Comprehensive Designs of Innovate Secure Hardware Devices against Machine Learning Attacks and Power Analysis Attacks

Hardware security is an innovate subject oriented from growing demands of cybersecurity and new information vulnerabilities from physical leakages on hardware devices. However, the mainstream of hardware manufacturing industry is still taking benefits of products and the performance of chips as priority, restricting the design of hardware secure countermeasures under a compromise to a finite expense of overheads. Consider the development trend of hardware industries and state-of-the-art researches of architecture designs, this dissertation proposes some new physical unclonable function (PUF) designs as countermeasures to side-channel attacks (SCA) and machine learning (ML) attacks simultaneously. Except for the joint consideration of hardware and software vulnerabilities, those designs also take efficiencies and overhead problems into consideration, making the new-style of PUF more possible to be merged into current chips as well as their design concepts. While the growth of artificial intelligence and machine-learning techniques dominate the researching trends of Internet of things (IoT) industry, some mainstream architectures of neural networks are implemented as hypothetical attacking model, whose results are used as references for further lifting the performance, the security level, and the efficiency in lateral studies. In addition, a study of implementation of neural networks on hardware designs is proposed, this realized the initial attempt to introduce AI techniques to the designs of voltage regulation (VR). All aforementioned works are demonstrated to be of robustness to threats with corresponding power attack tests or ML attack tests. Some conceptional models are proposed in the last of the dissertation as future plans so as to realize secure on-chip ML models and hardware countermeasures to hybrid threats

Trojan Detection System Using Machine Learning Approach

Malware attack cases continue to rise in our current day. The Trojan attack, which may be extremely destructive by unlawfully controlling other users' computers in order to steal their data. As a result, Trojan horse detection is essential to identify the Trojan and limit Trojan attacks. In this study, we proposed a Trojan detection system that employed machine learning algorithms to detect Trojan horses within the system. A public dataset of Trojan horses that contain 2001 samples comprises of 1041 Trojan horses and 960 of benign is used to train the machine learning classification. In this paper, the Trojan detection system is trained using four types of classifiers which are Random Forest, J48, Decision Table and Naïve Bayes. WEKA is used for the execution of the classification process and performance analysis. The results indicated that the detection system trained with the Random Forest and Decision Table algorithms obtained the maximum level of accuracy

Adversarial Deep Learning and Security with a Hardware Perspective

Adversarial deep learning is the field of study which analyzes deep learning in the presence of adversarial entities. This entails understanding the capabilities, objectives, and attack scenarios available to the adversary to develop defensive mechanisms and avenues of robustness available to the benign parties. Understanding this facet of deep learning helps us improve the safety of the deep learning systems against external threats from adversaries. However, of equal importance, this perspective also helps the industry understand and respond to critical failures in the technology. The expectation of future success has driven significant interest in developing this technology broadly. Adversarial deep learning stands as a balancing force to ensure these developments remain grounded in the real-world and proceed along a responsible trajectory. Recently, the growth of deep learning has begun intersecting with the computer hardware domain to improve performance and efficiency for resource constrained application domains. The works investigated in this dissertation constitute our pioneering efforts in migrating adversarial deep learning into the hardware domain alongside its parent field of research

Enhancing Efficiency and Privacy in Memory-Based Malware Classification through Feature Selection

Malware poses a significant security risk to individuals, organizations, and critical infrastructure by compromising systems and data. Leveraging memory dumps that offer snapshots of computer memory can aid the analysis and detection of malicious content, including malware. To improve the efficacy and address privacy concerns in malware classification systems, feature selection can play a critical role as it is capable of identifying the most relevant features, thus, minimizing the amount of data fed to classifiers. In this study, we employ three feature selection approaches to identify significant features from memory content and use them with a diverse set of classifiers to enhance the performance and privacy of the classification task. Comprehensive experiments are conducted across three levels of malware classification tasks: i) binary-level benign or malware classification, ii) malware type classification (including Trojan horse, ransomware, and spyware), and iii) malware family classification within each family (with varying numbers of classes). Results demonstrate that the feature selection strategy, incorporating mutual information and other methods, enhances classifier performance for all tasks. Notably, selecting only 25\% and 50\% of input features using Mutual Information and then employing the Random Forest classifier yields the best results. Our findings reinforce the importance of feature selection for malware classification and provide valuable insights for identifying appropriate approaches. By advancing the effectiveness and privacy of malware classification systems, this research contributes to safeguarding against security threats posed by malicious software.Comment: Accepted in IEEE ICMLA-2023 Conferenc

Hardware Trojan Detection Using Machine Learning

The cyber-physical system’s security depends on the software and underlying hardware. In today’s times, securing hardware is difficult because of the globalization of the Integrated circuit’s manufacturing process. The main attack is to insert a “backdoor” that maliciously alters the original circuit’s behaviour. Such a malicious insertion is called a hardware trojan. In this thesis, the Random Forest Model has proposed for hardware trojan detection and this research focuses on improving the detection accuracy of the Random Forest model. The detection technique used the random forest machine learning model, which was trained by using the power traces of the circuit behaviour. The data required for training was obtained from an extensive database by simulating the circuit behaviours with various input vectors. The machine learning model was then compared with the state-of-art models in terms of accuracy in detecting malicious hardware. Our results show that the Random Forest classifier achieves an accuracy of 99.80 percent with a false positive rate (FPR)of 0.009 and a false negative rate (FNR) of 0.038 when the model is created to detect hardware trojans. Furthermore, our research shows that a trained model takes less training time and can be applied to large and complex datasets

Built-In Return-Oriented Programs in Embedded Systems and Deep Learning for Hardware Trojan Detection

Microcontrollers and integrated circuits in general have become ubiquitous in the world today. All aspects of our lives depend on them from driving to work, to calling our friends, to checking our bank account balance. People who would do harm to individuals, corporations and nation states are aware of this and for that reason they seek to find or create and exploit vulnerabilities in integrated circuits. This dissertation contains three papers dealing with these types of vulnerabilities. The first paper talks about a vulnerability that was found on a microcontroller, which is a type of integrated circuit. The final two papers deal with hardware trojans. Hardware trojans are purposely added to the design of an integrated circuit in secret so that the manufacturer doesn’t know about it. They are used to damage the integrated circuit, leak confidential information, or in other ways alter the circuit. Hardware trojans are a major concern for anyone using integrated circuits because an attacker can alter a circuit in almost any way if they are successful in inserting one. A known method to prevent hardware trojan insertion is discussed and a type of circuit for which this method does not work is revealed. The discussion of hardware trojans is concluded with a new way to detect them before the integrated circuit is manufactured. Modern deep learning models are used to detect the portions of the hardware trojan called triggers that activate them