標(biāo)題: Titlebook: Applied Cryptography and Network Security Workshops; ACNS 2024 Satellite Martin Andreoni Conference proceedings 2024 The Editor(s) (if app [打印本頁(yè)] 作者: 生動(dòng) 時(shí)間: 2025-3-21 17:33
書目名稱Applied Cryptography and Network Security Workshops影響因子(影響力)
書目名稱Applied Cryptography and Network Security Workshops影響因子(影響力)學(xué)科排名
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書目名稱Applied Cryptography and Network Security Workshops網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Applied Cryptography and Network Security Workshops被引頻次
書目名稱Applied Cryptography and Network Security Workshops被引頻次學(xué)科排名
書目名稱Applied Cryptography and Network Security Workshops年度引用
書目名稱Applied Cryptography and Network Security Workshops年度引用學(xué)科排名
書目名稱Applied Cryptography and Network Security Workshops讀者反饋
書目名稱Applied Cryptography and Network Security Workshops讀者反饋學(xué)科排名
作者: Introduction 時(shí)間: 2025-3-21 21:16 作者: 豐滿中國(guó) 時(shí)間: 2025-3-22 02:42
LM-cAPI:A Lite Model Based on?API Core Semantic Information for?Malware Classificationnew types and quantities of malware coupled with the continuous updating of dissemination methods, the rapid and accurate identification of malware as well as providing precise support for corresponding warning and defense measures have become a crucial challenge in maintaining network security. Thi作者: 大喘氣 時(shí)間: 2025-3-22 06:59 作者: 種屬關(guān)系 時(shí)間: 2025-3-22 10:48
FPGA Implementation of?Physically Unclonable Functions Based on?Multi-threshold Delay Time Measureme in semiconductor devices. Arbiter PUF is a typical extensive PUF that has a large space for challenge–response pairs (CPRs); however, it is vulnerable to deep learning (DL) attacks predicting unknown CRPs. One of the approaches to mitigate DL attacks is the RG-DTM PUF, which utilizes the delay time作者: Firefly 時(shí)間: 2025-3-22 14:54
Incorporating Cluster Analysis of?Feature Vectors for?Non-profiled Deep-learning-Based Side-Channel S 2019. In the proposed DDLA, the adversary sets the LSB or MSB of the intermediate value in the encryption process assumed for the key candidates as the ground-truth label and trains a deep neural network (DNN) with power traces as an input. The adversary also observes metrics such as loss and accu作者: 偽證 時(shí)間: 2025-3-22 18:36
Creating from?Noise: Trace Generations Using Diffusion Model for?Side-Channel Attackration of synthetic traces can help to improve attacks like profiling attacks. However, manually creating synthetic traces from actual traces is arduous. Therefore, automating this process of creating artificial traces is much needed. Recently, diffusion models have gained much recognition after bea作者: TIA742 時(shí)間: 2025-3-22 23:21
Diversity Algorithms for?Laser Fault Injection injection (FI). Within this process, FI aims to identify parameter combinations that reveal device vulnerabilities. The impracticality of conducting an exhaustive search over FI parameters has prompted the development of advanced and guided algorithms. However, these proposed methods often focus on作者: 護(hù)身符 時(shí)間: 2025-3-23 05:14
One for?All, All for?Ascon: Ensemble-Based Deep Learning Side-Channel Analysis well-known challenge of hyperparameter tuning in DLSCA encouraged the community to use methods that reduce the effort required to identify an optimal model. One of the successful methods is ensemble learning. While ensemble methods have demonstrated their effectiveness in DLSCA, particularly with A作者: 詞根詞綴法 時(shí)間: 2025-3-23 08:46
CNN Architecture Extraction on?Edge GPUlanguage processing, speech recognition, forecasting, etc. These applications are also used in resource-constrained environments such as embedded devices. In this work, the susceptibility of neural network implementations to reverse engineering is explored on the NVIDIA Jetson Nano microcomputer via作者: Affectation 時(shí)間: 2025-3-23 12:25 作者: glomeruli 時(shí)間: 2025-3-23 16:02
Everything All at?Once: Deep Learning Side-Channel Analysis Optimization Frameworknd speech recognition for decades, it is still lacking maturity for side-channel analysis. One of the challenges to train a good model is the fine-tuning of its hyperparameters. Many methods have been developed for Hyperparameter Optimization, but a few have been applied for deep learning side-chann作者: consolidate 時(shí)間: 2025-3-23 21:55
Device Fingerprinting in?a?Smart Grid CPSed in smart grid systems. These unobservable cyberattacks present a potentially dangerous threat to grid operations. Data integrity attacks that involve the compromise of various meter readings such as voltage and current levels can lead to threats ranging from trivial problems such as energy usage 作者: 巨碩 時(shí)間: 2025-3-23 22:44 作者: 有害 時(shí)間: 2025-3-24 02:53
