The Payment Channel Network (PCN) provides an off-chain payment model to alleviate the problem of limited blockchain throughput. However, PCNs typically rely on third-party regulators to monitor the blockchain states continuously to prevent honest users’ funds from being lost, which increases user overhead and compromises transaction privacy. To overcome these challenges, we propose a highly compatible bidirectional Self-Regulatory Virtual Channel (SRVC) that eliminates third-party reliance while enhancing privacy and reducing overhead. By leveraging absolute time locks, we establish a self-regulatory mechanism where users only need to monitor the blockchain online for a short time window to ensure the security of their funds, effectively removing the need for third-party monitoring. Additionally, we introduce a novel payment paradigm and a punishment mechanism based on adaptor signatures across underlying channels to ensure transaction privacy and security, while also reducing transaction overhead. We formalize the security properties of SRVC as an ideal functionality and prove that SRVC is secure in the Universal Composability framework. Performance analysis demonstrates that, compared to other virtual channel protocols based on Lightning channels, SRVC reduces communication overhead by approximately 46.8% in the open operation and 62.9% in the update operation. In high-concurrency payment scenarios, SRVC further decreases communication overhead by about 57.8% and reduces the number of transactions by around 52.8% compared to Sleepy Channel’s Virtual Channel (SCVC) implementation.

The payment channel network aims to solve the problems of long payment confirmation time and limited throughput in cryptocurrencies through off-chain payments. Hash Time-Lock Contract (HTLC) is an off-chain payment protocol that Lightning Network (LN) adopted. Unfortunately, when performing high-valued payments off-chain, due to the impact of payment channel capacity, it is often necessary to split a single payment, which increases the transaction fees and time. Therefore, we propose LightPay, an atomic off-chain multi-path payment protocol based on adapter signature and discrete logarithm problem. Among different conditions encoded in the multi-path contract, the multi-path transmission of a single high-valued payment can be realized under the premise of the unlinkability of partial payments. We construct an ideal functionality in the Universal Composability framework and demonstrate that LightPay UC-realizes it, thereby providing proof of its security and privacy. Experimental results indicate that the payment success rate of LightPay can be increased by 11.08% in 0.0025 BTC payments compared with the single-path payment protocol Multihop HTLC in LN. Additionally, compared with the multi-path payment protocol CryptoMaze, the communication overhead required by LightPay is reduced to about 55.6% on average in the simulated network. Overall, LightPay has advantages regarding payment success rate and overhead.

Network operation and maintenance rely heavily on network traffic monitoring. Due to the measurement overhead reduction, lack of measurement infrastructure, and unexpected transmission error, network traffic monitoring systems suffer from incomplete observed data and high data sparsity problems. Recent studies model missing data recovery as a tensor completion task and show good performance. Although promising, the current tensor completion models adopted in network traffic data recovery lack an effective and efficient retraining scheme to adapt to newly arrived data while retaining historical information. To solve the problem, we propose LightNestle, a novel sequential tensor completion scheme based on meta-learning, which designs (1) an expressive neural network to transfer spatial knowledge from previous embeddings to current embeddings; (2) an attention-based module to transfer temporal patterns into current embeddings in linear complexity; and (3) meta-learning-based algorithms to iteratively recover missing data and update transfer modules to catch up with learned knowledge. We conduct extensive experiments on two real-world network traffic datasets to assess our performance. Results show that our proposed methods achieve both fast retraining and high recovery accuracy.

