One of the key issues of information security is software vulnerabilities in computer systems. Malicious attackers can exploit security vulnerabilities to gain privileges, access unauthorized system resources, and even change sensitive data. The main purpose of this paper is the vulnerability model of distributed systems based on reliability theory. This paper directly analyzes the machine instruction, formulates the corresponding standard model, and studies the corresponding model control method. This paper presents an analytical method that uses reliability theory to introduce the Fuzzing random test method. The method uses static analysis techniques to gather information about the structure of the system, its interfaces, and useful code regions, and then develops test problems for executing the useful code regions—for which this paper uses genetic algorithms to understand. It guides the generation of test data and overcomes the shortcomings of random fuzzing methods in terms of large and unpredictable test data sets. The method also analyzes files that can be executed directly without the source code of the program. Experiments show that the vulnerability model system constructed in this paper is more realistic. The parallel login response time of the system in this paper is about 0.12s, and the parallel use response time is about 0.2s.

Patil Rajendra. Distributed System Vulnerability Model Based on Reliability Theory. Distributed Processing System (2021), Vol. 2, Issue 4: 1-9. https://doi.org/10.38007/DPS.2021.020401.

[1] Barbosa K, Fernandes M. Elderly vulnerability: concept development.. Revista brasileira de enfermagem, 2020, 73(suppl 3):e20190897.

[2] Ncube A, Tawodzera M. Communities' perceptions of health hazards induced by climate change in Mount Darwin district, Zimbabwe.. Jamba (Potchefstroom, South Africa), 2019, 11(1):748. https://doi.org/10.4102/jamba.v11i1.748

[3] Zang T, Gao S, Liu B, et al. Integrated fault propagation model based vulnerability assessment of the electrical cyber-physical system under cyber attacks. Reliability Engineering & System Safety, 2019, 189(SEP.):232-241.

[4] Lujano-Rojas J, José Yusta, José Domínguez-Navarro. Mitigating Energy System Vulnerability by Implementing a Microgrid with a Distributed Management Algorithm. Energies, 2019, 12(4):616. https://doi.org/10.3390/en12040616

[5] Gautam P, Piya P, Karki R. Development and Integration of Momentary Event Models in Active Distribution System Reliability Assessment. IEEE Transactions on Power Systems, 2019, PP(99):1-1.

[6] Xin, Gai, Chunyu, et al. Type the National Vulnerability Evaluation System Research Based on Fuzzy Comprehensive Evaluation. IOP Conference Series: Materials Science and Engineering, 2019, 490(6):62039-62039.

[7] Taylor R, Mitchell G, Andrade J, et al. Expression of Collagen Types I, II, IX, and X in the Mineralizing Turkey Gastrocnemius Tendon.. Anatomical record (Hoboken, N.J. : 2007), 2020, 303(6):1664-1669. https://doi.org/10.1002/ar.24091

[8] Arslan S S. A Reliability Model for Dependent and Distributed MDS Disk Array Units. IEEE Transactions on Reliability, 2019, 68(1):133-148.

[9] Zareie S, Hazbavi Z, Mostafazadeh R, et al. Vulnerability Comparison of Samian Sub-watersheds based on Climate Change Components. Physical Geography, 2021, 52(2):217-236.

[10] Karngala A K, Singh C. Reliability Assessment Framework for the Distribution System Including Distributed Energy Resources. IEEE Transactions on Sustainable Energy, 2021, PP(99):1-1.

[11] Zhang Y, Wang Y, Wang L, et al. An Extended Object-Oriented Petri Net Model for Vulnerability Evaluation of Communication-Based Train Control System. Symmetry, 2020, 12(9):1474. https://doi.org/10.3390/sym12091474

[12] Choi B, Sohn J Y, Yoon S W, et al. Secure Clustered Distributed Storage Against Eavesdropping. IEEE Transactions on Information Theory, 2019, 65(11):7646-7668.

[13] Kalla H, Farid A. Task allocation based modified bacterial foraging algorithm for maximising reliability of a heterogeneous distributed system. International Journal of Communication Networks and Distributed Systems, 2019, 23(1):1.

[14] Jagannathan J, Parvees M. Vulnerability Recognition and Resurgence in Network based on Prediction Model and Cognitive based Elucidation. Journal of Physics: Conference Series, 2021, 2070(1):012122-.

[15] Ciminello M. Reliability of structural health monitoring system based on distributed fiber optic and autocorrelation of the first order derivative of strain. IEEE Sensors Journal, 2019, PP(99):1-1.

[16] Ustun, Ayyubi. Automated Network Topology Extraction Based on Graph Theory for Distributed Microgrid Protection in Dynamic Power Systems. Electronics, 2019, 8(6):655.

[17] Fichera A, Marrasso E, Sasso M, et al. Energy, Environmental and Economic Performance of an Urban Community Hybrid Distributed Energy System. Energies, 2020, 13(10):2545. https://doi.org/10.3390/en13102545

[18] Bogdanovich V I, Giorbelidze M G. Ion-Plasma Treated Parts Quality Improvement Analysis Based on the Reliability Theory Criteria. IOP Conference Series Materials Science and Engineering, 2021, 1118(1):012004. https://doi.org/10.1088/1757-899X/1118/1/012004