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In this article, we introduce Time Memory Trade Off attack and a method for finding near collisions in a hash function. By considering hash computations, it is easy to compute a lower bound for the complexity of near-collision algorithms, and to construct matching algorithm. However, this algorithm needs a lot of memory, and uses  memory accesses. Recently, some algorithms have been proposed that do not require this amount of memory. They need more hash evaluation, but this attack is actually more practical. These algorithms can be divided in two main group: the first group is based on truncation and the second group is based on covering codes. In this paper, we consider the first group that is based on truncation. For practical implementation, it can be assumed that some memory is available, Leurent [10] showed that it is possible to reduce the complexity significantly by using this memory. In the next step, Sasaki et al. [9] proposed improvement of most popular Time Memory Trade off for K-tree algorithm by using multi-collision based on Helman’s table. As a result, they obtained new trade off curve  that for k=4 the tradeoff curve will be . In this article, at the first the methods of TMTO, and then the method of finding near-collision by using TMTO are explained.

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Devices such as wireless sensor networks and RFIDs have limited memory, power and energy. They have security requirements so that the usual implementation of cryptographic algorithms is not appropriate for them and leads to high consumption of resources. One solution is designing new lightweight algorithms that have a lower security level than standard algorithms. The second solution is implementing standard algorithms such as AES block cipher as a lightweight algorithm. In this type of implementation, some techniques such as resource sharing, S-box implementation with combinational circuits, mapping computations finite fields from one base to another base and on the fly computation are used. In this paper, the most important lightweight implementations of AES are evaluated. The criteria considered for this evaluation include gate count, the number of clocks required for an encryption/decryption operation, throughput, power, energy and the combination of themes. Studies show that we can use standard encryption algorithms in applications with limited area between 2000-3000 GE and a small amount of energy, for example a few PJ. Some of these successes are achieved due to advancements in CMOS circuit technology and some others are the result of designing suitable hardware architecture, exact scheduling of cryptographic operations and efficient use of resources.
 

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With the increasing and widespread application of deep learning and neural networks across various scientific domains and the notable successes achieved, deep neural networks were employed for differential cryptanalysis in 2019. This marked the initiation of growing interest in this research domain. While most existing works primarily focus on enhancing and deploying neural distinguishers, limited studies have delved into the intrinsic principles and learned characteristics of these neural distinguishers. In this study, our focus will be on analyzing block ciphers such as Speck, Simon, and Simeck using deep learning. We will explore and compare the factors and components that contribute to better performance. Additionally, by detailing attacks and comparing results, we aim to address the question of whether neural networks and deep learning can effectively serve as tools for block cipher cryptanalysis or not.

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Leakage-resilient cryptography aims to design key exchange protocols to withstand leakage attacks. These protocols are examined using a leakage-resilient security model to determine whether they possess the claimed security properties. The security analysis focuses on how the leakage-resilient security model has evolved to meet increasing security requirements and cover a broader range of attacks. By studying and analyzing the presented security properties of these models, potential vulnerabilities in protocol design can be effectively addressed. This article delves into various leakage-resilient security models based on two models, CK and eCK, and provides examples of secure key exchange protocols defined within these models. Additionally, it explores the relationship between adversaries' capabilities in these models and different attack schemes in the real world. By offering insights into various leakage-resilient security models, leakage attacks, and the development of secure protocols, it contributes to advancing knowledge in this field.

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In the world of digital communication, authentication is an important concern and the need for a safe and secure system increases the necessity of designing authentication systems. To perform authentication, biometric-based approaches are of great interest due to the property of being alive and resistant to forgery. In this study, an authentication system based on heart signal is designed. Due to the process of receiving heart signals, their data usually has a lot of noise. In order to prepare the data, in the proposed system, the heart signals are first cleaned and then transferred to the frequency domain for feature extraction. Also, they are converted into an image by applying the Wigner-Ville distribution, so that each image contains the signal information of each person’s heart and is unique. In the proposed authentication system, these images are used for training and evaluation in a deep convolutional neural network. The output of this system provides the possibility of people’s identification. The data of this study are taken from the NSRDB and MITDB databases, and significant results have been obtained compared to previous studies.