Autonomous System (AS) business relationship data serves as an important trust foundation for detecting route leaks. Some AS's reluctant to disclose all of their business relationship data for various reasons has led to a decrease in routing detection accuracy, requiring AS business relationship prediction. The existing AS business relationship prediction schemes have problems with data tampering and the accuracy needs to be improved. This article proposes a trusted AS business relationship prediction method based on neural networks. By collecting and storing AS business relationship data on the blockchain, a feature library of AS business relationship data is constructed. Finally, a deep neural network is used for AS business relationship prediction to achieve reliable and accurate AS business relationship prediction.
KEYWORDS: Network security, Computer security, Transformers, Data modeling, Feature extraction, Neural networks, Information security, Education and training, Machine learning, Head
With the rise of new threats represented by advanced Persistent Threat Attack (APT), traditional network security methods such as vulnerability scanning, and intrusion detection can no longer meet the actual needs. Therefore, it is increasingly necessary to make full use of more security data for Network Security Situation Prediction (NSSP) to ensure the safe operation of Internet services. In this paper, the HKIT-Trans model is effectively proposed for the NSSP task. The aim of this work is to predict the current network condition by exploiting network data characteristics. The original network data has a large number of missing null values, and it is difficult to obtain data characteristics. Therefore, we design a null filling method combining Huber regression and KNN algorithm to improve the availability of data. At the same time, we propose the improved tab-transformer algorithm to extract network data features and perform inference. The experimental results show that the proposed method has better classification performance considering the existing techniques. Compared with the same baseline model, the AUC value of our proposed null filling method is increased by 1.94%-14.91%. For the classification effect of dataset CNCERT, HKIT-Trans is 7.14%higher than Huber-KNN with Logistic Regression, and 2.26%higher than Huber-KNN with LDA.
KEYWORDS: Data storage, Clouds, Computer security, Blockchain, Network security, Data privacy, Data transmission, Data processing, Data communications, Tunable filters
Cloud storage has become a widespread trend with its efficient and convenient features. However, this also brings new security risks, such as identity forgery, data theft, privacy disclosure and other security problems. Based on blockchain, elliptic curve cryptography and other technologies, this paper proposes a blockchain-based cloud environment data storage scheme, which can provide users with decentralized identity authentication and data integrity verification functions, and the transaction information of data storage is stored on the chain to realize the safe storage of data.
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