A Social Network Based Patching Scheme for Worm Containment in Cellular Networks

Abstract

Abstract-Recently, cellular phone networks have begun allowing third-party applications to run over certain open-API phone operating systems such as Windows Mobile, Iphone and Google's Android platform. However, with this increased openness, the fear of rogue programs written to propagate from one phone to another becomes ever more real. This paper proposes a countermechanism to contain the propagation of a mobile worm at the earliest stage by patching an optimal set of selected phones. The counter-mechanism continually extracts a social relationship graph between mobile phones via an analysis of the network traffic. As people are more likely to open and download content that they receive from friends, this social relationship graph is representative of the most likely propagation path of a mobile worm. The counter mechanism partitions the social relationship graph via two different algorithms, balanced and clustered partitioning and selects an optimal set of phones to be patched first as those which have the capability to infect the most number of other phones. The performance of these partitioning algorithms is compared against a benchmark random partitioning scheme. Through extensive trace-driven experiments using real IP packet traces from one of the largest cellular networks in the US, we demonstrate the efficacy of our proposed counter-mechanism in containing a mobile worm.

Containing smartphone worm propagation with an influence maximization algorithm

“…Zhu et al [34] proposed a systematic approach to contain MMS worm propagation based on social networks. First, the authors built an undirected weighted graph G to represent the mobiles' social relationships in the cellular network from a real trace.…”

Self-propagating mal-packets in wireless sensor networks: Dynamics and defense implications

“…In [25], Xie et al studied the feasibility of leveraging the existing P2P overlay structure for distributing automated security patches to vulnerable machines and examined two approaches. Zhu et al [26] proposed a graph-partitioning approach to contain the propagation of a mobile worm in the context of cellular networks. The proposed methodology was shown to effectively limit the spread of MMS and SMS based worms.…”

Toward Optimal Community Detection: From Trees to General Weighted Networks

“…For instance, 182 DINH AND THAI communities within the World Wide Web may correspond to sets of webpages on related topics; communities within mobile networks may correspond to sets of friends or colleagues; in computer networks communities may correspond to users in is peer-to-peer traffic or botnet farms [31]. Detecting this special substructure is also extremely useful in deriving social-based applications such as forwarding and routing strategies in communication networks [12,21,26], Sybil defense [28,30], worm containment on cellular networks [26,32], and sensor programming [27].…”