Side-channel attacks exploit the physical leakages from hardware components, such as power consumption, to break secure cryptographic algorithms and retrieve their secret key. Evaluating implementations of cryptograph...
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ISBN:
(数字)9783982674100
ISBN:
(纸本)9798331534646
Side-channel attacks exploit the physical leakages from hardware components, such as power consumption, to break secure cryptographic algorithms and retrieve their secret key. Evaluating implementations of cryptographic algorithms against such analysis is crucial but traditional frameworks require expensive external devices like oscilloscopes, making the process expensive and time-consuming. Recent advancements in on-chip sensors offer a cost-effective, fully on-chip SCA framework, eliminating the need for external devices. In this paper, we propose Raven, an on-chip SCA framework with hardware implementations of Test Vector Leakage Assessment (TVLA), Correlation Power Analysis (CPA), and Deep Learningbased Leakage Assessment (DL-LA), for run-time evaluation of cryptographic implementations. RAVEN leverages on-chip sensors to efficiently assess side-channel security, without requiring any external measurement devices or any customized evaluation platform. Our proposed hardware implementations of TVLA, CPA, and DL-LA are lightweight and the entire architecture including the sensors can fit within the lightweight and low-cost AMD-Xilinx PYNQ FPGA platform. The proposed framework is verified on an FPGA implementation of AES-128 and the corresponding result of TVLA, CPA, and DL-LA closely matches with these algorithm's software implementation while requiring significantly less time and storage.
As the complexity of cyberattacks continues to increase, traditional static defense strategies have shown significant limitations. This study proposes an intelligent cybersecurity defense framework based on large lang...
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ISBN:
(数字)9798331520298
ISBN:
(纸本)9798331520304
As the complexity of cyberattacks continues to increase, traditional static defense strategies have shown significant limitations. This study proposes an intelligent cybersecurity defense framework based on large language model Agents, and conducts a comparative analysis of the effectiveness of various leading models in the cybersecurity defense domain. By developing an attack behavior model, designing an Agent decision-making mechanism, and constructing a defense strategy execution and evaluation system, we assess the defense performance of models such as GPT-4.5, Claude 3.7, LLaMA 3.3, and Gemini 2.0 under different attack scenarios. The experimental results demonstrate that defense strategies based on LLM Agents outperform traditional methods in terms of attack detection rate, response time, and false alarm rate. Furthermore, each model exhibits unique advantages in specific security contexts. This research provides new insights for the development of more adaptive and efficient cybersecurity defense systems.
Path-based Testing is a common technique to test System Under Test (SUT) processes. Generally, a directed graph that models a system’s workflow is input to the test path generation process, as well as the selected te...
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ISBN:
(数字)9798331534677
ISBN:
(纸本)9798331534684
Path-based Testing is a common technique to test System Under Test (SUT) processes. Generally, a directed graph that models a system’s workflow is input to the test path generation process, as well as the selected test coverage criterion. Several algorithms are proposed in the literature that traverse the graph and facilitate the generation of test cases for the selected coverage criterion. However, a plain directed graph used for modeling SUT processes does not allow for the capture of real-life dependencies and constraints between actions in the tested processes, which might pose a limit in application of this technique. Therefore, we defined an extended model that allows the specification of constraints upon the graph’s elements and a set of algorithms that allow the generation of the set of test cases that satisfy the given constraints with the edge coverage. Considering the fact that in path-based testing, there is no platform in which engineers and researchers can share SUT models to be further assembled into open datasets to test performance of evolved path-based testing MBT algorithms, especially for the given problem of test paths generation with the constraints, this paper presents a summary of the problem and a novel management for creation and management of SUT models with constraints that allows the generation of test paths as well as to serve as a platform for creation of such benchmark datasets.
MPPT techniques are fundamental for boosting the efficiency of photovoltaic arrays under varying climatic situations. This work evaluates the performance of the Grey Wolf Optimizer (GWO) strategy, and the classic Pert...
