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

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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.
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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.
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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
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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
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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
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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
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