Understanding the spreading patterns of mobile phone viruses
Lacking a standardizedperformance
Computer systems are rapidly changing. Over the next few years, we will see wide-scale deployment of dynamically-scheduled processors that can issue multiple instructions every clock cycle, execute instructions out of order, and overlap computation and cache misses , traditional cellphones have been relatively immune to viruses. Smartphones, however, can share programs and data with each other, representing a fertile ground for virus writers . Indeed, since 2004 more than 420 smartphone viruses have been identified , the newer ones having reached a state of sophistication that took computer viruses about two decades to arrive at . While smartphones currently represent less than 5% of the mobile market, given their reported high annual growth rate they are poised to become the dominant communication device in the near future, raising the possibility of virus breakouts that could overshadow the disruption caused by traditional computer viruses . The spread of mobile viruses is aided by two dominant communication protocols. First, a Bluetooth virus can infect all Bluetooth-activated phones within a distance from 10 to 30m, resulting in a spatially localized spreading pattern similar to the one observed in the case of influenza SARS and other contact-based diseases Second, an MMS virus can send a copy of itself to all mobile phones whose numbers are found in the infected phone’s address book, a long range spreading pattern previously exploited only by computer viruses . Thus to quantitatively study the spreading dynamics of mobile viruses we need to simultaneously model the location (13), the mobility and the communication patterns of mobile phone users. To achieve this we use as input anonymized billing record of a mobile phone provider, providing the calling patterns and the coordinates of the closest mobile phone tower each time mobile subscribers use their phone (but not the coordinate of individual users).
The methods we used to track the spreading of a potential Bluetooth and MMS virus are described in the Supporting Online Material (SOM). Briefly, once a phone becomes infected with an MMS virus, after a τ=2min time it sends a copy of itself to each mobile phone number found in the handset’s phone book, approximated with the list of numbers the handset’s user communicated with during a month long observational period. A Bluetooth virus can infect only mobile phones within a distance r=10m. To track this process, we assign to each user an hourly location that is consistent with its travel patterns and follow the infection dynamics within each mobile tower area using the SI model . That is, we consider that an infected user (I) infects a susceptible user (S), so that the Once an infected user moves in the vicinity of a new tower, it will serve as a source of a Bluetooth infection in its new location.