Modeling Malware Propagation Dynamics and Developing Prevention Methods in Wireless Sensor Networks

Zaobo He (), Yaguang Lin, Yi Liang, Xiaoming Wang, Akshita Maradapu Vera Venkata Sai and Zhipeng Cai ()
Additional contact information
Zaobo He: Miami University
Yaguang Lin: School of Computer Science, Shaanxi Normal University
Yi Liang: Georgia State University
Xiaoming Wang: School of Computer Science, Shaanxi Normal University
Akshita Maradapu Vera Venkata Sai: Georgia State University
Zhipeng Cai: Georgia State University

A chapter in Nonlinear Combinatorial Optimization, 2019, pp 231-250 from Springer

Abstract: Abstract Modeling malware propagation dynamics and developing prevention methods are very imperative with flourishing and advancement of WSN technologies in a variety of fields, such as smart cities. In the last decade, a lot of effort has been put into designing effective models to characterize the propagation dynamics of malware and developing effective prevention methods, with different focuses such as spatial–temporal model, pulse immunization, trade-off model between prevention cost and network utility, etc. This chapter reviews the state-of-the-art malware modeling and prevention method to present a comprehensive guide on how to choose a more appropriate approach for different applications. First, the application background and definitions of WSNs and malware are introduced, followed by the challenges of modeling malware propagation dynamics and developing prevention methods. Second, the recent advances in modeling and prevention methods are summarized. Third, four recently published papers that focus on spatial–temporal modeling, pulse immunization, and cost-efficiency trade-off are introduced. Finally, this chapter is ended by pointing out some possible future research directions.

Date: 2019
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-3-030-16194-1_10

Ordering information: This item can be ordered from
http://www.springer.com/9783030161941

DOI: 10.1007/978-3-030-16194-1_10

Access Statistics for this chapter

More chapters in Springer Optimization and Its Applications from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().