Network Security Situation Analysis of Weighted Neural Network with Association Rules Mining
- DOI
- 10.2991/icamcs-16.2016.9How to use a DOI?
- Keywords
- Tristate Wireless Sensor Networks, Multistate and Multiple-Valued Decision Diagram, Reliability Assessment, Transmission Delay of Data, Imperfect Cover, Common Cause Failure
- Abstract
In order to assess reliability of polymorphic wireless sensor networks, specific to characteristics of communication delays, imperfect cover (IPC) and common cause failure (CCF) which are different from general network, a reliability evaluation model of multistate WSN is constructed, which simultaneously considers processing delay of node data, IPC and CCF. Specific to the problem of “combinatorial explosion” produced by system state space of multimode WSN with the increasing of node number, in order to avoid solving the minimal path set, multistate and multiple-valued decision diagram (MMDD) is introduced to build WSN as required. By just constructing one MMDD, according to common cause set, reliability of WSN can be calculated under the effect of IPC and CCF, which effectively controls the space complexity of the algorithm. Experimental results show that the provided MMDD algorithm can complete reliability assessment task of polymorphic WSN constrained by IPC CCF and time delay. The calculated reliability value is lower than the result of solution method for model of WSN ignoring the network congestion and the influence of CCF and IPC.
- Copyright
- © 2016, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Jie Du AU - Hongna Luo PY - 2016/06 DA - 2016/06 TI - Network Security Situation Analysis of Weighted Neural Network with Association Rules Mining BT - Proceedings of the 2016 5th International Conference on Advanced Materials and Computer Science PB - Atlantis Press SP - 37 EP - 41 SN - 2352-5401 UR - https://doi.org/10.2991/icamcs-16.2016.9 DO - 10.2991/icamcs-16.2016.9 ID - Du2016/06 ER -