Determining Vulnerabilities of Pervasive IoT Devices and Their Geographic Distribution

Abstract

Projections have it that by 2023 there will be a global per capita uptick of 1.2 of networked devices which aims at hitting 29 billion mark (https://www.cisco.com/c/en/us/solutions/collateral/executive-perspectives/annual-internet-report/white-paper-c11-741,490.html). This is a massive expansion of the attack surface, a land flowing with milk and honey for the nefarious hackers. With review showing research focus not being favourable toward vulnerability assessment of these connected devices, especially the Internet of Things (IoT), there is a danger of pandemic of security breaches in the near future. One effective way to circumvent this looming crisis is to craft a viable framework to implement on a real-time basis the identification or detection of these devices when they join a network and measure their vulnerability with a view to either mitigate or expunge from the network altogether. Using the capabilities of Python programming, the experiment retrieves device IPs from through a network analysis tool and automatically parses into Web Application Interface (WAI) capability of Nessus vulnerability scanner to both assess and score or rank the existing vulnerabilities and severity respectively. Results showed that the device vulnerability scoring or ranking will facilitate prompt remediation decision to secure the rest of the network against any potential breach.