A novel IoT-aware WLAN environment identification for efficient internet-wide port scan

With the emergence of Internet of Things (IoT), network security has become an area of acute concern owing to susceptibilities of IoT security that can be exploited to attack other devices and network infrastructures. Internet-Wide Port Scan (IWPS), a well-established network sifting mechanism that identifies threats and defensive mechanisms, is gaining attention to probe IoT networks and identify vulnerable IoT devices. A key enabler for IoT networks is the Wireless Local Area Network (WLAN) that comprises of numerous heterogeneous devices such as smartphones, computers, IoT devices, and so on. Hence, efficient probing of such networks can be challenging; since networks can easily experience congestion, poor signal-to-noise-ratio (SINR), etc. that can result in loss of probe packets and subsequently low discovery rate. In this paper these issues have been addressed and a holistic classification algorithm has been proposed to identify the states of heterogeneous WLAN environment based on real-time and historical measurements. Such a classification can assist in choosing scan strategies for improved IWPS performance. The experimental results presented in this paper reveal that the proposed algorithm is very effective to classify such a complex and heterogeneous environment.