Analysis of network information security issues under the background of big data

[1] Qian, Tong. (2022). Resilient decentralized optimization of chance constrained electricity-gas systems over lossy communication networks. Energy, 239, 122158. Search in Google Scholar

[2] Qian, Tong. (2019). Event-triggered updating method in centralized and distributed secondary controls for islanded microgrid restoration. IEEE Transactions on Smart Grid, 11(2), 1387-1395. Search in Google Scholar

[3] Fang, Chunhua. (2021). Mapping Relation of Leakage Currents of Polluted Insulators and Discharge Arc Area. Frontiers in Energy Research, 9, 777230. Search in Google Scholar

[4] Zhou, Wenhai. (2020). Chinese Independent Animation: Renegotiating Identity in Modern China. Springer Nature. Search in Google Scholar

[5] Wang, Hongman. (2020). An algorithm based on markov chain to improve edge cache hit ratio for blockchainenabled IoT. China Communications, 17(9), 66-76. Search in Google Scholar

[6] Fei, Dingzhou. (2019). Stochastic model for emotion contagion in social networks security based on machine learning. Safety science, 118, 757-762. Search in Google Scholar

[7] Afzal, Humaira. (2019). Performance analysis of radio spectrum for cognitive radio wireless networks using discrete time Markov chain. Journal of Systems and Software, 151, 1-7. Search in Google Scholar

[8] Tereikovskiy, Ihor. (2018). Markov model of normal conduct template of computer systems network objects. 2018 14th International Conference on Advanced Trends in Radioelecrtronics, Telecommunications and Computer Engineering (TCSET). IEEE. Search in Google Scholar

[9] Zhang, Tao. (2020). Research on privacy security risk assessment method of mobile commerce based on information entropy and Markov. Wireless Communications and Mobile Computing 2020. Search in Google Scholar

[10] Li, Hui-Jia. (2021). Measuring the network vulnerability based on Markov criticality. ACM Transactions on Knowledge Discovery from Data (TKDD), 16(2), 1-24. Search in Google Scholar

[11] Herath, Hemantha SB, and Tejaswini C. Herath. (2018). Post-audits for managing cyber security investments: Bayesian post-audit using Markov Chain Monte Carlo (MCMC) simulation. Journal of Accounting and Public Policy, 37(6), 545-563. Search in Google Scholar

[12] Li, Qianmu. (2019). Time series association state analysis method for attacks on the smart internet of electric vehicle charging network. Transportation Research Record, 2673(4), 217-228. Search in Google Scholar

[13] Liang, Wei. (2018). A security situation prediction algorithm based on HMM in mobile network. Wireless Communications and Mobile Computing 2018. Search in Google Scholar

[14] Sengan, Sudhakar. (2022). Secured and privacy-based IDS for healthcare systems on E-medical data using machine learning approach. International Journal of Reliable and Quality E-Healthcare (IJRQEH), 11(3), 1-11. Search in Google Scholar

[15] Wang, Zheng. (2022). Learning-Aided Markov Chain Monte Carlo Scheme for Spectrum Sensing in Cognitive Radio. IEEE Transactions on Vehicular Technology. Search in Google Scholar

[16] Wei, Lei. (2022). Application of Bayesian Algorithm in Risk Quantification for Network Security. Computational Intelligence and Neuroscience 2022. Search in Google Scholar

[17] Agajo, James. (2022). Development A Web-Based System for Real Time Prediction of Drought in Northern Nigeria Using Markov Chain Technique. 2022 IEEE Nigeria 4th International Conference on Disruptive Technologies for Sustainable Development (NIGERCON). IEEE. Search in Google Scholar

[18] Ram, Mahendra. (2019). Enabling green wireless sensor networks: Energy efficient T-MAC using Markov chain based optimization. Electronics, 8(5), 534. Search in Google Scholar

