دانلود مقاله ISI انگلیسی شماره 78781
ترجمه فارسی عنوان مقاله

مدل تجزیه و تحلیل خطر امنیتی برای سیستم های اطلاعاتی: رابطه علت ومعلولی از عوامل خطر و تجزیه و تحلیل آسیب پذیری انتشار

عنوان انگلیسی
A security risk analysis model for information systems: Causal relationships of risk factors and vulnerability propagation analysis
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
78781 2014 17 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Information Sciences, Volume 256, 20 January 2014, Pages 57–73

ترجمه کلمات کلیدی
سیستم های اطلاعاتی؛ خطر امنیتی؛ شبکه های بیزی - بهینه سازی کلونی مورچه؛ انتشار آسیب پذیری
کلمات کلیدی انگلیسی
Information systems; Security risk; Bayesian networks; Ant colony optimization; Vulnerability propagation
پیش نمایش مقاله
پیش نمایش مقاله  مدل تجزیه و تحلیل خطر امنیتی برای سیستم های اطلاعاتی: رابطه علت ومعلولی از عوامل خطر و تجزیه و تحلیل آسیب پذیری انتشار

چکیده انگلیسی

With the increasing organizational dependence on information systems, information systems security has become a very critical issue in enterprise risk management. In information systems, security risks are caused by various interrelated internal and external factors. A security vulnerability could also propagate and escalate through the causal chains of risk factors via multiple paths, leading to different system security risks. In order to identify the causal relationships among risk factors and analyze the complexity and uncertainty of vulnerability propagation, a security risk analysis model (SRAM) is proposed in this paper. In SRAM, a Bayesian network (BN) is developed to simultaneously define the risk factors and their causal relationships based on the knowledge from observed cases and domain experts. Then, the security vulnerability propagation analysis is performed to determine the propagation paths with the highest probability and the largest estimated risk value. SRAM enables organizations to establish proactive security risk management plans for information systems, which is validated via a case study.