Modeling the Spatio-Temporal Dynamics of Worm Propagation in Smartphones Based on Cellular Automata

García, Gabriel González; Ramirez, Maria Elena Larraga

doi:10.1109/ems.2016.042

Cited by 3 publications

(3 citation statements)

References 13 publications

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“…Gonzalez and Larraga et al [46] suggested a CA-based two-dimensional model based on an epidemiological compartmentalized model to examine the transmission of Bluetooth worms. According to related research, there are seven different smartphone prevalence states: vulnerable, exposed, infected, diagnosed, carried, interrupted, and recovered.…”

Section: Bluetooth Wormmentioning

confidence: 99%

Defending IoT Devices against Bluetooth Worms with Bluetooth OBEX Proxy

Hsu,

Wu,

Hwang

et al. 2023

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The number of Internet of Things (IoT) devices has increased dramatically in recent years, and Bluetooth technology is critical for communication between IoT devices. It is possible to protect electronic communications, the Internet of Things (IoT), and big data from malware and data theft with BlueZ’s Bluetooth File Transfer Filter (BTF). It can use a configurable filter to block unauthorized Bluetooth file transfers. The BTF is available for various Linux distributions and can protect many Bluetooth-enabled devices, including smartphones, tablets, laptops, and the Internet of Things. However, the increased number and density of Bluetooth devices have also created a serious problem—the Bluetooth worm. It poses a severe threat to the security of Bluetooth devices. In this paper, we propose a Bluetooth OBEX Proxy (BOP) to filter malicious files transferred to devices via the OBEX system service in BlueZ. The method described in this article prevents illegal Bluetooth file transfers, defending big data, the Internet of Things (IoT), and electronic communications from malware and data theft. It also protects numerous Bluetooth devices, including smartphones, tablets, laptops, and the Internet of Things, with many Linux distributions. Overall, the detection findings were entirely accurate, with zero false positives and 2.29% misses.

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Section: Bluetooth Wormmentioning

confidence: 99%

Defending IoT Devices against Bluetooth Worms with Bluetooth OBEX Proxy

Hsu,

Wu,

Hwang

et al. 2023

Information

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“…In [22] authors proposed a discrete mathematical model to simulate Bluetooth-based worn spreading on smartphones by using CA. Specifically, the propagation of a worm is studied by means of a model where seven classes of epidemic states for a smartphone are considered: susceptible, exposed, carrier, infected, diagnosed, recovered, and interrupted.…”

Section: Introductionmentioning

confidence: 99%

“…The main goal of this work is to improve the work [22] to represent in a better way malware spreading on time and space. For this purpose, the state diagram that defines the dynamics of the model is changed to work more properly, in particular, when heterogeneous smartphones with different operating systems and the influence of the mobility on the spreading of the malware are considered.…”

Section: Introductionmentioning

confidence: 99%

Worm Propagation Modeling Considering Smartphones Heterogeneity and People Mobility

García¹,

Lárraga²,

Álvarez-Icaza

2017

Proceedings of the 2017 International Conference on Applied Mathematics, Modeling and Simulation (AMMS 2017)

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Abstract-In recent years, the worldwide market for smartphones has grown dramatically. The kind of information stored in these devices makes them an attractive target for malware writers. Consequently, modeling of worm propagation in smartphones in order to predict the side effects of a new threat and understand the complex behavior of the modeled malware has received significant attention. One of the possible mechanisms for malware spreading are Bluetooth antennas, where the malware infects devices in its proximity as biological viruses do. Due to this strong similarity in the behaviors of self-replicating and propagation between mobile malware and biological viruses, most investigations of malware propagation in smartphones focus predominately on modeling its propagation dynamics by employing the classical epidemic theories in epidemiology. Cellular Automata (CA) models have emerged recently as a promising alternative to characterize worm propagation and understand its behavior. However, in the most of the existing CA models for mobile malware, it is assumed that all smartphones are homogeneous and that transmission time of the worm is one time cycle. In this work, a mathematical model to study the spatio-temporal propagation dynamics of Bluetooth worms based on CA and the compartmental epidemiological models is introduced. The model considers the local interactions between the smartphones and is able to simulate the individual dynamic of each device and the effect of mobility of their users on the infection propagation.

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Bluetooth Worm Propagation in Smartphones: Modeling and Analyzing Spatio-Temporal Dynamics

et al. 2021

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The use of smartphones has become an inherent part of daily human life. It allows users to keep personal information, emails, pictures, social media accounts, and financial data in one place. Consequently, smartphones are an attractive target for malware developers to spread malicious content, aiming at extracting information without the user's knowledge. Therefore, understanding malware propagation characteristics could provide a means to evaluate how they behave in order to plan security solutions accordingly. Bluetooth antennas are a channel for spreading malware through smartphones, where the probability of infection, similar to biological viruses, depends mainly on the attacker's physical proximity. This work presents a model based on cellular automata and epidemiological compartmental models for studying the spatial and temporal propagation of Bluetooth worms in smartphones. The proposed model incorporates the individual characteristics of each device, such as security settings, latency time, operating system, different classes of Bluetooth antennas (range and transfer rate), and different user mobility patterns. Several simulation scenarios are analyzed in order to study the spreading dynamics of Bluetooth-based worms, considering the location where the outbreak begins, and the different types of antennas integrated into the smart devices. Simulation results indicated that the proposed model is appropriate for studying how the users' demographics affect the worm's propagation dynamics in time and space. Moreover, the model permits an analysis of the impact of users' awareness about the risks inherent in using smart devices in Bluetooth networks, based on the acceptance of incoming communication and the effects of recovery and immunity to threats. Finally, the proposed model preserves simplicity and computational efficiency, with the possibility of extending beyond Bluetooth in order to include other transmission media.

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Modeling the Spatio-Temporal Dynamics of Worm Propagation in Smartphones Based on Cellular Automata

Cited by 3 publications

References 13 publications

Defending IoT Devices against Bluetooth Worms with Bluetooth OBEX Proxy

Defending IoT Devices against Bluetooth Worms with Bluetooth OBEX Proxy

Worm Propagation Modeling Considering Smartphones Heterogeneity and People Mobility

Bluetooth Worm Propagation in Smartphones: Modeling and Analyzing Spatio-Temporal Dynamics

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