Polynomial multiplication is a critical performance bottleneck in lattice-based cryptographic schemes, in which efficient and flexible hardware implementations are essential. This paper presents a high-performance and reconfigurable polynomial multiplication unit that integrates the cooptimization of algorithmic and architectural optimizations. As the core operation of polynomial multiplication, we propose the Karatsuba-based high-efficiency radix-4 number theoretic transform (KHNTT) algorithm, which reduces the computational complexity by leveraging low-bit-width arithmetic and parallel processing techniques. Based on this algorithm, we develop a unified radix-4 butterfly unit capable of supporting modular polynomial operations under various parameter configurations, and to further improve the computational efficiency and architectural flexibility, key algorithm modules such as modular reduction and data flow scheduling are deeply optimized. Furthermore, we introduce an iterative interleaving memory strategy combined with a bank resource sharing mechanism to eliminate memory access conflicts and improve data access efficiency. The proposed polynomial multiplication unit allows flexible configuration of the butterfly units and supports polynomial computations of various degrees without the need for recompilation. The FPGA implementation results demonstrate that, compared with existing solutions, our architecture achieves a$1.25\times $to$5.27\times $improvement in the polynomial multiplication throughput and a$1.14\times $to$2.7\times $enhancement in the area efficiency, fully leveraging the computational advantages of FPGAs.

Abstract Binary vulnerability detection plays an important role in the field of program security. In order to deal with large-scale vulnerability detection tasks, more and more neural network technologies are applied to cross-architectures vulnerability detection. These technologies have significantly improved the accuracy of vulnerability detection. However, existing methods still face problems such as single extracted information, poor robustness against compilation optimization, and inability to perform cross-architectures vulnerability detection. Therefore, this paper proposes a cross-architectures vulnerability detection method based on attention mechanism and multi-feature fusion. This method can simultaneously obtain information such as assembly code, attribute control flow graph and function-level features for cross-architecture, cross-compilers and cross-optimization options vulnerability detection. Adding attention mechanism to GRU and GoogleNet improves the model to obtain semantic information and attribute information after the fusion of basic block-level and function-level features, and searches for Top-K suspected vulnerability functions and graph matching through deep neural network model to perform phased vulnerability detection. The experimental results show that this method achieves an accuracy of 95.79% and a Recall of 97.06%, which is better than the existing methods and performs well in vulnerability detection in real environments.

In lattice-based postquantum cryptography (PQC), polynomial multiplication is complex and time-consuming, which affects the overall computational efficiency. In addition, the parameters of different lattice-based algorithms require different number theoretic transform (NTT) structures, which limits the versatility of hardware design. To this end, this article proposes scalable and parallel optimization of the NTT based on a field-programmable gate array (FPGA). By analyzing the algorithm flow of the NTT, inverse NTT (INTT), and pointwise multiplication (PWM), an FPGA loosely coupled structure is designed, which can be used to place butterfly units of multiple pipelines in parallel and supports various modulo operations of a polynomial. In addition, to improve computing efficiency and scalability, key algorithm modules such as multipliers and modular reduction are deeply optimized. Moreover, the storage optimization of multiple RAM channels is carried out, and the alternate access control of data and the multiplexing of RAM resources reduce resource consumption and improve data access efficiency. For the SHA-3 algorithm, the scalable Keccak algorithm is implemented in a serial–parallel hybrid manner and supports multiple hash modes. Finally, taking the Dilithium algorithm as an example, through the parallelization of SHA-3 and NTT, the calculation cycle of key generation, signature, and verification is shortened. The experimental results and analysis show that the scheme in this article shortens the NTT calculation period while ensuring a high frequency, and the calculation time is significantly better than that of other schemes. Furthermore, it can support the optimized parallelization of multiple moduli and give full play to the computing advantages of an FPGA.

Code randomization, aka software diversification, is an effective way to mitigate code-reuse attacks. This mechanism diversifies the target software into heterogeneous variants, making a specific attack chain unfeasible for the transformed variants. Enhancing the heterogeneity of these variants is important for this method to reach its expected security level. Additionally, limiting their execution overhead is necessary to ensure the availability of this protection. However, finding the optimal subset among a large number of diversified variants is computationally difficult. This creates a dilemma in software diversification schema where enhancing heterogeneity and reducing execution overhead are both desired.To ensure a determined code quality control in generating software variants, we propose a guided software diversification mechanism (called GuiDiv). GuiDiv formalizes the iterate-used transformations into a branching process of a tree (called DivTree) and introduces an optimization process (called nodemerging) to guide the diversification. The optimizer evaluates the intermediate compilation results using multi-target evaluation functions in each iteration. This process aims to optimize the contribution of structural heterogeneity from redundant instructions, allowing variants with higher structural dissimilarity and lower overhead to have a higher chance of participating in the next iteration. We developed a proof-of-concept compilation module and used OpenSSL as the performance benchmark. In the evaluations, compared to related schemes, GuiDiv can bring higher control flow dissimilarity in most test cases. Regarding the variant heterogeneity, variants generated by GuiDiv exhibit significantly lower execution overhead.

