Systematic Literature Review of EM-SCA Attacks on Encryption

• URL: http://arxiv.org/abs/2402.10030v1
• Date: Thu, 15 Feb 2024 15:53:46 GMT
• Title: Systematic Literature Review of EM-SCA Attacks on Encryption
• Authors: Muhammad Rusyaidi Zunaidi, Asanka Sayakkara, Mark Scanlon,
• Abstract summary: Side-channel attacks (SCAs) pose a significant threat to cryptographic integrity, compromising device keys. EM-SCA gathers information by monitoring EM radiation, capable of retrieving encryption keys and detecting malicious activity. This study evaluates EM-SCA's impact on encryption across scenarios and explores its role in digital forensics and law enforcement.
• Score: 0.1433758865948252
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Cryptography is vital for data security, but cryptographic algorithms can still be vulnerable to side-channel attacks (SCAs), physical assaults exploiting power consumption and EM radiation. SCAs pose a significant threat to cryptographic integrity, compromising device keys. While literature on SCAs focuses on real-world devices, the rise of sophisticated devices necessitates fresh approaches. Electromagnetic side-channel analysis (EM-SCA) gathers information by monitoring EM radiation, capable of retrieving encryption keys and detecting malicious activity. This study evaluates EM-SCA's impact on encryption across scenarios and explores its role in digital forensics and law enforcement. Addressing encryption susceptibility to EM-SCA can empower forensic investigators in overcoming encryption challenges, maintaining their crucial role in law enforcement. Additionally, the paper defines EM-SCA's current state in attacking encryption, highlighting vulnerable and resistant encryption algorithms and devices, and promising EM-SCA approaches. This study offers a comprehensive analysis of EM-SCA in law enforcement and digital forensics, suggesting avenues for further research.

Related papers

• Post-Quantum Secure UE-to-UE Communications [1.0230631028817565]
  This demo paper proposes the integration of Post-Quantum Cryptography (PQC) in TLS for UE Communication to mitigate the risks of quantum attacks. By addressing the implementation of PQC within a 5G network to secure UE-to-UE communication, this research aims to pave the way for developing quantum-resistant mobile devices.
  arXiv  Detail & Related papers  (2024-08-20T18:19:39Z)
• A Security Assessment tool for Quantum Threat Analysis [34.94301200620856]
  The rapid advancement of quantum computing poses a significant threat to many current security algorithms used for secure communication, digital authentication, and information encryption. A sufficiently powerful quantum computer could potentially exploit vulnerabilities in these algorithms, rendering data in insecure transit. This work developed a quantum assessment tool for organizations, providing tailored recommendations for transitioning their security protocols into a post-quantum world.
  arXiv  Detail & Related papers  (2024-07-18T13:58:34Z)
• Physical Layer Deception with Non-Orthogonal Multiplexing [52.11755709248891]
  We propose a novel framework of physical layer deception (PLD) to actively counteract wiretapping attempts. PLD combines PLS with deception technologies to actively counteract wiretapping attempts. We prove the validity of the PLD framework with in-depth analyses and demonstrate its superiority over conventional PLS approaches.
  arXiv  Detail & Related papers  (2024-06-30T16:17:39Z)
• Post-Quantum Cryptography: Securing Digital Communication in the Quantum Era [0.0]
  Post-quantum cryptography (PQC) is a critical field aimed at developing resilient cryptographic algorithms to quantum attacks. This paper delineates the vulnerabilities of classical cryptographic systems to quantum attacks, elucidates impervious principles of quantum computing, and introduces various PQC algorithms.
  arXiv  Detail & Related papers  (2024-03-18T12:51:56Z)
• Cryptanalysis and improvement of multimodal data encryption by machine-learning-based system [0.0]
  encryption algorithms to accommodate varied requirements of this field. Best approach to analyzing an encryption algorithm is to identify a practical and efficient technique to break it.
  arXiv  Detail & Related papers  (2024-02-24T10:02:21Z)
• Purple Llama CyberSecEval: A Secure Coding Benchmark for Language Models [41.068780235482514]
  This paper presents CyberSecEval, a comprehensive benchmark developed to help bolster the cybersecurity of Large Language Models (LLMs) employed as coding assistants. CyberSecEval provides a thorough evaluation of LLMs in two crucial security domains: their propensity to generate insecure code and their level of compliance when asked to assist in cyberattacks.
  arXiv  Detail & Related papers  (2023-12-07T22:07:54Z)
• CRYPTO-MINE: Cryptanalysis via Mutual Information Neural Estimation [42.481750913003204]
  Mutual Information (MI) is a measure to evaluate the efficiency of cryptosystems. Recent advances in machine learning have enabled progress in estimating MI using neural networks. This work presents a novel application of MI estimation in the field of cryptography.
  arXiv  Detail & Related papers  (2023-09-14T20:30:04Z)
• NEQRX: Efficient Quantum Image Encryption with Reduced Circuit Complexity [2.7985570786346745]
  We propose an efficient implementation scheme for a quantum image encryption algorithm combining the generalized affine transform and logistic map. We achieve a remarkable 50% reduction in cost while maintaining security and efficiency.
  arXiv  Detail & Related papers  (2022-04-14T10:15:23Z)
• EEG-Based Brain-Computer Interfaces Are Vulnerable to Backdoor Attacks [68.01125081367428]
  Recent studies have shown that machine learning algorithms are vulnerable to adversarial attacks. This article proposes to use narrow period pulse for poisoning attack of EEG-based BCIs, which is implementable in practice and has never been considered before.
  arXiv  Detail & Related papers  (2020-10-30T20:49:42Z)
• Dos and Don'ts of Machine Learning in Computer Security [74.1816306998445]
  Despite great potential, machine learning in security is prone to subtle pitfalls that undermine its performance. We identify common pitfalls in the design, implementation, and evaluation of learning-based security systems. We propose actionable recommendations to support researchers in avoiding or mitigating the pitfalls where possible.
  arXiv  Detail & Related papers  (2020-10-19T13:09:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.