12-08-2012, 09:42 PM
I NEED SOME PROGRAM CODING RELATED TO THIS TOPIC...
12-08-2012, 09:42 PM
I NEED SOME PROGRAM CODING RELATED TO THIS TOPIC...
18-09-2012, 11:19 AM
Modeling and Restraining Mobile Virus Propagation
1Modeling and Restraining.pdf (Size: 1.8 MB / Downloads: 74) Abstract Viruses and malwares can spread from computer networks into mobile networks with the rapid growth of smart cellphone users. In a mobile network, viruses and malwares can cause privacy data leakage, extra charges, and remote listening. Furthermore, they can jam wireless servers by sending thousands of spam messages or track user positions through GPS. Because of the potential damages of mobile viruses, it is important for us to gain a deep understanding of the propagation mechanisms of mobile viruses. In this paper, we propose a two-layer network model for simulating virus propagation through both Bluetooth and SMS. Different from previous work, our work addresses the impacts of human behaviors, i.e., operational behavior and mobile behavior, on virus propagation. Our simulation results provide further insights into the determining factors of virus propagation in mobile networks. Moreover, we examine two strategies for restraining mobile virus propagation, i.e., pre-immunization and adaptive dissemination strategies drawing on the methodology of autonomy-oriented computing (AOC). The experimental results show that our strategies can effectively protect largescale and/or highly dynamic mobile networks. INTRODUCTION MANY studies have reported the damages of mobile viruses in smart phones [1], [2], [3]. For example, an outbreak of mobile viruses occurred in China in 2010. The ‘Zombie’ virus attacked more than 1 million cell phones, and created a loss of $300,000 per day1. Among many potential damages, mobile viruses can cause private data leakage and disturb conversation by remote control [3], [4]. In some more serious situations, viruses can even jam wireless services by sending thousands of spam messages, and reduce the quality of voice communication. In view of this situation, there is an urgent need for both users and service providers to further understand the propagation mechanisms of mobile viruses and to deploy efficient countermeasures. In order to help researchers observe and predict potential damages of a virus, some models have been used to study the dynamic process of virus propagation. Valid propagation models can be used as test-beds to: (1) estimate the scale of a virus outbreak before it occurs in reality [5] and (2) evaluate new and/or improved countermeasures for restraining virus propagation [6]. Recently, there exist some models to characterize and predict the infection dynamics of mobile viruses [5], [7], [8], [9], [10]. Virus Propagation through BT and SMS According to the communication channels of mobile viruses, mobile viruses fall into two categories: BT-based viruses (e.g., Cabir, Lasco) and SMS-based viruses (e.g., TXSBBSpy, Zombie, Commwarrior). A BT-based virus is a local-contact driven virus [7] since it infects other phones only through Bluetooth and WiFi devices within a short radio range. Similar to other contact-based diseases in humans (e.g., SARS and H1N1) [26], the propagation of a BT-based virus follows a spatially localized spreading pattern. One of the most common approaches to studying such virus propagation is based on epidemic modeling. It assumes that individuals are homogeneous in a host population, each of which has an equal likelihood of contact with others [27], [28]. Some studies have applied epidemic modeling to analyzing the propagation dynamics of a BT-based virus. For example, studies reported in [5], [7] and [16] have characterized the propagation process of a BT-based virus based on the typical SI [27] and SIR [28] models, respectively. Because of the limited transmission range of a Bluetooth device, human mobility plays an important role in BT-based virus propagation [17]. MODELING MOBILE VIRUS PROPAGATION In this section, we first introduce a two-layer network model for simulating mobile virus spreading through different communication channels in Section 3.1. Next, we present detailed propagation processes of SMS-based and BT-based viruses in Sections 3.2 and 3.3, respectively. The work presented in this section is an extension of the work in [35]. Different from others, our work addresses the issue of how human behaviors, i.e., operational and mobile behavior, affect virus propagation. Based on the analysis of propagation mechanisms, we note that a primary factor contributing to SMS-based virus propagation lies in users’ operations after receiving infected messages. If users have enough knowledge about the risk of mobile viruses (i.e., with a high security awareness), they will not open suspicious messages and their phones will not be easily infected. In addition to operational patterns, mobility patterns play a key role in BT-based virus propagation. This is because BT-based viruses can only infect local neighbors (whether or not they know these neighbors) within a certain range. SMS-Based Virus Propagation Figure 5 shows the effects of message delivery latency and failure rate (as mentioned in Section 3.2) on SMSbased virus propagation. The parameter of “INFOCOM” in Fig. 5(a) means that 5% of delivered messages have a latency longer than 1 hour [40]. While, other delay times in Fig. 5(a) are constant. The results show that a long delivery latency can affect the propagation speed (i.e., postpone the outbreak of virus propagation, just as the throttling technique [41] in computer networks), but cannot restrain the propagation scope (i.e., reduce the final number of infected phones). However, appropriately increasing the delivery failure rate, as shown in Fig. 5(b), can restrain virus propagation in terms of both propagation speed and scope. |
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