Optimal security patch management tool design based on probabilistic modeling and analysis

2023 Fiscal Year Final Research Report

• Project/Area Number: 21K17742
• Research Category: Grant-in-Aid for Early-Career Scientists
• Allocation Type: Multi-year Fund
• Review Section: Basic Section 60070:Information security-related
• Research Institution: Osaka University (2022-2023); Ritsumeikan University (2021)
• Principal Investigator: Zheng Junjun 大阪大学, 大学院情報科学研究科, 特任助教(常勤) (80822832)
• Project Period (FY): 2021-04-01 – 2024-03-31
• Keywords: 耐侵入システム / 確率モデル / マルコフ再生過程 / 位相型近似 / 感度分析 / パッチ管理

Outline of Final Research Achievements

This study focuses on developing an optimal security patch management tool for intrusion-tolerant systems using probabilistic models. The system behavior is modeled using Markov regenerative processes, and the optimal patch application timing is determined from both security and cost perspectives. Sensitivity analysis is conducted to optimize the system design by identifying parameters that significantly impact system reliability and performance. Additionally, deep learning techniques are employed to propose efficcient methods for malware detection and classification, enhancing system safety and availability. A hierarchical modeling approach is proposed for calculating performance measures for multi-state systems, enabling the determination of optimal patch application strategies from various performance perspectives. This study strengthened the theoretical foundation of intrusion-tolerant systems and marked a significant step towards practical implementation.

Free Research Field

情報学

Academic Significance and Societal Importance of the Research Achievements

本研究は，耐侵入システムの理論的基盤を強化し，最適なセキュリティパッチ管理を実現するための新しいアプローチを提供した点で学術的意義がある．確率モデルと深層学習を組み合わせることで，システムの信頼性と安全性を向上させる手法を確立した．また、これによりシステム管理者がより効率的にセキュリティパッチを適用できるようになり，サイバー攻撃に対する防御力が向上する社会的意義も大きい．実用化に向けた重要なステップを踏み出した本研究は，セキュリティ技術の発展に寄与している．