Sheng Yuan

The smart grid has now been fully implemented with a robust defense system that is built on the primary security principles of “security zoning, network specialization, horizontal isolation, and vertical authentication.” The secure cryptographic module plays a pivotal role as one of the key components in the power system, facilitating horizontal isolation and vertical authentication. Nevertheless, assessing secure cryptographic modules within power systems presents several challenges. These include low recognition efficiency of cryptographic algorithms, the difficulty in identifying the characteristics of encryption algorithm variants, and challenges specific to special business scenarios. To address these issues, this paper introduces an automatic cryptographic algorithm recognition method. Leveraging a deep learning framework in conjunction with modules such as data encryption, data enhancement, feature extraction, model construction, and five-fold cross-validation, this method achieves automated recognition of cryptographic algorithms, enhancing the testing capabilities of secure cryptographic modules. The experiments reveal that cryptographic algorithm detection using deep learning offers significant advantages, including a high recognition success rate, strong adaptability, and a high degree of automation. This method proves beneficial in significantly enhancing the efficiency of security evaluation and modification, effectively detecting the ciphertext features of commercial codes, and providing vital support for the secure operation of the smart grid.

Internet of Things (IoT) revolutionizes data collection, especially in e-healthcare, where patients data from wearables and sensors improves medical services. However, IoT’s limitations in computing and storage require cloud outsourcing. Combining IoT with the cloud has potential but raises concerns about data security. Leveraging cloud storage presents an attractive solution for accommodating the substantial volume of data outsourced by IoT devices. As the outsourcing of real-time data to cloud storage becomes commonplace, the adoption of data auditing schemes emerges as a means to ensure data integrity. To curtail operational expenses, various deduplication techniques are commonly employed on outsourced data, effectively sidestepping redundant data and resulting in storage and bandwidth efficiencies. Although real-time data typically remains distinct due to its diverse origins, scenarios such as data sharing or trading in data-driven services and datamarkets can lead to data redundancy. Moreover, in order to fortify against any potential information leakage, encryption is implemented prior to deduplication. Convergent Encryption (CE) stands as a prominent exemplar of this approach. Effectively integrating data auditing, deduplication, and encryption for wireless sensor devices is no trivial task. To efficiently and securely accommodate data while authenticating them through a heterogeneous framework, we present a novel remote data checking scheme, denoted as the VRDC scheme. This scheme empowers IoT data to be encrypted, updated, deduplicated, and audited, aligning with the imperatives of security, privacy, and efficiency. Through comprehensive security analysis, we establish that our VRDC scheme is fortified against potential threats. Our experimental findings highlight the efficiency of our approach in the realms of auditing, deduplication, and updates. Furthermore, the evidence highlights the potential for optimization within our scheme when compared to related works. This is achieved through the careful management of dynamic update scales within a file.