[19] Ugurlu, Omer Faruk, and Mustafa Kumral. (2020). Reliability-based performance analysis of mining drilling operations through Markov chain Monte Carlo and mean reverting process simulations. Simulation, 96(7), 593-604. Search in Google Scholar

[20] Song, Xiaona. (2020). Gain-scheduled finite-time synchronization for reaction–diffusion memristive neural networks subject to inconsistent Markov chains. IEEE Transactions on Neural Networks and Learning Systems, 32(7), 2952-2964. Search in Google Scholar

[21] D’Angelo, Gianni, Massimo Ficco, and Francesco Palmieri. (2021). Association rule-based malware classification using common subsequences of API calls. Applied Soft Computing, 105 107234. Search in Google Scholar

[22] Hoffmann, Romuald. (2019). Markov models of cyber kill chains with iterations. 2019 International Conference on Military Communications and Information Systems (ICMCIS). IEEE. Search in Google Scholar

[23] Fattah, Md, Syed Riad Morshed, and Syed Yad Morshed. (2021) Multi-layer perceptron-Markov chain-based artificial neural network for modelling future land-specific carbon emission pattern and its influences on surface temperature. SN Applied Sciences, 3(3), 1-22. Search in Google Scholar

[24] Tian, Yuan. (2019). A privacy preserving location service for cloud-of-things system. Journal of Parallel and Distributed Computing, 123, 215-222. Search in Google Scholar

[25] Fattah, Md, Syed Riad Morshed, and Syed Yad Morshed. (2021). Multi-layer perceptron-Markov chain-based artificial neural network for modelling future land-specific carbon emission pattern and its influences on surface temperature. SN Applied Sciences, 3(3), 1-22. Search in Google Scholar

[26] Liang, Junwei, and Maode Ma. (2020). ECF-MRS: An efficient and collaborative framework with Markov-based reputation scheme for IDSs in vehicular networks. IEEE transactions on information forensics and security, 16, 278-290. Search in Google Scholar

[27] Tian, Yuan. (2019). A privacy preserving location service for cloud-of-things system. Journal of Parallel and Distributed Computing, 123, 215-222. Search in Google Scholar

[28] Ren, Xin. (2018). A web attack detection technology based on bag of words and hidden Markov model. 2018 IEEE 15th International Conference on Mobile Ad Hoc and Sensor Systems (MASS). IEEE. Search in Google Scholar

[29] Ning, Qian. (2019). Multi-UAVs trajectory and mission cooperative planning based on the Markov model. Physical communication, 35, 100717. Search in Google Scholar

[30] Barakat, Ghena (2022). Modeling and Simulation of IoT Botnet Behaviors Using DEVS. 2022 13th International Conference on Information and Communication Systems (ICICS). IEEE. Search in Google Scholar

[31] Janakiraman, Sengathir, and Bipin Bihari Jayasingh (2019). A hyper-exponential factor-based semi-Markov prediction mechanism for selfish rendezvous nodes in MANETs. Wireless Personal Communications, 108(3), 1493-1511. Search in Google Scholar

[32] Cui, Mengtian, and Yi-Cheng Zhang. (2018). Application of Markov chain approach for multi-attributes dynamic software reliability assessment under both AHP and gray correlation methods. International Journal of Modern Physics C, 29(5), 1840008. Search in Google Scholar

[33] Liu, Chang. (2018). Lafft: Length-aware fft based fingerprinting for encrypted network traffic classification. 2018 IEEE Symposium on Computers and Communications (ISCC). IEEE. Search in Google Scholar

[34] Kandhoul, Nisha, and Sanjay Kumar Dhurandher. (2018). An asymmetric RSA-based security approach for opportunistic IoT. International Conference on Wireless Intelligent and Distributed Environment for Communication. Springer, Cham. Search in Google Scholar

[35] Yao, Zhongjiang. (2020). Encrypted traffic classification based on Gaussian mixture models and Hidden Markov Models. Journal of Network and Computer Applications, 166, 102711. Search in Google Scholar