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ISBN:
(数字)9798331532970
ISBN:
(纸本)9798331532987
MPPT techniques are fundamental for boosting the efficiency of photovoltaic arrays under varying climatic situations. This work evaluates the performance of the Grey Wolf Optimizer (GWO) strategy, and the classic Perturb and Observe (P&O) technique with step sizes of 0.001 and 0.008 in partially shaded PV systems, where shading complicates the tracking process by introducing multiple local maxima. We use MATLAB/Simulink environment to model and simulate the system using different controllers. Simulation results indicate that the GWO strategy identifies the global maximum power point (GMPP) with higher accuracy and effectiveness under dynamic shading conditions and temperature variations. In contrast, the performance of the P&O strategy is highly related to the chosen step size, which should be selected optimally to balance accuracy, stability, and convergence time. In conclusion, GWO is confirmed to be a more robust and reliable method than P&O, with higher efficiency, especially in challenging irradiance scenarios.
Within the rapidly shifting field of drug discovery, ML and DL techniques have opened new directions for identifying potential therapeutic compounds with higher efficiency and accuracy. The present work explores the i...
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ISBN:
(数字)9798331519582
ISBN:
(纸本)9798331519599
Within the rapidly shifting field of drug discovery, ML and DL techniques have opened new directions for identifying potential therapeutic compounds with higher efficiency and accuracy. The present work explores the integration of ML and DL algorithms for drug discovery in apoptosis, by utilizing PaDEL and Lipinski descriptors for feature extraction. PaDEL is widely used open-source software that helps in generating the molecular descriptors and fingerprints of a chemical compound while Lipinski's descriptors depend on the “Rule of Five” which explores critical analysis of drug-likeness of molecules. The present article provides a comparative analysis of different ML and DL algorithms which include SVM, RF, GBM, KNN, ANN, and GNN. The performance of each one is evaluated based on key metrics such as adjustment of r2 score and mean square error. This presented work shall help in predicting the chemical compounds at the early stage of drug discovery using the QSAR method. Computed results are depicted graphically.
Supercomputers have significantly transformed capabilities in data processing and analysis across various sectors, including finance, where they facilitate complex trading simulations and predictive modeling. This pap...
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ISBN:
(数字)9798331517649
ISBN:
(纸本)9798331517656
Supercomputers have significantly transformed capabilities in data processing and analysis across various sectors, including finance, where they facilitate complex trading simulations and predictive modeling. This paper examines the application of the LUMI supercomputer to enhance the performance of trading algorithms within the PAID-T framework. PAID-T, a sophisticated trading software, necessitates running extensive simulations to optimize trading strategies. Given the computational demands, executing approximately 1.2 million simulations was achieved through LUMI’s powerful infrastructure. The paper details the necessary adaptations for HPC execution, compares performance metrics between local PC and supercomputer, and discusses the challenges encountered during deployment. Our results highlight the remarkable efficiency gains realized by leveraging LUMI’s capabilities, reducing execution time significantly and achieving real-time processing of vast market data sets. The insights drawn underscore the potential of HPC systems in advancing algorithmic trading performance and strategy optimization.
In this paper, a fast dynamic and chattering-free control strategy called super-twisting controller (STC) is introduced. The STC controller generates a proper input signal to the bidirectional dual input single output...
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ISBN:
(数字)9798331525132
ISBN:
(纸本)9798331525149
In this paper, a fast dynamic and chattering-free control strategy called super-twisting controller (STC) is introduced. The STC controller generates a proper input signal to the bidirectional dual input single output (BDISO) DC-DC converter by means of pulse width modulation (PWM) technique. There are several ports in this BDISO converter, including, a DC power source port, an energy storage port, and an output port. The particular consideration about the design of the STC controller is to force the controlled states to seek their desired values with the fastest dynamic possible along with the lowest over/undershoot in various modes, including buck, boost, and bidirectional mode. Through simulation outputs, the effectiveness of the introduced control methodology is evaluated, and its performance is compared to that of a conventionally well-tuned proportional-integral (PI) and a fixed-frequency sliding mode (FSMC) controller by means of MATLAB/SIMULINK software.
