This article analyzes the problem of filtering spam messages and addressing spam messages, Bayesian theorems based on artificial intelligence, LVQ algorithms (LVQ learning vector quantization) and a filtering scheme for systems based on neural networks. The direct construction of an effective neural network model of spam filtering using database recognition technology is considered. The parameters of access to the neural network how to include predefined statistical and non-statistical attributes of messages are given. The structure of neural network technology for classifying emails is also considered. The procedure for analyzing incoming data using the tool included in the analytical platform Deductor Studio 5.3 is described. as a result, a training kit is obtained that is suitable for use.
- A Grey,. et al. We read spam a lot: Prospective cohort study of unsolicited and unwanted academic invitations. BMJ 355, i5383, 2016.
- S.Mazzarello, M.Fralick, M.Clemons, “A simple approach for eliminating spam”, Curr Oncol2015;23:75-6. doi:10.3747/co.23.2860 pmid:26966417.
- Bekmuratov Tulkun, Botirov Fayzullajon, “Analysis of Integrated Neural Network Attack Detection System and User Behavior Models”, International Conference on Information Science and Communications Technologies (ICISCT), 2019, https://ieeexplore.ieee.org/document/9011869.
- M.Kozak, O.Iefremova, J.Hartley, “Spamming in scholarly publishing: A case study”, J Assoc Info Sci Tech, 2015, 10.1002/asi.23521.
- W.Wang, D.Zhou, “A multi-level approach to highly efficient recognition of Chinese spam short messages”, Front. Comput. Sci. 12, pp. 135–145, 2018.
- M.A.MOHAMMED, S.S.GUNASEKARAN, S.A.MOSTAFA, A.MUSTAFA, & M.K.A.GHANI, “Implementing an Agent-based Multi-Natural Language Anti-Spam Model”, In 2018 International Symposium on Agent, Multi-Agent Systems and Robotics (ISAMSR), pp. 1-5, 2018.
- M.A.SHAFI’I, M.S.A.LATIFF, H.CHIROMA, O.OSHO, G.ABDUL-SALAAM, A.I.Abubakar, & T.Herawan, “A review on mobile SMS spam filtering techniques”, IEEE, 2017.
- N.MIRZA, B.PATIL, T.MIRZA, & R.AUTI, “Evaluating efficiency of classifier for email spam detec-tor using hybrid feature selection approaches”, In Intelligent Computing and Control Systems (ICICCS), 2017 International Conference on, June, 2017, pp. 735-740.
- M.SINGH, “Classification of Spam Email Using Intelligent Water Drops Algorithm with Naïve Bayes Classifier”, In Progress in advanced computing & Intelligent Engineering, Springer, Singapore, pp. 133-138, 2019.
- F.PAGANI, M.De ASTIS, M.GRAZIANO, A.LANZI, & D.BALZAROTTI, “Measuring the Role of Grey-listing and Nolisting in Fighting Spam, In Dependable Systems and Networks (DSN)”, 2016 46th Annual IEEE/IFIP International Conference on, 2016, June, pp. 562-571.
- E.FERRARA, “Measuring social spam and the effect of bots on information diffusion in social media”, In Complex Spreading Phenomena in Social Systems, Springer, Cham, 2018, pp. 229-255.
Bekmuratov, Tulkun Fayzievich; Botirov, Fayzullajon Bakhtiyorovich; and Haydarov, Elshod Dilshod ugli
"ELECTRONIC SPAM FILTERING BASED ON NEURAL NETWORKS,"
Chemical Technology, Control and Management: Vol. 2020
, Article 10.
Available at: https://uzjournals.edu.uz/ijctcm/vol2020/iss3/10