Quantitative security evaluation of a multi-biometric authentication system
SAFECOMP'12 Proceedings of the 2012 international conference on Computer Safety, Reliability, and Security
Towards modelling adaptive attacker's behaviour
FPS'12 Proceedings of the 5th international conference on Foundations and Practice of Security
A stochastic model of attack process for the evaluation of security metrics
Computer Networks: The International Journal of Computer and Telecommunications Networking
A methodology and supporting techniques for the quantitative assessment of insider threats
Proceedings of the 2nd International Workshop on Dependability Issues in Cloud Computing
Go with the flow: toward workflow-oriented security assessment
Proceedings of the 2013 workshop on New security paradigms workshop
| Hi-index | 0.00 |
System architects need quantitative security metrics to make informed trade-off decisions involving system security. The security metrics need to provide insight on weak points in the system defense, considering characteristics of both the system and its adversaries. To provide such metrics, we formally define the ADversary View Security Evaluation (ADVISE) method. Our approach is to create an executable state-based security model of a system and an adversary that represents how the adversary is likely to attack the system and the results of such an attack. The attack decision function uses information about adversary attack preferences and possible attacks against the system to mimic how the adversary selects the most attractive next attack step. The adversary's decision involves looking ahead some number of attack steps. System architects can use ADVISE to compare the security strength of system architecture variants and analyze the threats posed by different adversaries. We demonstrate the feasibility and benefits of ADVISE using a case study. To produce quantitative model-based security metrics, we have implemented the ADVISE method in a tool that facilitates user input of system and adversary data and automatically generates executable models.