Titlebook: Applied Cryptography and Network Security Workshops; ACNS 2024 Satellite Martin Andreoni Conference proceedings 2024 The Editor(s) (if app

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Introduction

豐滿中國(guó)

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

大喘氣

種屬關(guān)系

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

Firefly

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

偽證

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

TIA742

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

護(hù)身符

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

詞根詞綴法

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