The need for performance and energy efficiency by expanding technologies such as internet-of-things devices and artificial neural networks (ANNs) has led to the exploration of in-memory computing paradigms, specifical...
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ISBN:
(数字)9798331509422
ISBN:
(纸本)9798331509439
The need for performance and energy efficiency by expanding technologies such as internet-of-things devices and artificial neural networks (ANNs) has led to the exploration of in-memory computing paradigms, specifically utilizing resistive-switching memory (RSM) based analog and multilevel matrix-vector multipliers (MVMs). However, nonidealities in these MVMs cause larger-than-expected deterioration in the output quality and introduce errors with potentially catastrophic consequences in safety-critical applications such as autonomous vehicles, med-ical diagnosis, and control systems. Therefore, to enable the use of MVMs in such applications, the error bounds of the MVMs must be formally verified, which, to our knowledge, remains unaddressed. In this paper, we aim to address this gap with a formal verification methodology for finding the maximum possible error in resistive-switching-based multilevel MVMs. We introduce three approaches to compute the maximum error and provide a polynomial-time solution as our primary contribution, which reduces the computation time by up to 2181 times. Additionally, we provide a tracing feature for error source identification and debugging, enabling targeted enhancements of the design. We demonstrate the methodology's efficiency with a timing analysis and its effectiveness through a case study using the metrics of a fabricated design from the literature. We made the source code of our software implementation publicly available to promote further research in the use of multilevel MVMs in safety-critical applications.
Reverse engineering (RE) is often used in security-critical applications to determine the structure and functionality of various systems, including printed circuit boards (PCBs). Although it has both beneficial and ma...
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ISBN:
(数字)9798331509422
ISBN:
(纸本)9798331509439
Reverse engineering (RE) is often used in security-critical applications to determine the structure and functionality of various systems, including printed circuit boards (PCBs). Although it has both beneficial and malicious uses, it is particularly vital within the realm of hardware trust and assurance. PCB RE enhances legacy electronic system replacement, intellectual property (IP) protection, and supply chain integrity. To contribute to the requirements of effective PCB RE, extensive research has been conducted on the analysis of PCBs using X-ray computed tomography (CT) scans, including image segmentation focusing on via and trace annotation. Applying extracted annotations, this work outlines a Python-based framework, coupled with the open-source KiCaD software, for the automated reconstruction of PCB design files. Given the via, pad and trace annotations, in addition to board dimensions, the algorithm automatically recognizes board shape, trace size, and connections to reconstruct the bare PCB accurately. This technique was tested on three distinct layers of a sample multilayer PCB with great success. Its feasibility holds great promise for future extensions to complete the entire PCB RE framework. The project source code is available for reference on GitHub. 1 1 https://***/koblahdavid/xray_ct_reconstruction
Existing digital watermarking techniques for vector geographic data based on spatio-temporal domains face problems such as high computational complexity, low embedding capacity of effective watermark information, and ...
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ISBN:
(数字)9798331520298
ISBN:
(纸本)9798331520304
Existing digital watermarking techniques for vector geographic data based on spatio-temporal domains face problems such as high computational complexity, low embedding capacity of effective watermark information, and low security of watermark storage. To cope with these challenges, a digital watermarking scheme based on quick response code (QR code) and blockchain is proposed. In this scheme, the QR code is used as a carrier for the real watermark information, which increases the payload of the watermark. The authentic watermark is stored using blockchain and proprietary file system (IPFS) technology, and the hash index value returned by the blockchain is embedded into the vector map after folding, which alleviates the security problem of centralized storage to a certain extent. When a watermark verification or modification event occurs, the blockchain records user identity and timestamp information. Experimental results show that the scheme is robust to attacks such as data cropping, compression and geometric transformation, and can effectively extract correct watermark data.
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