With the advances and innovations in digital technologies, blockchain has empowered advancements in communications and networking, promising to build trust and establish secure decentralized communications networks. Unfortunately, current personal data privacy protection schemes still suffer from explicit storage, lack of data ownership and implementation of fine-grained access control by users, and lack of transparency and auditability of data. In this article, we propose a personal data privacy protection scheme based on consortium blockchain that stores original data encrypted with an improved Paillier homomorphic encryption mechanism, namely PDPChain, where users realize fine-grained access control based on ciphertext policy attribute-based encryption (CP-ABE) on blockchain. In this scheme, consortium blockchain combines distributed private clusters to store the encrypted data, improving data transmission efficiency, and guaranteeing user privacy and security through off-chain storage and on-chain transmission synergy. In addition, it is more lightweight encryption and demarcation, ultimately protecting personal data privacy and providing a secure and trusted way to obtain information for data mining. For the performance testing, data in the form of files are used as an example, and the scheme is designed and simulated on Hyperledger Fabric and InterPlanetary File System. Experimental results show that the improved Paillier encryption mechanism reduces the overall encryption and decryption elapsed time by 25% and encryption elapsed time by 48%. Furthermore, the proposed CP-ABE access control method is adaptive to storing and sharing a massive amount of data. With the increase in the number of access control policies, the overall time-consuming of the scheme does not increase, and the time-consuming of decryption can also be stabilized at about 2 s.

Cyber–physical systems (CPSs) is a security real-time embedded system. CPS integrates the information sensed by the current physical sensors, through high-speed real-time transmission, and then carries out powerful information processing to effectively interact and integrate the physical and the information worlds. With the aim to improve the quality of service, optimize the existing physical space, and increase security, collaborative filtering algorithms have also been widely used in various recommendation models for Internet of Things (IoT) services. However, general collaborative filtering algorithms cannot capture complex interactive information in the sparse Mashup–Web service call matrix, which leads to lower recommendation performance. Based on the artificial intelligence technology, this study proposes a recommendation algorithm for a security collaborative filtering service that integrates content similarity. A security collaborative filtering module is used to capture the complex interaction information between Mashup and Web services. By applying the content similarity module to extract the semantic similarity information between the Mashup and Web services, the two modules are seamlessly integrated into a deep neural network to accurately and quickly predict the rating information of Mashup for the Web services. Real data set on the intelligent CPS is captured and then compared with mainstream service recommendation algorithms. Experimental results show that the proposed algorithm not only efficiently completes the Web service recommendation task under the premise of sparse data but also shows better accuracy, effectivity, and privacy. Thus, the proposed method is highly suitable for the application of intelligence CPS.

The Industrial Internet of Things ( IIoT ) is designed to refine and optimize the process controls, thereby leveraging improvements in economic benefits, such as efficiency and productivity. However, the Radio Frequency Identification ( RFID ) technology in an IIoT environment has problems such as low security and high cost. To overcome such issues, a mutual authentication scheme that is suitable for RFID systems, wherein techniques in Deep Learning ( DL ) are incorporated onto the Arbiter Physical Unclonable Function ( APUF ) for the secured access authentication of the IC circuits on the IoT, is proposed. The design applies the APUF-MPUF mutual authentication structure obtained by DL to generate essential real-time authentication information, thereby taking advantage of the feature that the tag in the PUF circuit structure does not need to store any essential information and resolving the problem of key storage. The proposed scheme also uses a bitwise comparison method, which hides the PUF response information and effectively reduces the resource overhead of the system during the verification process, to verify the correctness of the two strings. Security analysis demonstrates that the proposed scheme has high robustness and security against different conventional attack methods, and the storage and communication costs are 95.7% and 42.0% lower than the existing schemes, respectively.

The rapid development of information technology promotes the development and application of Telecare Information System (TMIS). However, TMIS also has security problems such as information leakage, false authentication, and key loss. In order to solve the safety problems of TMIS, this paper combines Physical Unclonable Function (PUF) and Elliptic Curve Cryptography (ECC) technology to propose an access control and authentication scheme suitable for TMIS. The proposed scheme uses PUF and compact PUF identity authentication models to implement secure mutual authentication between tag and server. The key information in the scheme is generated by PUF, which not only reduces the cost of algorithm design but also avoids the risk of information leakage and key loss. In addition, this article uses ECC technology to encrypt the PUF response information and random numbers, which can ensure that this data information will not be leaked to the attacker. Then through the ProVerif verification tool and security attribute analysis, it is proved that the scheme is safe in the face of major attacks. The comparative analysis results show that the proposed scheme has higher security and is more suitable for TMIS.