Search Results (6)

Authenticated encryption with associated data (AEAD) schemes based on stream ciphers, such as ASCON and MORUS, typically use nonlinear feedback shift registers (NFSRs) and linear feedback shift registers (LFSRs) to generate variable-length key streams. While these methods ensure message confidentiality and authenticity, they present challenges in security analysis, especially when automated evaluation is involved. In this paper, we present MLOL, a novel AEAD algorithm based on the LOL framework. MLOL combines authenticated encryption with optimizations to the LFSR structure to enhance both security and efficiency. The cost evaluation demonstrates that on specialized CPU platforms without SIMD instruction set support, MLOL achieves better performance in authenticated encryption speed compared to LOL-MINI with GHASH. Our security analysis confirms that MLOL provides 256-bit security against current cryptanalytic techniques. Experimental results demonstrate that MLOL not only inherits the excellent performance of LOL but also reduces the time complexity of the authenticated encryption process, providing more reliable security guarantees. It significantly simplifies security evaluation, making it suitable for automated analysis tools, and offers a feasible new approach for AEAD algorithm design. Full article

Medical image analysis methods have been applied to clinical scenarios of tumor diagnosis and treatment. Many studies have attempted to optimize the effectiveness of tumor MRI image segmentation by deep learning, but they do not consider the optimization of local details and the interaction of global semantic information. Second, although medical image pattern recognition can learn representative semantic features, it is challenging to ignore useless features in order to learn generalizable embeddings. Thus, a tumor-assisted segmentation method is proposed to detect tumor lesion regions and boundaries with complex shapes. Specifically, we introduce a denoising convolutional autoencoder (DCAE) for MRI image noise reduction. Furthermore, we design a novel tumor MRI image segmentation framework (NFSR-U-Net) based on class-correlation pattern aggregation, which first aggregates class-correlation patterns in MRI images to form a class-correlational representation. Then the relationship of similar class features is identified to closely correlate the dense representations of local features for classification, which is conducive to identifying image data with high heterogeneity. Meanwhile, the model uses a spatial attention mechanism and residual structure to extract effective information of the spatial dimension and enhance statistical information in MRI images, which bridges the semantic gap in skip connections. In the study, over 4000 MRI images from the Monash University Research Center for Artificial Intelligence are analyzed. The results show that the method achieves segmentation accuracy of up to 96% for tumor MRI images with low resource consumption. Full article

Nonlinear feedback shift registers (NFSRs) are the main components of stream ciphers and convolutional decoders. Recent years have seen an increase in the requirement for information security, which has sparked NFSR research. However, the NFSR study is very imperfect as a result of the lack of appropriate mathematical tools. Many scholars have discovered in recent years that the introduction of semi-tensor products (STP) of matrices can overcome this issue because STP can convert the NFSR into a quasi-linear form. As a result of STP, new NFSR research has emerged from a different angle. In view of this, in order to generalize the latest achievements of NFSRs based on STP and provide some directions for future development, the research results are summarized and sorted out, broadly including the modeling of NFSRs, the analysis of the structure of NFSRs, and the study of the properties of NFSRs. Full article

The present study’s main objective is to assess the impact of non-financial sustainability reporting (NFSR) on corporate reputation and the role of the CEO in the opportunistic behavior of companies listed on the Tehran Stock Exchange. In total, 178 firms were assessed for this paper during 2013–2020. In this study for calculating the NFSR, environmental sustainability reporting (ESR), social sustainability reporting (SSR), governance sustainability reporting (GSR) and ethical sustainability reporting (ETSR), Arianpoor and Salehi’s comprehensive and conceptual model has been used. In addition, the literature states that a CEO’s power can be classified as an opportunity for discretion and opportunistic behavior in CEOs that is in contrast with stakeholder demands. To this end, in this study, CEOs’ power has been used as an indicator for the CEO’s opportunistic behavior, and the CEO pay slice (CPS) index was used to calculate the CEO’s level of power. The results revealed that NFSR affects corporate reputation positively. In addition, ESR, SSR, ETSR and GSR positively affect corporate reputation. Moreover, the CEO’s power affects the relationship between NFSR/ESR/SSR/ETSR and corporate reputation. Because managers desire to engage in social and ethical activities, they try to hide the company’s errors and increase its reputation. The results revealed that the CEO’s power did not affect the relationship between GSR and corporate reputation. Since companies in the Tehran Stock Exchange are under intensive supervision, such as in governance, the impact of a CEO’s power and the interaction of a CEO’s power and GSR on company reputation in this study might, thus, not apply to these companies. It is crucial to investigate NFSR, corporate reputation and CEO power within Iran-specific conditions because of differences in emerging markets and developing countries such as Iran, which have diverse ownership structures, economic status, legal systems, government policies, and culture. Full article

A chaotic system and two Nonlinear Feadback Shift Registers (NFSRs) are used to generate a new stream cipher in this paper. This design can be used for efficient encryption in resource-constrained devices or environments. The chaotic system is quantified and integrated with two NFSRs based on the technology of Field Programmable Gate Array (FPGA). Many analyses are made from the angle of entropy in order to verify the cryptographic characteristics of the stream cipher, and National Institute of Standards and Technology (NIST) statistical test is completed to analyze the cipher. The test results show that the stream cipher here has good cryptographic characteristics. Full article

In Fast Software Encryption (FSE) 2015, while presenting a new idea (i.e., the design of stream ciphers with the small internal state by using a secret key, not only in the initialization but also in the keystream generation), Sprout was proposed. Sprout was insecure and an improved version of Sprout was presented in FSE 2017. We introduced Fruit stream cipher informally in 2016 on the web page of IACR (eprint) and few cryptanalysis were published on it. Fortunately, the main structure of Fruit was resistant. Now, Fruit-80 is presented as a final version which is easier to implement and is secure. The size of LFSR and NFSR in Fruit-80 is only 80 bits (for 80-bit security level), while for resistance to the classical time-memory-data tradeoff (TMDTO) attacks, the internal state size should be at least twice that of the security level. To satisfy this rule and to design a concrete cipher, we used some new design ideas. It seems that the bottleneck of designing an ultra-lightweight stream cipher is TMDTO distinguishing attacks. A countermeasure was suggested, and another countermeasure is proposed here. Fruit-80 is better than other small-state stream ciphers in terms of the initialization speed and area size in hardware. It is possible to redesign many of the stream ciphers and achieve significantly smaller area size by using the new idea. Full article