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Survey#Cybersecurity, Power Side-Channel Attacks, Security🔬 ResearchAnalyzed: Jan 3, 2026 18:35

Application-Specific Power Side-Channel Attacks and Countermeasures Survey

Published:Dec 29, 2025 17:13
1 min read
ArXiv

Analysis

This survey paper is important because it moves beyond the traditional focus on cryptographic implementations in power side-channel attacks. It explores the application of these attacks and countermeasures in diverse domains like machine learning, user behavior analysis, and instruction-level disassembly, highlighting the broader implications of power analysis in cybersecurity.
Reference

“This survey aims to classify recent power side-channel attacks and provide a comprehensive comparison based on application-specific considerations.”

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Research Paper#Cryptography, Post-Quantum Security, Side-Channel Attacks🔬 ResearchAnalyzed: Jan 4, 2026 00:00

Statistical Risk Model for Timing Variability in Post-Quantum Cryptography

Published:Dec 26, 2025 03:12
1 min read
ArXiv

Analysis

This paper addresses a critical security concern in post-quantum cryptography: timing side-channel attacks. It proposes a statistical model to assess the risk of timing leakage in lattice-based schemes, which are vulnerable due to their complex arithmetic and control flow. The research is important because it provides a method to evaluate and compare the security of different lattice-based Key Encapsulation Mechanisms (KEMs) early in the design phase, before platform-specific validation. This allows for proactive security improvements.
Reference

“The paper finds that idle conditions generally have the best distinguishability, while jitter and loaded conditions erode distinguishability. Cache-index and branch-style leakage tends to give the highest risk signals.”

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Research#cryptography🔬 ResearchAnalyzed: Jan 4, 2026 10:38

Machine Learning Power Side-Channel Attack on SNOW-V

Published:Dec 25, 2025 16:55
1 min read
ArXiv

Analysis

This article likely discusses a security vulnerability in the SNOW-V encryption algorithm. The use of machine learning suggests an advanced attack technique that analyzes power consumption patterns to extract secret keys. The source, ArXiv, indicates this is a research paper, suggesting a novel finding in the field of cryptography and side-channel analysis.
Reference

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Research#security🔬 ResearchAnalyzed: Jan 4, 2026 09:08

Power Side-Channel Analysis of the CVA6 RISC-V Core at the RTL Level Using VeriSide

Published:Dec 23, 2025 10:41
1 min read
ArXiv

Analysis

This article likely presents a research paper on the security analysis of a RISC-V processor core (CVA6) using power side-channel attacks. The focus is on analyzing the core at the Register Transfer Level (RTL) using a tool called VeriSide. This suggests an investigation into vulnerabilities related to power consumption patterns during the execution of instructions, potentially revealing sensitive information.
Reference

“The article is likely a technical paper, so specific quotes would depend on the paper's content. A potential quote might be related to the effectiveness of VeriSide or the specific vulnerabilities discovered.”

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Research#llm🔬 ResearchAnalyzed: Jan 4, 2026 12:00

PermuteV: A Performant Side-channel-Resistant RISC-V Core Securing Edge AI Inference

Published:Dec 19, 2025 23:31
1 min read
ArXiv

Analysis

This article introduces PermuteV, a RISC-V core designed for secure edge AI inference. The focus is on side-channel resistance, which is crucial for protecting sensitive data during AI processing at the edge. The performance aspect suggests an attempt to balance security with efficiency, a common challenge in embedded systems.
Reference

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Research#Federated Learning🔬 ResearchAnalyzed: Jan 10, 2026 12:07

FLARE: Wireless Side-Channel Fingerprinting Attack on Federated Learning

Published:Dec 11, 2025 05:32
1 min read
ArXiv

Analysis

This research paper details a novel attack that exploits wireless side-channels to fingerprint federated learning models, raising serious concerns about the security of collaborative AI. The findings highlight the vulnerability of federated learning to privacy breaches, especially in wireless environments.
Reference

“The paper is sourced from ArXiv.”

PermalinkArXiv