Proceedings of the 2007 ACM Workshop on Recurring Malcode 2007
DOI: 10.1145/1314389.1314402
|View full text |Cite
|
Sign up to set email alerts
|

Can you infect me now?

Abstract: In this paper we evaluate the effects of malware propagating using communication services in mobile phone networks. Although self-propagating malware is well understood in the Internet, mobile phone networks have very different characteristics in terms of topologies, services, provisioning and capacity, devices, and communication patterns. To investigate malware in this new environment, we have developed an event-driver simulator that captures the characteristics and constraints of mobile phone networks. In pa… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

0
9
0

Year Published

2009
2009
2020
2020

Publication Types

Select...
4
2
1

Relationship

0
7

Authors

Journals

citations
Cited by 70 publications
(9 citation statements)
references
References 21 publications
0
9
0
Order By: Relevance
“…For example, random waypoint mobility models are known to create a higher density of devices in the center of the region considered [15,16], thus creating dense networks under stable conditions. Fleizach et al [31] investigate the effect of malware propagation over the wireless backbone net-works. They characterize the speed and severity of the malware under realistic scenarios and explore network-based defenses against such malware.…”
Section: Studying Mobile Malware Spreadmentioning
confidence: 99%
See 1 more Smart Citation
“…For example, random waypoint mobility models are known to create a higher density of devices in the center of the region considered [15,16], thus creating dense networks under stable conditions. Fleizach et al [31] investigate the effect of malware propagation over the wireless backbone net-works. They characterize the speed and severity of the malware under realistic scenarios and explore network-based defenses against such malware.…”
Section: Studying Mobile Malware Spreadmentioning
confidence: 99%
“…Some of them [94] consider lower resolution information for mapping the devices (although this is good enough for SMS/MMS malware). Others [98,100,103,89,31] use small size networks, and detailed packet level simulations to study the proximity-based propagation. The study of the spread of malware through simulations can be accomplished with varying levels of accuracy, complexity, and scalability.…”
Section: Drawbacks In Existing Approachesmentioning
confidence: 99%
“…By selecting the behaviors from our set that best fit the these two values and the 95% confidence interval, as explained in Section 3.4, we would obtain the results shown in Figure 2.2. With our simulations, we are able to capture the real behavior of the App1 (red points in Figure 2.2) until the 28-th day after the app launch, since data d (14), d (21) and d (28) is inside the 95% confidence interval generated by the proposed method. Notice that, although the value corresponding to 34-th day, d (34), lies outside the confidence interval, it is not far from the 95% CI.…”
Section: Resultsmentioning
confidence: 94%
“…In Figure 2.7, the predicted behavior and 95% confidence interval of the number of accumulated downloads vs. time, based on data from d(5) and d(7) corresponding to the 5-th and 7-th days respectively, are shown. With data from d(5) and d (7) we can predict correctly the behavior of the accumulated downloads until d (14). The model is able to capture the real behavior for 11-th and 14-th days, whose total number of accumulated downloads are given by d(11) and d (14), respectively.…”
Section: App3mentioning
confidence: 99%
See 1 more Smart Citation