Lightweight Cryptanalysis of IoT Encryption Algorithms : Is Quota Sampling the Answer?

• URL: http://arxiv.org/abs/2404.08165v1
• Date: Fri, 12 Apr 2024 00:08:39 GMT
• Title: Lightweight Cryptanalysis of IoT Encryption Algorithms : Is Quota Sampling the Answer?
• Authors: Jonathan Cook, Sabih ur Rehman, M. Arif Khan,
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Rapid growth in the number of small sensor devices known as the Internet of Things (IoT) has seen the development of lightweight encryption algorithms. Two well-known lightweight algorithms are SIMON and SIMECK which have been specifically designed for use on resource-constrained IoT devices. These lightweight encryption algorithms are based on the efficient Feistel block structure which is known to exhibit vulnerabilities to differential cryptanalysis. Consequently, it is necessary to test these algorithms for resilience against such attacks. While existing state-of-the-art research has demonstrated novel heuristic methods of differential cryptanalysis that improve time efficiency on previous techniques, the large state sizes of these encryption algorithms inhibit cryptanalysis time efficiency. In this paper, we introduce Versatile Investigative Sampling Technique for Advanced Cryptanalysis (VISTA-CRYPT) - a time-efficient enhancement of differential cryptanalysis of lightweight encryption algorithms. The proposed technique introduces a simple framework of quota sampling that produces state-of-the-art results with time reductions of up to $76\%$ over existing techniques. Further, we present a preliminary graph-based analysis of the output differentials for the identification of relationships within the data and future research opportunities to further enhance the performance of differential cryptanalysis. The code designed for this work and associated datasets will be available at https://github.com/johncook1979/simon-cryptanalysis.

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)
• Encrypted system identification as-a-service via reliable encrypted matrix inversion [0.0]
  Encrypted computation opens up promising avenues across a plethora of application domains. In particular, Arithmetic homomorphic encryption is a natural fit for cloud-based computational services. This paper presents an encrypted system identification service enabled by a reliable encrypted solution to at least squares problems.
  arXiv  Detail & Related papers  (2024-10-27T20:00:04Z)
• 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)
• Speed-up of Data Analysis with Kernel Trick in Encrypted Domain [2.592307869002029]
  Homomorphic encryption (HE) is pivotal for secure computation on encrypted data, crucial in privacy-preserving data analysis. We present an effective acceleration method using the kernel method for HE schemes, enhancing time performance in ML/STAT algorithms within encrypted domains.
  arXiv  Detail & Related papers  (2024-06-14T04:49:40Z)
• Cryptanalysis of the SIMON Cypher Using Neo4j [0.0]
  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.
  arXiv  Detail & Related papers  (2024-05-08T00:52:57Z)
• 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)
• SOCI^+: An Enhanced Toolkit for Secure OutsourcedComputation on Integers [50.608828039206365]
  We propose SOCI+ which significantly improves the performance of SOCI. SOCI+ employs a novel (2, 2)-threshold Paillier cryptosystem with fast encryption and decryption as its cryptographic primitive. Compared with SOCI, our experimental evaluation shows that SOCI+ is up to 5.4 times more efficient in computation and 40% less in communication overhead.
  arXiv  Detail & Related papers  (2023-09-27T05:19:32Z)
• A Variational Quantum Attack for AES-like Symmetric Cryptography [69.80357450216633]
  We propose a variational quantum attack algorithm (VQAA) for classical AES-like symmetric cryptography. In the VQAA, the known ciphertext is encoded as the ground state of a Hamiltonian that is constructed through a regular graph.
  arXiv  Detail & Related papers  (2022-05-07T03:15:15Z)
• Recovering AES Keys with a Deep Cold Boot Attack [91.22679787578438]
  Cold boot attacks inspect the corrupted random access memory soon after the power has been shut down. In this work, we combine a novel cryptographic variant of a deep error correcting code technique with a modified SAT solver scheme to apply the attack on AES keys. Our results show that our methods outperform the state of the art attack methods by a very large margin.
  arXiv  Detail & Related papers  (2021-06-09T07:57:01Z)
• Bayesian Optimization with Machine Learning Algorithms Towards Anomaly Detection [66.05992706105224]
  In this paper, an effective anomaly detection framework is proposed utilizing Bayesian Optimization technique. The performance of the considered algorithms is evaluated using the ISCX 2012 dataset. Experimental results show the effectiveness of the proposed framework in term of accuracy rate, precision, low-false alarm rate, and recall.
  arXiv  Detail & Related papers  (2020-08-05T19:29:35Z)
• Faster Secure Data Mining via Distributed Homomorphic Encryption [108.77460689459247]
  Homomorphic Encryption (HE) is receiving more and more attention recently for its capability to do computations over the encrypted field. We propose a novel general distributed HE-based data mining framework towards one step of solving the scaling problem. We verify the efficiency and effectiveness of our new framework by testing over various data mining algorithms and benchmark data-sets.
  arXiv  Detail & Related papers  (2020-06-17T18:14:30Z)

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.