Machine Learning Theory and Practice, 2020, 1(4); doi: 10.38007/ML.2020.010406.
Jiayao Ji
The People’s Procuratorate of Shanghai Hudong District, Hongkou, Shanghai, China
With the rapid development and widespread popularity of the Internet, the types and quantities of abnormal traffic are also increasing day by day. Network intrusion detection has become an important fortress to ensure network information security. People put forward more and more urgent demands for the robustness and development of network security technology. Therefore, this paper studies information security technology based on machine learning. In this paper, the concepts of network security situation assessment and network intrusion detection are described in detail at first, then the machine based learning data preprocessing module, network vulnerability level and coarse and fine granularity hybrid detection system are designed, and finally the detection performance and defense performance of fine granularity hybrid detection system are analyzed in detail, and a conclusion is drawn.
Machine Learning, Network Security, Information Security Technology, Detection System
Jiayao Ji. Research Information Security Technology Based on Machine Learning. Machine Learning Theory and Practice (2020), Vol. 1, Issue 4: 45-52. https://doi.org/10.38007/ML.2020.010406.
[1] Ons Aouedi , Kandaraj Piamrat, Salima Hamma, Menuka Perera Jayasuriya Kuranage: Network traffic analysis using machine learning: an unsupervised approach to understand and slice your network. Ann. Des Telecommunications 77(5-6): 297-309 (2020).
[2] Abigail Goldsteen, Gilad Ezov, Ron Shmelkin, Micha Moffie, Ariel Farkash: Data minimization for GDPR compliance in machine learning models. Al Ethics 2(3): 477-491 (2020).
[3] Suresh Dara, Swetha Dhamercherla, Surender Singh Jadav, Ch Madhu Babu, Mohamed Jawed Ahsan : Machine Learning in Drug Discovery: A Review. Artif. Intell. Rev. 55(3): 1947-1999 (2020).
[4] Leandro Miranda, Jose Viterbo, Flavia Bernardini: A survey on the use of machine learning methods in context-aware middlewares for human activity recognition. Artif. Intell. Rev. 55(4): 3369-3400 (2020).
[5] Muhammad Waqas, Shanshan Tu, Zahid Halim, Sadaqat ur Rehman, Ghulam Abbas, Ziaul Haq Abbas: The role of artificial intelligence and machine learning in wireless networks security: principle, practice and challenges. Artif. Intell. Rev. 55(7): 5215-5261 (2020).
[6] Simon Penny: Review of Art in the Age of Machine Learning by Sofian Audry. Artif. Life 28(1): 167-169 (2020). https://doi.org/10.1162/artl_r_00352
[7] Zied Ftiti, Kais Tissaoui, Sahbi Boubaker: On the relationship between oil and gas markets: a new forecasting framework based on a machine learning approach. Ann. Oper. Res.313(2): 915-943 (2020). https://doi.org/10.1007/s10479-020-03652-2
[8] Dieudonne Tchuente, Serge Nyawa: Real estate price estimation in French cities using geocoding and machine learning. Ann. Oper. Res. 308(1): 571-608 (2020).
[9] Omer Faruk Beyca, Ibrahim Yazici, Omer Faruk Gurcan, Halil Zaim, Dursun Delen,Selim Zaim: A comparative analysis of machine learning techniques and fuzzy analytic hierarchy process to determine the tacit knowledge criteria. Ann. Oper. Res.308(1): 753-776 (2020). https://doi.org/10.1007/s10479-020-03697-3
[10] Koushiki Dasgypta Chaudhuri, Bugra Alkan: A hybrid extreme learning machine model with harris hawks optimisation algorithm: an optimised model for product demand forecasting applications. Appl.Intell. 52(10): 11489-11505 (2020).
[11] Pradip Dhal, Chandrashekhar Azad: A comprehensive survey on feature selection in the various fields of machine learning. Appl. Intell. 52(4): 4543-4581 (2020).
[12] Marta Fernandes , Juan Manuel Corchado, Goreti Marreiros: Machine learning techniques applied to mechanical fault diagnosis and fault prognosis in the context of real industrial manufacturing use-cases: a systematic literature review. Appl. Intell. 52(12): 14246-14280 (2020).
[13] Ekaterina Gurina, Nikita Klyuchnikov, Ksenia Antipova, Dmitry A. Koroteev: Forecasting the abnormal events at well drilling with machine learning. Appl. Intell. 52(9): 9980-9995 (2020).
[14] Elena Hernandez-Pereira, Oscar Fontenla-Romero, Veronica Bolon-Canedo, Brais Cancela-Barizo, Bertha Guijarro-Berdinas, Amparo Alonso-Betanzos: Machine learning techniques to predict different levels of hospital care of CoVid-19. Appl. Intell. 52(6): 6413-6431 (2020).
[15] luri Krak, Olexander Barmak , Eduard Manziuk : Using visual analytics to develop human and machine-centric models: A review of approaches and proposed information technology. Comput. Intell. 38(3): 921-946 (2020). https://doi.org/10.1111/coin.12289
[16] Atika Qazi o, Najmul Hasan, Olusola Abayomi-Alli, Glenn Hardaker o, Ronny Scherer, Yeahia Sarker, Sanjoy Kumar Paul, Jaafar Zubairu Maitama: Gender differences in information and communication technology use & skills: a systematic review and meta-analysis. Educ. Inf. Technol. 27(3): 4225-4258 (2020).
[17] Ichiro Ide, Huynh Thi Thanh Binh: Special issue on "The Eighth International Symposium on Information and Communication Technology- SolCT 2017". J. Heuristics 28(2): 147-148 (2020).