source, thereby making first steps in establishing ML as a diagnostic tool. To this end, we inspect the classical and the quantum entropy sources in a vacuum fluctuation based QRNG using ML technique. Alongside existing NIST statistical tests, we discover that even a statistically-sound PRNG can be considered non-uniform under the scrutiny of ML techniques.

Nowadays, an integrated circuit can carry many functions which make them more challenging to design and manufacture. The involvement of the third party is not unusual which can open the possibilities for unauthorized modification by inserting illegal block called hardware trojan to perform some malicious functions. This issue raises attention in the field of hardware security to prevent the insertion of the hardware trojan and to detect its presence in a chip. Side channel analysis is considered as a prospective method, although there are several difficulties encountered. In this paper, we will implement cryptographic hardware that can encrypt information using Advanced Encryption Standards (AES) in a Field Programmable Gate Arrays (FPGA). Then, this reference model is subjected to the implantation of hardware trojan designed to leak the AES key. By using the side channel analysis and by adjusting the behavior of the hardware trojan, we will try to detect its presence using conventional instruments available in our labs. In the end, we can conclude the possibility for the end customer to detect the existence of hardware trojan and the design techniques of hardware trojan to evade the detection.

PurposeThis study aims to emphasize utilization of Predictive Six Sigma to achieve process improvements based on machine learning (ML) techniques embedded in define, measure, analyze, improve, control (DMAIC). With this aim, this study presents selection and utilization of ML techniques, including multiple linear regression (MLR), artificial neural network (ANN), random forests (RF), gradient boosting machines (GBM) and k-nearest neighbors (k-NN) in the analyze and improve phases of Six Sigma DMAIC.Design/methodology/approachA data set containing 320 observations with nine input and one output variables is used. To achieve the objective which was to decrease the number of fabric defects, five ML techniques were compared in terms of prediction performance and best tools were selected. Next, most important causes of defects were determined via these tools. Finally, parameter optimization was conducted for minimum number of defects.FindingsAmong five ML tools, ANN, GBM and RF are found to be the best predictors. Out of nine potential causes, “machine speed” and “fabric width” are determined as the most important variables by using these tools. Then, optimum values for “machine speed” and “fabric width” for fabric defect minimization are determined both via regression response optimizer and ANN surface optimization. Ultimately, average defect number was decreased from 13/roll to 3/roll, which is a considerable decrease attained through utilization of ML techniques in Six Sigma.Originality/valueAddressing an important gap in Six Sigma literature, in this study, certain ML techniques (i.e. MLR, ANN, RF, GBM and k-NN) are compared and the ones possessing best performances are used in the analyze and improve phases of Six Sigma DMAIC.

In recent years, planners have started considering Machine Learning (ML) techniques as an alternative to discrete choice models (CM). ML techniques are primarily data-driven and typically achieve better prediction accuracy compared to CM. However, it is hypothesized that since the ML techniques do not have the strong grounding to economic theory as the CMs, they may not perform well in contexts that are radically different from the ‘training’ scenario. It is also hypothesized that the relative prediction performance may be affected by the metrics used for comparing the models. This research aims to test these two hypotheses empirically by modelling vehicle ownership choices using household survey data from Dhaka, Bangladesh collected in 2004, 2010 and 2019. The performances of CM (multinomial logit) and ML techniques (neural networks and gradient boosting trees) have been compared using log-likelihood and mean absolute percentage error of market shares. The results indicate that the multinomial logit model (MNL) with a piecewise linear transformation of the household income, has the best performance in terms of log-likelihood and mean absolute percentage error of market shares. This is followed by Neural Networks (NN) and Gradient Boosting Trees (GBT). The results thus provide empirical evidence that the ML techniques do not consistently outperform CM. Moreover, the difference in the performance of the models further increases if the prediction scenario is substantially different. This reinforces the hypothesis that CMs, with their behavioural underpinning, are better suited for long-term forecasting than data-driven ML approaches, especially if the population and network attributes are expected to change substantially. These findings will be useful for planners and policy makers in the selection of the appropriate tool for forecasting travel demand.