Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/26275
Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings
Authors: Alharbi, Hani
Aloufi, Khalid
Hussain, Amir
Contact Email: ahu@cs.stir.ac.uk
Title: A new biologically-inspired analytical worm propagation model for mobile unstructured peer-to-peer networks
Editors: Liu, C-L
Hussain, A
Luo, B
Tan, KC
Zeng, Y
Zhang, Z
Citation: Alharbi H, Aloufi K & Hussain A (2016) A new biologically-inspired analytical worm propagation model for mobile unstructured peer-to-peer networks In: Liu C-L, Hussain A, Luo B, Tan KC, Zeng Y, Zhang Z (ed.) Advances in Brain Inspired Cognitive Systems: 8th International Conference, BICS 2016, Beijing, China, November 28-30, 2016, Proceedings, Cham, Switzerland: Springer. BICS 2016: Advances in Brain Inspired Cognitive Systems 8th International Conference, 28.11.2016 - 30.11.2016, Beijing, China, pp. 251-263.
Issue Date: 2016
Series/Report no.: Lecture Notes in Computer Science, 10023
Conference Name: BICS 2016: Advances in Brain Inspired Cognitive Systems 8th International Conference
Conference Dates: 2016-11-28T00:00:00Z
Conference Location: Beijing, China
Abstract: Millions of users world-wide are sharing content using the Peer-to-Peer (P2P) client network. While new innovations bring benefits, there are nevertheless some dangers associated with them. One of the main threats is P2P worms that can penetrate the network even from a single node and can then spread very quickly. Many attempts have been made in this domain to model the worm propagation behaviour, and yet no single model exists that can realistically model the process. Most researchers have considered disease epidemic models for modelling the worm propagation process. Such models are, however, based on strong assumptions which may not necessarily be valid in real-world scenarios. In this paper, a new biologically-inspired analytical model is proposed, one that considers configuration diversity, infection time lag, user-behaviour and node mobility as the important parameters that affect the worm propagation process. The model is flexible and can represent a network where all nodes are mobile or a heterogeneous network, where some nodes are static and others are mobile. A complete derivation of each of the factors is provided in the analytical model, and the results are benchmarked against recently reported analytical models. A comparative analysis of simulation results indeed shows that our proposed biologically-inspired model represents a more realistic picture of the worm propagation process, compared to the existing state-of-the-art analytical models.
Status: Book Chapter: publisher version
Rights: The publisher does not allow this work to be made publicly available in this Repository. Please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study.
URL: https://link.springer.com/chapter/10.1007/978-3-319-49685-6_23

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