Cryptanalysis of the SIMON Cypher Using Neo4j

• URL: http://arxiv.org/abs/2405.04735v2
• Date: Thu, 10 Oct 2024 05:15:06 GMT
• Title: Cryptanalysis of the SIMON Cypher Using Neo4j
• Authors: Jonathan Cook, Sabih ur Rehman, M. Arif Khan,
• Abstract summary: This paper introduces the novel use of knowledge graphs to identify intricate relationships between differentials in the SIMON LEA. It allows the identification of optimal paths throughout the differentials, and increasing the effectiveness of the differential security analyses of SIMON.
• Score: 0.0
• License:
• Abstract: The exponential growth in the number of Internet of Things (IoT) devices has seen the introduction of several Lightweight Encryption Algorithms (LEA). While LEAs are designed to enhance the integrity, privacy and security of data collected and transmitted by IoT devices, it is hazardous to assume that all LEAs are secure and exhibit similar levels of protection. To improve encryption strength, cryptanalysts and algorithm designers routinely probe LEAs using various cryptanalysis techniques to identify vulnerabilities and limitations of LEAs. Despite recent improvements in the efficiency of cryptanalysis utilising heuristic methods and a Partial Difference Distribution Table (PDDT), the process remains inefficient, with the random nature of the heuristic inhibiting reproducible results. However, the use of a PDDT presents opportunities to identify relationships between differentials utilising knowledge graphs, leading to the identification of efficient paths throughout the PDDT. This paper introduces the novel use of knowledge graphs to identify intricate relationships between differentials in the SIMON LEA, allowing for the identification of optimal paths throughout the differentials, and increasing the effectiveness of the differential security analyses of SIMON.

Related papers

• A Theoretical Perspective for Speculative Decoding Algorithm [60.79447486066416]
  One effective way to accelerate inference is emphSpeculative Decoding, which employs a small model to sample a sequence of draft tokens and a large model to validate. This paper tackles this gap by conceptualizing the decoding problem via markov chain abstraction and studying the key properties, emphoutput quality and inference acceleration, from a theoretical perspective.
  arXiv  Detail & Related papers  (2024-10-30T01:53:04Z)
• Binary Code Similarity Detection via Graph Contrastive Learning on Intermediate Representations [52.34030226129628]
  Binary Code Similarity Detection (BCSD) plays a crucial role in numerous fields, including vulnerability detection, malware analysis, and code reuse identification. In this paper, we propose IRBinDiff, which mitigates compilation differences by leveraging LLVM-IR with higher-level semantic abstraction. Our extensive experiments, conducted under varied compilation settings, demonstrate that IRBinDiff outperforms other leading BCSD methods in both One-to-one comparison and One-to-many search scenarios.
  arXiv  Detail & Related papers  (2024-10-24T09:09:20Z)
• Unlocking Potential Binders: Multimodal Pretraining DEL-Fusion for Denoising DNA-Encoded Libraries [51.72836644350993]
  Multimodal Pretraining DEL-Fusion model (MPDF) We develop pretraining tasks applying contrastive objectives between different compound representations and their text descriptions. We propose a novel DEL-fusion framework that amalgamates compound information at the atomic, submolecular, and molecular levels.
  arXiv  Detail & Related papers  (2024-09-07T17:32:21Z)
• Con-ReCall: Detecting Pre-training Data in LLMs via Contrastive Decoding [118.75567341513897]
  Existing methods typically analyze target text in isolation or solely with non-member contexts. We propose Con-ReCall, a novel approach that leverages the asymmetric distributional shifts induced by member and non-member contexts.
  arXiv  Detail & Related papers  (2024-09-05T09:10:38Z)
• Deep Learning and Chaos: A combined Approach To Image Encryption and Decryption [1.8749305679160366]
  We introduce a novel image encryption and decryption algorithm using hyperchaotic signals from the novel 3D hyperchaotic map, 2D memristor map, Convolutional Neural Network (CNN) The robustness of the encryption algorithm is shown by key sensitivity analysis, i.e., the average sensitivity of the algorithm to key elements.
  arXiv  Detail & Related papers  (2024-06-24T16:56:22Z)
• Evaluating the Security of Merkle Trees in the Internet of Things: An Analysis of Data Falsification Probabilities [27.541105686358378]
  This paper develops a theoretical framework to calculate the probability of data falsification, taking into account various scenarios based on the length of the Merkle path and hash length. Empirical experiments validate the theoretical models, exploring simulations with diverse hash lengths and Merkle path lengths. The findings reveal a decrease in falsification probability with increasing hash length and an inverse relationship with longer Merkle paths.
  arXiv  Detail & Related papers  (2024-04-18T11:24:12Z)
• Lightweight Cryptanalysis of IoT Encryption Algorithms : Is Quota Sampling the Answer? [0.0]
  Two well-known lightweight algorithms are SIMON and SIMECK which have been specifically designed for use on resource-constrained IoT devices. It is necessary to test these algorithms for resilience against differential cryptanalysis attacks. In this paper, we introduce Versatile Investigative Sampling Technique for Advanced Cryptanalysis.
  arXiv  Detail & Related papers  (2024-04-12T00:08:39Z)
• 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)
• Active RIS-aided EH-NOMA Networks: A Deep Reinforcement Learning Approach [66.53364438507208]
  An active reconfigurable intelligent surface (RIS)-aided multi-user downlink communication system is investigated. Non-orthogonal multiple access (NOMA) is employed to improve spectral efficiency, and the active RIS is powered by energy harvesting (EH) An advanced LSTM based algorithm is developed to predict users' dynamic communication state. A DDPG based algorithm is proposed to joint control the amplification matrix and phase shift matrix RIS.
  arXiv  Detail & Related papers  (2023-04-11T13:16:28Z)
• The Wyner Variational Autoencoder for Unsupervised Multi-Layer Wireless Fingerprinting [6.632671046812309]
  We propose a multi-layer fingerprinting framework that jointly considers the multi-layer signatures for improved identification performance. In contrast to previous works, by leveraging the recent multi-view machine learning paradigm, our method can cluster the device information shared among the multi-layer features without supervision. Our empirical results show that the proposed method outperforms the state-of-the-art baselines in both supervised and unsupervised settings.
  arXiv  Detail & Related papers  (2023-03-28T10:05:06Z)
• Hierarchical Deep CNN Feature Set-Based Representation Learning for Robust Cross-Resolution Face Recognition [59.29808528182607]
  Cross-resolution face recognition (CRFR) is important in intelligent surveillance and biometric forensics. Existing shallow learning-based and deep learning-based methods focus on mapping the HR-LR face pairs into a joint feature space. In this study, we desire to fully exploit the multi-level deep convolutional neural network (CNN) feature set for robust CRFR.
  arXiv  Detail & Related papers  (2021-03-25T14:03:42Z)

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.