Evaluation of?Lightweight Machine Learning-Based NIDS Techniques for?Industrial IoTthese devices has propelled the growth of Industry 4.0 to an exponential pace. However, while this vast pool of interconnected devices broadens the opportunities for better business and better lives, it also attracts the attention of cybercriminals. Nevertheless, it has been shown that the resource-作者: 忘川河 時(shí)間: 2025-3-24 07:16
Measuring Cyber Resilience of?IoT-Enabled Critical National Infrastructureson properly. CNI are increasingly being connected to the internet to improve operational efficiency and reduce costs. The adoption of the Industrial Internet of Things (IoT) introduced new attack vectors which have necessitated a need to build and improve cyber resilience in CNI. The quantification 作者: 冷淡周邊 時(shí)間: 2025-3-24 12:14 作者: Chronic 時(shí)間: 2025-3-24 17:34
https://doi.org/10.1007/978-3-662-49459-2res (EfficientNets, MobileNets, NasNet, etc.) are implemented on the GPU of Jetson Nano and the electromagnetic radiation of the GPU is analyzed during the inference operation of the neural networks. The results of the analysis show that neural network architectures are easily distinguishable using deep learning-based side-channel analysis.作者: Vaginismus 時(shí)間: 2025-3-24 19:54 作者: BARK 時(shí)間: 2025-3-25 01:12
https://doi.org/10.1007/978-3-662-49459-2Artix-7 and in a simulation and demonstrate its attack resistance against DL attacks. The experimental results show that the fDTM PUF achieves much higher attack resistance than the conventional Arbiter PUF with the equivalent area and achieves equivalent attack resistance to previous PUFs with areas around several to dozens of times smaller.作者: 有限 時(shí)間: 2025-3-25 06:47 作者: 可以任性 時(shí)間: 2025-3-25 09:36
Zur Tonsillektomie aus immunologischer Sicht on the global model and clients’ data. Comparative analysis of the ML model and DP parameters shows that the LSTM model gives better results with adequate privacy parameters to predict the PQPs of five distributed microgrids. LSTM model gives the least MAE of 0.2323 for FL without privacy and 0.3256 test loss for appropriate DP level.作者: 抱怨 時(shí)間: 2025-3-25 12:42 作者: 公豬 時(shí)間: 2025-3-25 17:29 作者: 不滿分子 時(shí)間: 2025-3-25 22:37 作者: 嬰兒 時(shí)間: 2025-3-26 03:51 作者: 爭(zhēng)論 時(shí)間: 2025-3-26 04:46
EasyLog: An Efficient Kernel Logging Service for?Machine Learninge ., ., and the . service. EasyLog extracts and records logs with special identifier suffixes by introducing a ring buffer. In terms of interface utilization, EasyLog offers the . interface for kernel developers and the reading interface for user-space applications.作者: 礦石 時(shí)間: 2025-3-26 11:27
0302-9743 Conference on Applied Cryptography and Network Security, ACNS 2024, held in Abhu Dabhi, United Arab Emirates, during March 5-8, 2024...The 33 full papers and 11 poster papers presented in this volume were carefully reviewed and selected from 62 submissions. They stem from the following workshops:..6作者: chronology 時(shí)間: 2025-3-26 15:04
https://doi.org/10.1007/978-3-662-49459-2e ., ., and the . service. EasyLog extracts and records logs with special identifier suffixes by introducing a ring buffer. In terms of interface utilization, EasyLog offers the . interface for kernel developers and the reading interface for user-space applications.作者: 殺人 時(shí)間: 2025-3-26 17:49
An End-to-End Secure Solution for?IoMT Data Exchange and privacy of sensitive IoMT data poses a significant challenge. In this context, one potential solution to ensure the confidentiality and integrity of medical data is the utilization of Blockchain technology. This paper explores the potential of Blockchain in IoMT networks, specifically focusing 作者: 現(xiàn)實(shí) 時(shí)間: 2025-3-26 22:52 作者: archetype 時(shí)間: 2025-3-27 02:56
Acki Nacki: A Probabilistic Proof-of-Stake Consensus Protocol with?Fast Finality and?Parallelisationpproach is separating the verification of execution by a consensus committee from the attestation of block propagation by network participants. Our consensus committee is randomly selected for each block and is not predetermined, while the Leader is deterministic.作者: 或者發(fā)神韻 時(shí)間: 2025-3-27 05:50
Incorporating Cluster Analysis of?Feature Vectors for?Non-profiled Deep-learning-Based Side-Channel as MSB or HW. We propose a new deep-learning-based SCA in a non-profiled scenario to solve these problems. Our core idea is to conduct dimensionality reduction on the leakage waveform using DNN. The adversary conducts cluster analysis using the feature vectors extracted from power traces using DNN.作者: 暗語(yǔ) 時(shí)間: 2025-3-27 11:11 作者: GUEER 時(shí)間: 2025-3-27 16:10 作者: Truculent 時(shí)間: 2025-3-27 17:48 作者: Fulsome 時(shí)間: 2025-3-28 00:10
