TY - JOUR
AU1 - Li, Yan
AU2 - Wang, Chun-zi
AU3 - Li, Ying-chao
AB - Designed for scenarios, including both unknown and known sets of attack strategies, we propose a multi-view analysis method for the robust suppression of attack diffusion when the defense is limited. This method formally defines the optimal defensive utility in the attack and defensive process, respectively. We present a defensive subset mining algorithm and a solution for determining the best random defensive measures for a set of known attack strategies based on the accurate approximation of the local directed acyclic graphs. In addition to proving that the algorithms are polynomial time, we designed and implemented a multi-view visualized prototype system and used it to observe the method’s results. Simulations and multidimensional analysis show that this method provides a good compromise between optimal accuracy and efficiency.
TI - Multi-view analysis method for robust suppression of attack diffusion
JF - The Journal of Supercomputing
DO - 10.1007/s11227-019-03016-z
DA - 2020-01-23
UR - https://www.deepdyve.com/lp/springer-journals/multi-view-analysis-method-for-robust-suppression-of-attack-diffusion-L0bz4plsMN
SP - 411
EP - 426
VL - 76
IS - 1
DP - DeepDyve
ER -