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In recent years, frequent network attacks have seriously threatened the interests and security of humankind. To address this threat, many detection methods have been studied, some of which have achieved good results. However, with the development of network interconnection technology, massive amounts of network data have been produced, and considerable redundant information has been generated. At the same time, the frequently changing types of cyberattacks result in great difficulty collecting samples, resulting in a serious imbalance in the sample size of each attack type in the dataset. These two problems seriously reduce the robustness of existing detection methods, and existing research methods do not provide a good solution. To address these two problems, we define an unbalanced index and an optimal feature index to directly reflect the performance of a detection method in terms of overall accuracy, feature subset optimization, and detection balance. Inspired by DNA computing, we propose intelligent attack detection based on DNA computing (ADDC). First, we design a set of regular encoding and decoding features based on DNA sequences and obtain a better subset of features through biochemical reactions. Second, nondominated ranking based on reference points is used to select individuals to form a new population to optimize the detection balance. Finally, a large number of experiments are carried out on four datasets to reflect real-world cyberattack situations. Experimental results show that compared with the most recent detection methods, our method can improve the overall accuracy of multiclass classification by up to 10%; the imbalance index decreased by 0.5, and 1.5 more attack types were detected on average; and the optimal index of the feature subset increased by 83.8%.

Blockchain has become a well-known, secured, decentralized datastore in many domains, including medical, industrial, and especially the financial field. However, to meet the requirements of different fields, platforms that are built on blockchain technology must provide functions and characteristics with a wide variety of options. Although they may share similar technology at the fundamental level, the differences among them make data or transaction exchange challenging. Cross-chain transactions have become a commonly utilized function, while at the same time, some have pointed out its security loopholes. It is evident that a secure transaction scheme is desperately needed. However, what about those nodes that do not behave? It is clear that not only a secure transaction scheme is necessary, but also a system that can gradually eliminate malicious players is of dire need. At the same time, integrating different blockchain systems can be difficult due to their independent architectures, and cross-chain transactions can be at risk if malicious attackers try to control the nodes in the cross-chain system. In this paper, we propose a dynamic reputation management scheme based on the past transaction behaviors of nodes. These behaviors serve as the basis for evaluating a node’s reputation to support the decision on malicious behavior and enable the system to intercept it in a timely manner. Furthermore, to establish a reputation index with high precision and flexibility, we integrate Particle Swarm Optimization (PSO) into our proposed scheme. This allows our system to meet the needs of a wide variety of blockchain platforms. Overall, the article highlights the importance of securing cross-chain transactions and proposes a method to prevent misbehavior by evaluating and managing node reputation.

Recently, blockchain and smart contracts have been one of most popular technology to establish trustworthy applications in several fields. However, due to the transparency and publicity of blockchain, the information processed by a smart contract is visible to every party in a blockchain. In light of this, this study proposes a trusted computing as a service (TCaaS) framework based on the blockchain. One of the critical component is the Execution Environment for Secured Smart Contract Computing (ESC)2 node. In the proposed framework, people can deploy (ESC)2 nodes in a blockchain. Users can upload general-purposed programs and associated parameters and discover an (ESC)2 node for execution via related smart contracts. The programs and parameters are encrypted so that only selected (ESC)2 node can decrypt the data. Then the execution environment calculates the result and returns it to the blockchain. We evaluate our concept with the ESP32 microcontroller with the ATECC508A security chip and the Quorum blockchain platform. Therefore, the study contributes to ensure faithful execution of programs without losing confidentiality.