Biblio

Due to its decentralized trust mechanism, blockchain is increasingly used as a trust intermediary for multi-party cooperation to reduce the cost and risk of maintaining centralized trust nowadays. And as the requirements for privacy and high throughput, consortium blockchain is widely used in data sharing and business cooperation in practical application scenarios. Nowadays, the protection of traditional medicine has been regarded as human intangible cultural heritage in recent years, but this kind of protection still faces the problem that traditional medicine prescriptions are unsuitable for disclosure and difficult to protect. Hyperledger is a consortium blockchain featuring authorized access, high throughput, and tamper-resistance, making it ideal for privacy protection and information depository in traditional medicine protection. This study proposes a solution for intellectual property protection of traditional medicine by using a blockchain platform to record prescription iterations and clinical trial data. The privacy and confidentiality of Hyperledger can keep intellectual property information safe and private. In addition, the author proposes to invite the Patent Offices and legal institutions to join the blockchain network, maintain users' properties and issue certificates, which can provide a legal basis for rights protection when infringement occurs. Finally, the researchers have built a system corresponding to the scheme and tested the system. The test outcomes of the system can explain the usability of the system. And through the test of system throughput, under low system configuration, it can reach about 200 query operations per second, which can meet the application requirements of relevant organizations and governments.

In the computer field, cybersecurity has always been the focus of attention. How to detect malware is one of the focuses and difficulties in network security research effectively. Traditional existing malware detection schemes can be mainly divided into two methods categories: database matching and the machine learning method. With the rise of deep learning, more and more deep learning methods are applied in the field of malware detection. Deeper semantic features can be extracted via deep neural network. The main tasks of this paper are as follows: (1) Using machine learning methods and one-dimensional convolutional neural networks to detect malware (2) Propose a machine The method of combining learning and deep learning is used for detection. Machine learning uses LGBM to obtain an accuracy rate of 67.16%, and one-dimensional CNN obtains an accuracy rate of 72.47%. In (2), LGBM is used to screen the importance of features and then use a one-dimensional convolutional neural network, which helps to further improve the detection result has an accuracy rate of 78.64%.

The integration of Internet of Things (IoT) and edge computing is currently a new research hotspot. However, the lack of trust between IoT edge devices has hindered the universal acceptance of IoT edge computing as outsourced computing services. In order to increase the adoption of IoT edge computing applications, first, IoT edge computing architecture should establish efficient trust calculation mechanism to alleviate the concerns of numerous users. In this paper, a reliable and lightweight trust mechanism is originally proposed for IoT edge devices based on multi-source feedback information fusion. First, due to the multi-source feedback mechanism is used for global trust calculation, our trust calculation mechanism is more reliable against bad-mouthing attacks caused by malicious feedback providers. Then, we adopt lightweight trust evaluating mechanism for cooperations of IoT edge devices, which is suitable for largescale IoT edge computing because it facilitates low-overhead trust computing algorithms. At the same time, we adopt a feedback information fusion algorithm based on objective information entropy theory, which can overcome the limitations of traditional trust schemes, whereby the trust factors are weighted manually or subjectively. And the experimental results show that the proposed trust calculation scheme significantly outperforms existing approaches in both computational efficiency and reliability.