Harnessing the?Power of?General-Purpose LLMs in?Hardware Trojan Designic module abstractions of hardware designs. By doing so, we tackle the challenges posed by the context length limit of LLMs, that become prevalent during LLM-based analyses of large code bases. Next, we initiate an LLM analysis of the reduced code base, that includes only the register transfer level作者: inscribe 時(shí)間: 2025-3-28 05:33
Device Fingerprinting in?a?Smart Grid CPSgs) is modeled through the use of machine learning techniques. Under a malicious spoofing attack, the noise pattern deviates from the fingerprinted pattern and hence enabling the proposed detection scheme to identify these attacks. A novel ensemble learning method is used to identify the Intelligent作者: 急急忙忙 時(shí)間: 2025-3-28 06:40
Evaluation of?Lightweight Machine Learning-Based NIDS Techniques for?Industrial IoTur implementations on the IoT-23 and TON_IoT datasets and compare the results in terms of classification performance, throughput and resource consumption. We show that tree-based models surpass the neural network-based models in classification performance and throughput but that hardware acceleratio作者: 周年紀(jì)念日 時(shí)間: 2025-3-28 10:53
Measuring Cyber Resilience of?IoT-Enabled Critical National Infrastructuresd that the performance of the system under an attack is dependent on the recovery time; hence, the higher the systemic impact, the lower the resilience of the CNI and vice versa. Quantifying the resilience of CNI is crucial to determining the security control defenses required to reduce the impact o作者: Matrimony 時(shí)間: 2025-3-28 16:51 作者: BLANK 時(shí)間: 2025-3-28 21:08 作者: 禮節(jié) 時(shí)間: 2025-3-29 01:35
https://doi.org/10.1007/978-3-662-49459-2 and privacy of sensitive IoMT data poses a significant challenge. In this context, one potential solution to ensure the confidentiality and integrity of medical data is the utilization of Blockchain technology. This paper explores the potential of Blockchain in IoMT networks, specifically focusing 作者: Nonconformist 時(shí)間: 2025-3-29 03:37
https://doi.org/10.1007/978-3-662-49459-2d redundant data in API call sequences, this model adopts an intimacy analysis method based on a self-attention mechanism for key information extraction. To enhance the capture of semantic information within malware API call sequences, a feature extraction model based on a self-attention mechanism i作者: 移植 時(shí)間: 2025-3-29 11:18
https://doi.org/10.1007/978-3-662-49459-2pproach is separating the verification of execution by a consensus committee from the attestation of block propagation by network participants. Our consensus committee is randomly selected for each block and is not predetermined, while the Leader is deterministic.作者: Fresco 時(shí)間: 2025-3-29 14:47 作者: HERE 時(shí)間: 2025-3-29 17:34
https://doi.org/10.1007/978-3-662-49459-2ficially created profiling data in the unknown mask setting can reduce the required attack traces for a profiling attack. This suggests that the artificially created profiling data from the trained diffusion model contains useful leakages to be exploited.作者: Armada 時(shí)間: 2025-3-29 20:53
https://doi.org/10.1007/978-3-662-49459-2volution Strategy (ES). Our findings reveal that these diversity methods, though identifying fewer vulnerabilities overall than the Memetic Algorithm (MA), still outperform Random Search (RS), identifying at least . more vulnerabilities. Using our novel metrics, we observe that the number of distinc作者: cardiovascular 時(shí)間: 2025-3-30 02:54
https://doi.org/10.1007/978-3-662-49459-2ttack two implementations of Ascon. Using an ensemble of five multilayer perceptrons or convolutional neural networks, we could find the secret key for the Ascon-protected implementation with less than 3?000 traces. To the best of our knowledge, this is the best currently known result. We can also i作者: 可商量 時(shí)間: 2025-3-30 07:23
https://doi.org/10.1007/978-3-662-49459-2ic module abstractions of hardware designs. By doing so, we tackle the challenges posed by the context length limit of LLMs, that become prevalent during LLM-based analyses of large code bases. Next, we initiate an LLM analysis of the reduced code base, that includes only the register transfer level作者: Digest 時(shí)間: 2025-3-30 08:21 作者: persistence 時(shí)間: 2025-3-30 12:23
https://doi.org/10.1007/978-3-642-71117-6ur implementations on the IoT-23 and TON_IoT datasets and compare the results in terms of classification performance, throughput and resource consumption. We show that tree-based models surpass the neural network-based models in classification performance and throughput but that hardware acceleratio作者: 痛苦一下 時(shí)間: 2025-3-30 17:05 作者: Tartar 時(shí)間: 2025-3-30 22:16 作者: PALMY 時(shí)間: 2025-3-31 01:25 作者: 集聚成團(tuán) 時(shí)間: 2025-3-31 05:59 作者: Minatory 時(shí)間: 2025-3-31 09:20
