N.N. Samarin

This article proposes a method of fuzz testing mail services based on an intelligent input parameter mutation, which is applicable not only to standard email protocols but also to their extended versions. The research examines the main extensions of POP, IMAP, and SMTP protocols. The described method was applied in laboratory conditions to evaluate its effectiveness compared to traditional fuzz testing methods. The results confirmed the possibility of using the method for fuzz testing not only email clients but also servers. Further research is planned to create intelligent methods for evaluating testing coverage in the absence of prior instrumentation of email client and server source code.

Samarin N.N. Development of the method for fuzz testing mail services based on an intelligent input parameter mutation. Radiotekhnika. 2024. V. 88. № 2. P. 53−61. DOI: https://doi.org/10.18127/j00338486-202402-08 (In Russian)

1. Gavriljuk V.I. Protokoly POP, IMAP, SMTP: osnovnye principy i primenenie. Molodoj uchenyj. 2020. № 19(309). S. 119-121 (in Russian).
2. Kljueva E.G., Shorin A.N. Analiz bezopasnosti pochtovyh serverov, funkcionirujushhih pod upravleniem operacionnyh sistem Linux/Windows. Informacionnye tehnologii v obrazovanii i nauke: Sb. materialov nauch.-praktich. mezhvuzov. internet-konferencii s mezhdunarodnym uchastiem, posvjashh. 70-letiju Atyrauskogo gos. un-ta im. Halela Dosmuhamedova, Atyrau (8 oktjabrja 2019 g.). Atyrau: Atyrauskij gos. un-t im. Halela Dosmuhamedova. 2019. S. 156-159 (in Russian).
3. Lobov I.Zh. Analiz ustojchivosti pochtovogo servera Microsoft exchange k setevym ugrozam na osnove rezul'tatov modelirovanija. Voprosy ustojchivogo razvitija obshhestva. 2022. № 9. S. 334-343 (in Russian).
4. Golovina E.Ju., Farhutdinova A.I. Razrabotka programmnogo sredstva proverki jelektronnyh pisem dlja pochtovogo servera predprijatija. Informacionnye tehnologii. Problemy i reshenija. 2022. № 2(19). S. 62-67 (in Russian).
5. Chernyshov M.K. Ispol'zovanie mehanizma Split DNS pri razvertyvanii korporativnogo pochtovogo servera. Informatika: problemy, metody, tehnologii: Materialy XX Mezhdunar. nauch.-metodich. konf. (g. Voronezh, 13–14 fevralja 2020 g.). Pod red. A.A. Zacarinnogo, D.N. Borisova. Voronezh: OOO «Vjelborn». 2020. S. 210-214 (in Russian).
6. Mahmoud A.B., Grigoriou N., Fuxman L., et al. Email is evil ! Behavioural responses towards permission-based direct email marketing and gender differences. Journal of Research in Interactive Marketing. 2019. V. 13. № 2. P. 227-248. DOI: 10.1108/JRIM-09-2018-0112.
7. Sobotta N. Why forwarded email threads are hard to read: The Email format as an antecedent of Email overload. Communications of the Association for Information Systems. 2016. V. 39. № 1. P. 16-31. DOI: 10.17705/1cais.03902.
8. Gan S., Qin X., Tu X., et al. Path Sensitive Fuzzing for Native Applications. IEEE Transactions on Dependable and Secure Computing. 2022. V. 19. № 3. P. 1544-1561. DOI: 10.1109/TDSC.2020.3027690.
9. Zakeri Nasrabadi M., Parsa S., Kalaee A. Format-aware learn&fuzz: deep test data generation for efficient fuzzing. Neural Computing & Applications. 2021. V. 33. № 5. P. 1497-1513. DOI: 10.1007/s00521-020-05039-7.
10. Zimin E.E. Metodika fazzing-testirovanija koda s pomoshh'ju AFL. Bezopasnye informacionnye tehnologii: Sb. trudov XI Mezhdunar. nauch.-tehnich. konf. (Moskva, 6–7 aprelja 2021 g.). M.: MGTU imeni N.Je. Baumana (NIU). 2021. S. 124-129 (in Russian).
11. Tronov K.A., Belov Ju.S. Optimizacija instrumentarija afl dlja luchshego pokrytija koda pri rabote so specifichnymi dannymi. E-Scio. 2021. № 5(56). S. 566-571 (in Russian).
12. Alekseev D.M., Ivanenko K.N., Ubirajlo V.N. Fuzzing kak metod testirovanija programm: preimushhestva i nedostatki. Nauka v sovremennom obshhestve: zakonomernosti i tendencii razvitija: Sb. statej mezhdunar. nauch.-praktich. konf. (g. Perm', 25 fevralja 2017 g.) V 2-h chastjah. Ch. 2. Perm': OOO «Ajeterna». 2017. S. 18-19 (in Russian).
13. Jazov Ju.K., Kadykov V.B., Enjutin A.Ju., Suhovephov A.S. Ispol'zovanie tehnologii fazzinga dlja poiska ujazvimostej v programmno-apparatnyh sredstvah avtomatizirovannyh sistem upravlenija tehnologicheskimi processami. Programmnaja inzhenerija. 2011. № 6.
    S. 44-47 (in Russian).
14. Cummins C., Petoumenos P., Leather H., Murray A. Compiler fuzzing through deep learning. ISSTA 2018. Proceedings of the 27th ACM SIGSOFT International Symposium on Software Testing and Analysis. Amsterdam. 2018. P. 95-105. DOI: 10.1145/3213846.3213848.
15. Azarnova T.V., Polukhin P.V. Dynamic Bayesian Networks as a Testing Tool for Fuzzing Web Applications. Computational Mathematics and Mathematical Physics. 2021. V. 61. № 7. P. 1118-1128. DOI: 10.1134/S0965542521070058.
16. Chen J., Wang X., Xu X. GC-LSTM: graph convolution embedded LSTM for dynamic network link prediction. Applied Intelligence. 2021. DOI: 10.1007/s10489-021-02518-9.
17. Jakimenko V.V. Arhitektura modeli LSTM dlja prognozirovanija vremennyh rjadov redkih sobytij. Aktual'nye problemy aviacii i kosmonavtiki: Sb. materialov VIII Mezhdunar. nauch.-praktich. konf., posvjashh. Dnju kosmonavtiki (g. Krasnojarsk, 11–15 aprelja 2022 g.). V 3-h tomah. T. 2. Krasnojarsk: FGBU VO «Sibirskij gosudarstvennyj universitet nauki i tehnologij im. akad. M.F. Reshetneva». 2022. S. 533-535 (in Russian).
18. Liu Y.C., Tsai Y.C., Li K.Y. Spindle thermal error prediction based on lstm deep learning for a cnc machine tool. Applied Sciences (Switzerland). 2021. V. 11. № 12. DOI: 10.3390/app11125444.
19. Kondrat'eva T.N. Prognozirovanie s pomoshh'ju mnogoslojnoj rekurrentnoj nejronnoj seti LSTM. Obozrenie prikladnoj i promyshlennoj matematiki. 2017. T. 24. № 1. S. 73-74 (in Russian).
20. Dukhan Ye.I., Voyevodin S.V., Sazonov V.Yu., Zvezhinskiy S.S. Obobshchennaya metodika izmereniya kharakteristik kharakteristik obnaruzheniya na osnove metoda mashinnogo eksperimenta. Radiotekhnika. T.86. № 1. 2022. S. 41-48. DOI: https://doi.org/10.18127/j00338486-202201-07 (in Russian).