|  Green, Pervasive, and Cloud Computing: 13th International Conference, GPC 2018, Hangzhou, China, May 11-13, 2018, Revised Selected Papers (Lecture Notes in Computer Science (11204)), 9783030150921 ( 3030150925), Springer, 2019
This book constitutes the proceedings of the 13th International Conference on Green, Pervasive, and Cloud Computing, GPC 2018, held in Hangzhou, China, in May 2018.
The 35 full papers included in this volume were carefully reviewed and selected from 101 initial submissions. They are organized in the following topical sections: network security, and privacy-preserving; pervasive sensing and analysis; cloud computing, mobile computing, and crowd sensing; social and urban computing; parallel and distributed systems, optimization; pervasive applications; and data mining and knowledge mining.
In recent years, wireless networks are becoming more and more popular. Wireless Local Area Networks are deployed both in the company and in public or home. A vibrant access point AP is also hugely convenient for people, especially those who use mobile terminals. The number of mobile users has been increasing these years, and the field is continually covering social, games, video, news, finance and so on. More and more users tend to use the mobile to interact, which is the natural advantage of the mobile terminal, but also make it a target of public criticism.
Therefore, how to make up for the loopholes in the mobile Internet as much as possible, and how to detect and prevent a variety of known and unknown intrusion is a critical thing.
This paper proposed a method of identifying complicated multistep attacks orient ing to wireless intrusion detection system (MSWIDS), which includes the submodules of alarm simplification, virtual topology graph (VTG) generator, logic attack graph (LAG) generator, attack signature database, attack path resolver and complex attack evaluation. Using introducing logic attack diagram and virtual topological graph, the
attach path was excavated. The experimental result showed that this identification method applies to the real scene of wireless intrusion detection, which plays certain significance to predict attackers’ ultimate attack intention.
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