A current research trend is to combine multimedia data with artificial intelligence and process them on cloud servers. In this context, ensuring the security of multimedia cloud servers is critical, and the cyber mimic defence (CMD) technology is a promising approach to this end. CMD, which is an innovative active defence technology developed in China, can be applied in many scenarios. However, although the mathematical model is a key component of CMD, a universally acceptable mathematical model for theoretical CMD has not been established yet. In this work, the attack problems and modelling difficulties were extensively examined, and a comprehensive modelling theory and concepts were clarified. By decoupling the model from the input and output of the specific system scene, the modelling difficulties were effectively avoided, and the mathematical expression of the CMD mechanism was enhanced. Furthermore, the process characteristics of the attack behaviour were identified by using a specific mathematical mapping method. Finally, based on the decomposition problem of large prime factors and convolution operations, an intuitive and exclusive CMD mathematical model was proposed. The proposed model could clearly express the CMD mechanism and transform the problems of attack and defence in the CMD domain into corresponding mathematical problems. These aspects were considered to qualitatively assess the CMD security, and it was noted that a high level of security can be realized. Furthermore, the overhead of CMD was analyzed. Moreover, the proposed model can be directly programmed.

Blockchain is widely used in encrypted currency, Internet of Things (IoT), supply chain finance, data sharing, and other fields. However, there are security problems in blockchains to varying degrees. As an important component of blockchain, hash function has relatively low computational efficiency. Therefore, this paper proposes a new scheme to optimize the blockchain hashing algorithm based on PRCA (Proactive Reconfigurable Computing Architecture). In order to improve the calculation performance of hashing function, the paper realizes the pipeline hashing algorithm and optimizes the efficiency of communication facilities and network data transmission by combining blockchains with mimic computers. Meanwhile, to ensure the security of data information, this paper chooses lightweight hashing algorithm to do multiple hashing and transforms the hash algorithm structure as well. The experimental results show that the scheme given in the paper not only improves the security of blockchains but also improves the efficiency of data processing.

With the rapid development of the Internet, the security of network multimedia data has attracted increasingly more attention. The moving target defense (MTD) and cyber mimic defense (CMD) approaches provide a new way to solve this problem. To enhance the security of network multimedia data, this paper proposes a mimic encryption box for network multimedia data security. The mimic encryption box can directly access the network where the multimedia device is located, automatically complete the negotiation, provide safe and convenient encryption services, and effectively prevent network attacks. According to the principles of dynamization, diversification, and randomization, the mimic encryption box uses a reconfigurable encryption algorithm to encrypt network data and uses IP address hopping, port number hopping, protocol camouflage, and network channel change to increase the attack threshold. Second, the mimic encryption box has a built-in pseudorandom number generator and key management system, which can generate an initial random key and update the key with the hash value of the data packet to achieve “one packet, one key.” Finally, through the cooperation of the ARM and the FPGA, an access control list can be used to filter illegal data and monitor the working status of the system in real time. If an abnormality is found, the feedback reconstruction mechanism is used to “clean” the FPGA to make it work normally again. The experimental results and analysis show that the mimic encryption box designed in this paper has high network encryption performance and can effectively prevent data leakage. At the same time, it provides a mimic security defense mechanism at multiple levels, which can effectively resist a variety of network attacks and has high security.

As a recently proposed public key primitive, attribute-base encryption(ABE) divided into Ciphertext-policy ABE (CP-ABE) and Key-policy ABE (KP-ABE) is a highly promising tool for the management and protection of data. And Blockchain, as one of the core technologies of Bitcoin that is the most representative cryptocurrency, has received extensive attentions recently. Supervision and privacy protection are two difficulties in blockchain. In this paper, a new scheme combining blockchain and accountable CP-ABE is proposed. In this scheme, each change of the data is recorded on the blockchain. And the different permissions of access are realized through ABE. If a malicious user shares a decryption key illegally, it will be considered illegal. Similarly, if the authority generates a decryption key for any unauthorized user, it also will be considered illegal. This scheme allows any third party to publicly verify the identity of a decryption key. And it is achievable for an auditor to publicly audit whether a malicious user or the authority should be responsible for an exposed decryption key, and the key abuser cannot deny it. At last this scheme is applied to the management of electronic documents.