###### Abstract  
The rapid growth in the IoT network comes with a huge security threat. Network scanning is considered necessary to identify vulnerable IoT devices connected to IP networks. However, most existing network scanning tools or system do not consider the burden of scan packet traffic on the network, especially in the IoT network where resources are limited. It is necessary to know the status of the communication environment and the reason why network scanning failed. Therefore, this paper proposes a multimodel-based approach which can be utilized to estimate the cause of failure/delay of network scanning over wireless networks where a scan packet or its response may sometimes be dropped or delayed. Specifically, the factors that cause network scanning failure/delay were identified and categorized. Then, using a machine learning algorithm, we introduced a multimodel linear discriminant analysis (MM-LDA) to estimate the cause of scan failure/delay based on the results of network scanning. In addition, a one-to-many model and a training data filtering technique were adopted to ensure that the estimation error was drastically reduced. The goal of our proposed method was to correctly estimate the causes of scan failure/delay in IP-connected devices. The performance of the proposed method was evaluated using computer simulation assuming a cellular (LTE) network as the targeted IoT wireless network and using LTE-connected devices as the targeted IoT devices. The proposed MM-LDA correctly estimates the cause of failure/delay of the network scan at an average probability of 98% in various scenarios. In comparison to other conventional machine learning classifiers, the proposed MM-LDA outperforms various classification methods in the estimation of the cause of scan failure/delay. c 2022 2  
was confirmed via Shodan that 120,000 IP cameras were detected as infected [3]. Therefore, \"wide-area-network scanning\" techniques are necessary to detect compromised IoT wireless devices and provide adequate countermeasures against such threats in an IoT network. However, when network scans are performed, the status of the scanned device port is reported as \"open\", \"closed\", or \"filtered/blocked\". The problem is that when a network scanning fails to acquire a response from a particular port of a device, the status may be considered as \"filtered/blocked\" and the reason for such failure (i.e., a failure that occurred because of bad network condition) is not considered. It is possible that a device whose ports are considered as safe, due to the \"filtered/blocked\" status reported during a bad network condition, may be vulnerable to a cyberattack if the ports are actually open. One possible solution to confirm the real status of the scanned port is to perform a rescan. However, if the port is actually closed, the traffic during the rescanning can interfere with the regular communication of the network, especially in a resource-constrained IoT wireless network. Therefore, in order to perform a network scan reliably and efficiently, it is necessary to know the condition of the network, and whether it is good or not for performing a network scan. Moreover, it is essential to determine the cause of network scan failure/delay [4] in relation to the communication environment status for appropriately scheduling a network scan. Hence, a technique for determining and estimating the cause of network scan failure/delay is required. In this paper, we propose a multimodel-based approach which can be utilized to estimate the cause of failure/delay of the network scan over wireless networks where a scan packet or its response may sometimes be dropped or delayed. Specifically, various factors that cause network scanning failure/delay are identified and categorized based on the network status. Furthermore, we utilize machine learning algorithms to estimate the cause of failure/delay based on the results of a series of network scanning obtained under various network scenarios (i.e., normal, low signal-to-noise-ratio, and network congestion).