Quantifying the Effectiveness of Mobile Phone Virus Response Mechanisms

Ruitenbeek, E. van; Courtney, T.; Sanders, William H.; Stevens, Fabrice

doi:10.1109/dsn.2007.78

Cited by 25 publications

(20 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Mabir, a variant of Cabir, was spread also via MMS by sending out copies of itself as a .sis file. Van Ruitenbeek et al [8] investigated the effects of MMS viruses that spread by sending infected messages to other devices. In addition, Bose and Shin [9] examined the propagation of malware that spread via SMS or MMS messages and short-range radio interfaces while Polla et al [10] have made a thorough survey on mobile malware.…”

mentioning

confidence: 99%

Secure Message Delivery Games for Device-to-Device Communications

Panaousis

Alpcan

Fereidooni

et al. 2014

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. Device-to-Device (D2D) communication is expected to be a key feature supported by next generation cellular networks. D2D can extend the cellular coverage allowing users to communicate when telecommunications infrastructure are highly congested or absent. In D2D networks, any message delivery from a source to a destination relies exclusively on intermediate devices. Each device can run different kinds of mobile security software, which offer protection against viruses and other harmful programs by using real-time scanning in every file entering the device. In this paper, we investigate the best D2D network path to deliver a potentially malicious message from a source to a destination. Although our primary objective is to increase security, we also investigate the contribution of energy costs and quality-of-service to the path selection. To this end, we propose the Secure Message Delivery (SMD) protocol, whose main functionality is determined by the solution of the Secure Message Delivery Game (SMDG). This game is played between the defender (i.e., the D2D network) which abstracts all legitimate network devices and the attacker which abstracts any adversary that can inject different malicious messages into the D2D network in order, for instance, to infect a device with malware. Simulation results demonstrate the degree of improvement that SMD introduces as opposed to a shortest path routing protocol. This improvement has been measured in terms of the defender's expected cost as defined in SMDGs. This cost includes security expected damages, energy consumption incurred due to messages inspection, and the quality-of-service of the D2D message communications.

show abstract

mentioning

confidence: 99%

Secure Message Delivery Games for Device-to-Device Communications

Panaousis

Alpcan

Fereidooni

et al. 2014

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…Ruitenbeek et al [14] proposed response mechanisms to analyze the effects of multimedia messaging system (MMS) viruses that spread by sending infected messages to other phones. Fleizach et al [15] developed an eventbased simulator to evaluate the effects of malware propagation using communication services like VOIP and MMS in mobile phone networks.…”

Section: Worm Propagation Modelingmentioning

confidence: 99%

Containing smartphone worm propagation with an influence maximization algorithm

Peng¹,

Wu²,

Wang³

et al. 2014

Computer Networks

View full text Add to dashboard Cite

“…Work in [18] explored rate-limits and SMS quarantine approaches after identifying infected devices. Similarly, the propagation of malware through MMS messages is studied in [84]. The authors model different MMS malware characteristics and study the effectiveness of different response mechanisms, such as virus scan and detection algorithms on all MMS messages, immunization patches, user education, monitoring anomalous behavior, and blacklisting of infected devices.…”

Section: Defenses Against Mobile Malwarementioning

confidence: 99%

High Performance Scalable and Expressive Modeling Environment to Study Mobile Malware in Large Dynamic Networks

Channakeshava

Bisset

Kumar

et al. 2011

2011 IEEE International Parallel &Amp; Distributed Processing Symposium

View full text Add to dashboard Cite

Advances in computing and communication technologies are blurring the distinction between today's PCs and mobile phones. With expected smart phones sales to skyrocket, lack of awareness regarding securing them, and access to personal and proprietary information, has resulted in the recent surge of mobile malware. In addition to using traditional social-engineering techniques such as email and file-sharing, malware unique to Bluetooth, Short Messaging Service (SMS) and Multimedia Messaging Service (MMS) messages are being used. Large scale simulations of malware on wireless networks have becomes important and studying them under realistic device deployments is important to obtain deep insights into their dynamics and devise ways to control them.In this dissertation, we present EpiNet: an individual-based scalable high-performance oriented modeling environment for simulating the spread of mobile malware over large, dynamic networks.EpiNet can be used to undertake comprehensive studies during both planning and response phase of a malware epidemic in present and future generation wireless networks. Scalability is an important design consideration and the current EpiNet implementation can scale to 3-5 million device networks and case studies show that large factorial designs on million device networks can be executed within a day on 100 node clusters. Beyond compute time, EpiNet has been designed for analysts to easily represent a range of interventions and evaluating their efficacy.The results indicate that Bluetooth malware with very low initial infection size will not result in a major wireless epidemic. The dynamics are dependent on the network structure and, activitybased mobility models or their variations can yield realistic spread dynamics. Early detection of the malware is extremely important in controlling the spread. Non-adaptive response strategies using static graph measures such as degree and betweenness are not effective. Device-based detection mechanisms provide a much better means to control the spread and only effective when detection occurs early on. Automatic signature generation can help in detecting newer strains of the malware and signature distributions through a central server results in better control of the spread. Centralized dissemination of patches are required to reach a large proportion of devices to be effective in slowing the spread. Non-adaptive dynamic graph measures such as vulnerability are found to be more effective.Our studies of SMS and hybrid malware show that SMS-only malware spread slightly faster than Bluetooth-only malware and do not spread to all devices. Hybrid malware spread orders of magnitude faster than either SMS-only or Bluetooth-only malware and can cause significant damage.Bluetooth-only malware spread faster than SMS-only malware in cases where density of devices in the proximity of an infected device is higher. Hybrid malware can be much more damaging than Bluetooth-only or SMS-only malware and we need mechanisms that can prevent such an outbreak.EpiNe...

show abstract

Quantifying the Effectiveness of Mobile Phone Virus Response Mechanisms

Cited by 25 publications

References 13 publications

Secure Message Delivery Games for Device-to-Device Communications

Secure Message Delivery Games for Device-to-Device Communications

Containing smartphone worm propagation with an influence maximization algorithm

High Performance Scalable and Expressive Modeling Environment to Study Mobile Malware in Large Dynamic Networks

Contact Info

Product

Resources

About