An improvement of chaos-based hash function in cryptanalysis approach: An experience with chaotic neural networks and semi-collision attack

Ghonaim, Wafaa; Ghali, Neveen I.; Hassanien, Aboul Ella; Banerjee, Soumya

doi:10.1007/s12293-013-0113-7

Cited by 8 publications

(3 citation statements)

References 14 publications

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“…the lower are the resources, the longer is the execution time, although in general terms the execution time is low. In Figure 5, it is possible to determine that the runtime of the proposed hash function for the mobile device Moto g is related to Equation (16), where x is the duration of the input recording.…”

Section: Computational Costmentioning

confidence: 99%

“…y ≈ −1 × 10 −5 x 2 + 0.0303x − 0.0365 (16) It is also possible to determine that the execution time of the proposed hash function for Nexus 5x is related to Equation (17).…”

Section: Computational Costmentioning

confidence: 99%

“…Currently, there are several mechanisms that seek to attack hash functions such as brute force attack and cryptanalytical attack [14][15][16]; therefore, the number of output bits in the hash code should be considered when designing the system. It is also necessary to clarify that collisions will always exist since the mapping process is not one to one [17].…”

Section: Introductionmentioning

confidence: 99%

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A Mobile-Oriented System for Integrity Preserving in Audio Forensics

2019

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This paper addresses a problem in the field of audio forensics. With the aim of providing a solution that helps Chain of Custody (CoC) processes, we propose an integrity verification system that includes capture (mobile based), hash code calculation and cloud storage. When the audio is recorded, a hash code is generated in situ by the capture module (an application), and it is sent immediately to the cloud. Later, the integrity of the audio recording given as evidence can be verified according to the information stored in the cloud. To validate the properties of the proposed scheme, we conducted several tests to evaluate if two different inputs could generate the same hash code (collision resistance), and to evaluate how much the hash code changes when small changes occur in the input (sensitivity analysis). According to the results, all selected audio signals provide different hash codes, and these values are very sensitive to small changes over the recorded audio. On the other hand, in terms of computational cost, less than 2 s per minute of recording are required to calculate the hash code. With the above results, our system is useful to verify the integrity of audio recordings that may be relied on as digital evidence.

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Section: Computational Costmentioning

confidence: 99%

“…y ≈ −1 × 10 −5 x 2 + 0.0303x − 0.0365 (16) It is also possible to determine that the execution time of the proposed hash function for Nexus 5x is related to Equation (17).…”

Section: Computational Costmentioning

confidence: 99%