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

Abstract

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.

ML-Based Classification of Device Environment Using Wi-Fi and Cellular Signal Measurements

“…Classification accuracy could be further improved by creating different models for different device types and operators. • Signal diversity: besides Wi-Fi and LTE signals, we are planning to add other types of signal measurements that are available on current phones, such as Bluetooth [27], [28], and UWB [29]. These were not considered in this paper due to limited deployments, but in future these may become more sidespread.…”

Defending IoT Devices against Bluetooth Worms with Bluetooth OBEX Proxy

“…Today, smartphones, laptops, and portable game consoles have built-in Bluetooth connectivity. Many studies have shown the spread of Bluetooth worms [1][2][3][4]. With the increasing number of Bluetooth-enabled devices around us, Bluetooth worms have become a severe problem.…”