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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, Hussain A, Luo B, Tan K, Zeng Y & Zhang Z (eds.) Advances in Brain Inspired Cognitive Systems: 8th International Conference, BICS 2016, Beijing, China, November 28-30, 2016, Proceedings. Lecture Notes in Computer Science, 10023. BICS 2016: Advances in Brain Inspired Cognitive Systems 8th International Conference, Beijing, China, 28.11.2016-30.11.2016. Cham, Switzerland: Springer, pp. 251-263. https://doi.org/10.1007/978-3-319-49685-6_23

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.