A Survey and Perspective on Artificial Intelligence for Security-Aware Electronic Design Automation

Koblah, David Selasi; Acharya, Rabin Yu; Capecci, Daniel E.; Dizon-Paradis, Olivia P.; Tajik, Shahin; Ganji, Fatemeh; Forte, Domenic

doi:10.1145/3563391

Cited by 9 publications

(2 citation statements)

References 223 publications

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“…Competitive learning, or clustering, is carried out during the training process. To enhance network performance, network parameters like the spread number and goal number are modified [31]. The vector product is created by multiplying the estimated distance between the input and weight vectors by the bias values.…”

Section: Resultsmentioning

confidence: 99%

Machine Learning Techniques to Evaluate the Approximation of Utilization Power in Circuits

M. P.,

Muthumanickam

2023

IJRITCC

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The need for products that are more streamlined, more useful, and have longer battery lives is rising in today's culture. More components are being integrated onto smaller, more complex chips in order to do this. The outcome is higher total power consumption as a result of increased power dissipation brought on by dynamic and static currents in integrated circuits (ICs). For effective power planning and the precise application of power pads and strips by floor plan engineers, estimating power dissipation at an early stage is essential. With more information about the design attributes, power estimation accuracy increases. For a variety of applications, including function approximation, regularization, noisy interpolation, classification, and density estimation, they offer a coherent framework. RBFNN training is also quicker than training multi-layer perceptron networks. RBFNN learning typically comprises of a linear supervised phase for computing weights, followed by an unsupervised phase for determining the centers and widths of the Gaussian basis functions. This study investigates several learning techniques for estimating the synaptic weights, widths, and centers of RBFNNs. In this study, RBF networks—a traditional family of supervised learning algorithms—are examined. Using centers found using k-means clustering and the square norm of the network coefficients, respectively, two popular regularization techniques are examined. It is demonstrated that each of these RBF techniques are capable of being rewritten as data-dependent kernels. Due to their adaptability and quicker training time when compared to multi-layer perceptron networks, RBFNNs present a compelling option to conventional neural network models. Along with experimental data, the research offers a theoretical analysis of these techniques, indicating competitive performance and a few advantages over traditional kernel techniques in terms of adaptability (ability to take into account unlabeled data) and computing complexity. The research also discusses current achievements in using soft k-means features for image identification and other tasks.

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Section: Resultsmentioning

confidence: 99%

Machine Learning Techniques to Evaluate the Approximation of Utilization Power in Circuits

M. P.,

Muthumanickam

2023

IJRITCC

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“…Machine learning, at its core, is an interdisciplinary field that intersects the boundaries of computer science, mathematics, and statistics. It aims to develop algorithms that enable computers to learn from data and make decisions based on it [85]. Rather than being explicitly programmed for a specific task, these algorithms leverage vast datasets to infer patterns and deduce rules, subsequently applying this acquired knowledge to new, unseen data.…”

Section: Introduction To Machine Learningmentioning

confidence: 99%

Overview of Mobile Attack Detection and Prevention Techniques Using Machine Learning

Al Hwaitat,

Fakhouri,

Alawida

et al. 2024

Int. J. Interact. Mob. Technol.

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In light of the increasing sophistication and frequency of mobile attacks, there is a growing demand for advanced intelligent techniques capable of offering comprehensive mobile attack detection and prevention. This paper aims to critically evaluate the landscape of mobile security, outlining the evolution of mobile attack vectors and pinpointing the deficiencies in traditional security methods. The text embarks on a journey to understand the connection between machine learning (ML) and its promising applications in enhancing mobile security. First, we outline the current state of mobile attacks and the traditional methods used for their detection, emphasizing the clear limitations and the necessity for an innovative approach. Following this, we will elucidate the fundamentals of ML and its implications in cybersecurity, exploring the benefits it can provide to mobile attack detection frameworks. We delve into discussing various ML algorithms, such as decision trees, random forests, and support vector machines, highlighting their effectiveness and the metrics used to evaluate ML models in security tasks. Moreover, the paper sheds light on novel approaches such as semi-supervised and unsupervised learning in anomaly detection, as well as the applications of transfer learning in security. Addressing the pressing challenges faced in artificial intelligence (AI)-driven mobile attack detection, we delve deep into the intricacies of data collection, labeling, and the prevailing issues of imbalance and overfitting. Furthermore, we explore contemporary adversarial attacks and defenses, scrutinizing the real-world adaptability of AI models and the pivotal role of human-AI collaboration in enhancing attack detection mechanisms.

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Power Analysis Side-Channel Attacks on Same and Cross-Device Settings: A Survey of Machine Learning Techniques

Ghimire,

Baligodugula,

Amsaad

2023

Internet of Things. Advances in Information and Communication Technology

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A Survey and Perspective on Artificial Intelligence for Security-Aware Electronic Design Automation

Cited by 9 publications

References 223 publications

Machine Learning Techniques to Evaluate the Approximation of Utilization Power in Circuits

Machine Learning Techniques to Evaluate the Approximation of Utilization Power in Circuits

Overview of Mobile Attack Detection and Prevention Techniques Using Machine Learning

Power Analysis Side-Channel Attacks on Same and Cross-Device Settings: A Survey of Machine Learning Techniques

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