Li Ke

Bio: Li Ke is an academic researcher from Harbin University of Science and Technology. The author has contributed to research in topics: Network security & Support vector machine. The author has an hindex of 1, co-authored 1 publications receiving 1 citations.

Application of Network Security Technology Based on Blockchain Technology

Abstract: : This paper discusses the potential value of blockchain technology in the application of network security technology. Firstly, the challenges and problems faced by the current network security technology are analyzed. Then, combined with the advantages of blockchain technology, the specific application scenarios of blockchain technology in network security technology application are put forward, including identity authentication, data tamper prevention, secure communication and so on. Finally, the existing problems and development direction of blockchain technology in the application of network security technology are discussed. This paper aims to provide new ideas and methods for the research of network security technology application field.

Quantitative Hierarchical Threat Evaluation Model for Network Security

Abstract: Evaluating security threat status is very important in network security management and analysis. A quantitative hierarchical threat evaluation model is developed in this paper to evaluate security threat status of a computer network system and the computational method is developed based on the structure of the network and the importance of services and hosts. The evaluation policy from bottom to top and from local to global is adopted in this model. The threat indexes of services, hosts and local networks are calculated by weighting the importance of services and hosts based on attack frequency, severity and network bandwidth consumption, and the security threat status is then evaluated. The experiment results show that this model can provide the intuitive security threat status in three hierarchies: services, hosts and local networks so that system administrators are freed from tedious analysis tasks based on the alarm datasets to have overall security status of the entire system. It is also possible for them to find the security behaviors of the system, to adjust the security strategies and to enhance the performance on system security. This model is valuable for guiding the security engineering practice and developing the tool of security risk evaluation.

A Network Security Situation Prediction Method through the Use of Improved TCN and BiDLSTM

Abstract: The rapid development of information technology has brought much convenience to human life, but more network threats have also come one after another. Network security situation prediction technology is an effective means to protect against network threats. Currently, the network environment is characterized by high data traffic and complex features, making it difficult to maintain the accuracy of the situation prediction. In this study, a network security situation prediction model based on attention mechanism (AM) improved temporal convolutional network (ATCN) combined with bidirectional long short-term memory (BiDLSTM) network is proposed. The TCN is improved by AM to extract the input temporal features, which has a more stable feature extraction capability compared with the traditional TCN and BiDLSTM, which is more capable of processing temporal data, and is used to perform the situation prediction. Finally, by validating on a real network traffic dataset, the proposed method has better performance on multiple loss functions and has more accurate and stable prediction results than TCN, BiDLSTM, TCN-LSTM, and other time-series prediction methods.

An intelligent protection framework for intrusion detection in cloud environment based on covariance matrix self-adaptation evolution strategy and multi-criteria decision-making

Abstract: The security defenses that are not comparable to sophisticated adversary tools, let the cloud as an open environment for attacks and intrusions. In this paper, an intelligent protection framework for intrusion detection in a cloud computing environment based on a covariance matrix self-adaptation evolution strategy (CMSA-ES) and multi-criteria decision-making (MCDM) is proposed. The proposed framework constructs an optimal intrusion detector by using CMSA-ES algorithm which adjusts the best parameter set for the attack detector. Moreover, the proposed framework uses a MEREC-VIKOR, a hybrid standardized evaluation technique. MEREC-VIKOR generates the own performance metrics (S, R, and Q) of the proposed framework which is a combination of multi-conflicting criteria. The proposed framework is evaluated for attack detection by using CICIDS 2017 dataset. The experiments show that the proposed framework can detect cloud attacks accurately with low S (utility), R (regret), and Q (integration between S and R). The proposed framework is analyzed with respect to several evolutionary algorithms such as GA, IGASAA, and CMA-ES. The performance analysis demonstrates that the proposed framework that depends on CMSA-ES converges faster than the other evolutionary algorithms such as GA, IGASAA, and CMA-ES. The outcomes also demonstrate that the proposed model is comparable to the state-of-the-art techniques.