Increasing The Efficiency of Damaged File Detection Tools Based on The Use of Hidden Markov Models
Keywords:
Heuristic model, statistical model, meta-, polymorphism, artificial neural networks, infection, Snort system, heuristic analysis, imitation, behavioral analysis, signature search, Anomaly detection.Abstract
This paper reviews a new algorithm developed to improve the performance of file detection based on hidden Markov models (HMM). Hidden Markov models are an effective method for detecting various types of disturbances and damages using time series and probabilities. The algorithm uses learning-based technologies to quickly and accurately detect file corruption. The article is based on recovery of damaged files, detection and analysis of changes in their structure. Monitoring file corruption processes through hidden Markov models increases the possibility of correctly predicting errors. The algorithm's performance is more efficient in more complex structures compared to simple statistics, it quickly detects file integration violations and creates a recovery mechanism. The main part of the article provides a detailed explanation of the mechanisms for explaining and predicting possible file corruption or invalidation using HMM. The new algorithm is designed to improve security and speed up the recovery of damaged files.
References
Marcus Botacin, Felipe Duarte Domingues, Fabrício Ceschin, Raphael Machnicki, Marco Antonio Zanata Alves, Paulo Lício de Geus, André Grégio, AntiViruses under the microscope: A hands-on perspective, ISSN 0167-4048,: https://doi.org/10.1016/j.cose.2021.102500, 2022.
СПОСОБ ГГОВЫШЕШШ ЭФФЕКТИВНОСТИ СРЕДСТВ ВЫЯВЛЕНИЯ ЗАРАЖЕННЫХ ФАЙЛОВ НА ОСНОВЕ ИСНОЛЬЗОВАИИЯ СКРЫТЫХ МАРКОВСКИХ ¡МОДЕЛЕЙ, Эдел Дмитрий Александрович, Ростов-на-Дону, 2013.
Jiaru Song, Guihe Qin, Yanhua Liang, Jie Yan, Minghui Sun,, DGIDS: Dynamic graph-based intrusion detection system for CAN,, ISSN 0167-4048,: https://doi.org/10.1016/j.cose.2024.104076, 2024.
Carlos Henrique Macedo dos Santos, Sidney Marlon Lopes de Lima,, XAI-driven antivirus in pattern identification of citadel malware,, ISSN 1877-7503,: https://doi.org/10.1016/j.jocs.2024.102389, 2024,.
Rajasekhar Chaganti, Vinayakumar Ravi, Tuan D. Pham,, Deep learning based cross architecture internet of things malware detection and classification,, ISSN 0167-4048,: https://doi.org/10.1016/j.cose.2022.102779, 2022.
Timothy McIntosh, Paul Watters, A.S.M. Kayes, Alex Ng, Yi-Ping Phoebe Chen,, Enforcing situation-aware access control to build malware-resilient file systems,, ISSN 0167-739X,: https://doi.org/10.1016/j.future.2020.09.035, 2021.
Manuel Navarro-García, Vanesa Guerrero, María Durban, Arturo del Cerro,, Feature and functional form selection in additive models via mixed-integer optimization,, ISSN 0305-0548: https://doi.org/10.1016/j.cor.2024.106945, 2025.
Rui Liu, Xiaoli Zhang,, Generating machine-executable plans from end-user's natural-language instructions,, ISSN 0950-7051: https://doi.org/10.1016/j.knosys.2017.10.023, 2018.
Muhammad Mudassar Yamin, Basel Katt,, Modeling and executing cyber security exercise scenarios in cyber ranges,, ISSN 0167-4048,: https://doi.org/10.1016/j.cose.2022.102635, 2022.
Hasan H. Al-Khshali, Muhammad Ilyas,, Impact of Portable Executable Header Features on Malware Detection Accuracy,, ISSN 1546-2218,: https://doi.org/10.32604/cmc.2023.032182, 2022.
