Identifying Encryption Algorithms in ECB and CBC Modes Using Computational Intelligence

Mello, Fernando Machado de; Xexeo, Jose Antonio Moreira

doi:10.3217/jucs-024-01-0025

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…After analyzing the results, they found that the setting of the key has a certain impact on the identification accuracy. Mello & Xexéo (2018) analyzed the ciphertexts encrypted by seven cryptographic algorithms of ARC4, Blowfish, DES, Rijdael, RSA, Serpent, and Twofish in both ECB and CBC modes, and they found these algorithms almost all can be fully identified in ECB mode.…”

Section: Related Workmentioning

confidence: 99%

A block cipher algorithm identification scheme based on hybrid k-nearest neighbor and random forest algorithm

Yuan

Feng

et al. 2022

PeerJ Computer Science

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Cryptographic algorithm identification, which refers to analyzing and identifying the encryption algorithm used in cryptographic system, is of great significance to cryptanalysis. In order to improve the accuracy of identification work, this article proposes a new ensemble learning-based model named hybrid k-nearest neighbor and random forest (HKNNRF), and constructs a block cipher algorithm identification scheme. In the ciphertext-only scenario, we use NIST randomness test methods to extract ciphertext features, and carry out binary-classification and five-classification experiments on the block cipher algorithms using proposed scheme. Experiments show that when the ciphertext size and other experimental conditions are the same, compared with the baselines, the HKNNRF model has higher classification accuracy. Specifically, the average binary-classification identification accuracy of HKNNRF is 69.5%, which is 13%, 12.5%, and 10% higher than the single-layer support vector machine (SVM), k-nearest neighbor (KNN), and random forest (RF) respectively. The five-classification identification accuracy can reach 34%, which is higher than the 21% accuracy of KNN, the 22% accuracy of RF and the 23% accuracy of SVM respectively under the same experimental conditions.

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Section: Related Workmentioning

confidence: 99%

A block cipher algorithm identification scheme based on hybrid k-nearest neighbor and random forest algorithm

Yuan

Feng

et al. 2022

PeerJ Computer Science

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Zoombombing – the end-to-end fallacy

Secara¹

2020

Network Security

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Zoombombing, a trend in recent months, has quickly moved from online classroom pranks to organised disruption efforts, which the FBI has threatened to punish with jail time. “The FBI has received multiple reports of conferences being disrupted by pornographic and/or hate images and threatening language.” the agency stated in a recent press release. 1 Zoombombing has quickly moved from online prank to organised disruption effort, which the FBI has threatened to punish with jail time. As the use of Zoom has increased dramatically due to the Covid-19 pandemic, multiple security issues have emerged concerning the platform. The company is taking steps to bring the platform up to date with industry standards. But the issue clearly shows that certain companies focus more on the functionality they provide to their users than they do on the appropriate security, says Ion-Alexandru Secara.

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Identifying Encryption Algorithms in ECB and CBC Modes Using Computational Intelligence

Cited by 2 publications

References 9 publications

A block cipher algorithm identification scheme based on hybrid k-nearest neighbor and random forest algorithm

A block cipher algorithm identification scheme based on hybrid k-nearest neighbor and random forest algorithm

Zoombombing – the end-to-end fallacy

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