The proposed project, supported by the Israel National Cyber Bureau in the Prime Minister's Office, seeks to develop an understanding regarding the ways in which computer users expose themselves to cybercrime victimization over public and private computer networks. Moreover, this project seeks to identify the unique configuration of computer environments that exacerbate victims' vulnerabilities and support WIFI trespassers' illegal operations with these networks. Importantly, since both public and private computer networks are situated within a range of residential environments, we suspect that street segments' demographic and social characteristics could play a significant role in influencing private WIFIs' likelihood to be compromised by WIFI trespassers. Moreover, we believe that street segments demographic characteristics may shape public WIFI users online behaviors while employing these networks. Our interdisciplinary research team will seek to address the following research questions: first, how prevalent are vulnerable private WIFI networks across different street segments are, and could illegitimate users of these networks be deterred from infiltrating them? And second, do surveillance cues and situational attributes that are common in street segments influence the online behaviors of public WIFI users? Answering these research questions will allow us to develop a set of recommendations regarding effective security practices that could be employed by both private and public WIFI users to enhance their cyber security posture and reduce susceptibility to cybercrime victimization.
To assess the volume of vulnerable private WIFI networks, we will to record the location of private wireless networks, and assess their vulnerabilities to being compromised. In order to do so our research team will drive a vehicle around street segments in Tel-Aviv and listen to WIFI networks signals using a portable computer and a GPS device. Then, we will investigate whether private WIFI trespassers could be deterred from login and using a private WIFI network, we will run a randomized trial. Specifically, we plan to send our research team members to selected street segments in Tel-Aviv. The street segments will be selected from the list we will survey in the first phase of data collection, and would vary based on their residents' socioeconomic status. Team members will be equipped with a private portable wireless router that simulates private WIFI network. The team members will sit in the selected location for a period of 7 hours (between 7am-7pm) and will record illegal login attempts to our network. Our routers will not require login credentials (i.e. password and user names) and will be named "DAVID_PRIVATE". However, when attempting to login to some of the routers (treatment group) the following message will appear on the trespassers' computer screen: "This network is a private network. Please disconnect from it immediately. Traffic on this network is closely monitored." Other routers (control group) will be set to present no message on the WIFI trespassers' computer screen. Using this approach we will be able to assess whether a warning message in the attacked private WIFI network is indeed effective in deterring WIFI trespassers login attempt to the network. Deployment of our private WIFI networks in different residential communities will reveal whether street segments characteristics condition private WIFI trespassers' compliance with the warning message.
In both experiments, the research team will collect relevant information on the physical environments within which the WIFI networks are situated using observations. Specifically, we will record the number of individuals that are present in each research site, the number of male and female in the location and the frequency of their movements in the premise. We will also collect data regarding the physical layout of the research sites and the ratio of employees to clients. The data collected from all experiments will be merged with census data for two purposes: first, to identify a sample of places where crime is high and low, and where other characteristics vary, like collective efficacy. Second, we will use the data as a variable in predicting the level of cyber security.