## 7 Conclusions  
It is essential to know the causes of network scan failure/delay in relation to the communication environment status in order to apply necessary countermeasures against cyberattacks. This paper proposed a method to improve the network scan failure/delay cause estimation using a multimodel-based approach. The causes of network scan failure/delay were identified and analyzed in relation to the communication environment status and communication results in wireless communication systems/networks.  
Specifically, a pre-evaluation process of cause of network scan failure estimation was adopted, and a feature that is useful for estimating the cause of scan failure/delay was identified from the scan response features, such as minimum RTT, maximum RTT, jitter, etc. In addition, an MM-LDA was proposed to build a one-to-many model for our proposed method. Using the MM-LDA, a discriminant score \(D_{s}\) was calculated for each terminal connected to a base station, access points, etc., and based on the number of terminals with negative \(D_{s}\), cause of scan failure/delay was judged as SNR insufficient or NW congestion. Furthermore, a filtering process was adopted to improve the performance of the proposed MM-LDA and to reduce bias estimation, which may occur when some features of the scan response data obtained in various scenarios are more sensitive to a particular cause of scan failure/delay than the others.  
The performance evaluation of our proposed method was conducted using the scan response results acquired through a computer simulation. The proposed MM-LDA with filtered training data achieved high performance when utilized to estimate the cause of scan failure/delay. The result shows that the proposed method achieved a high estimation result of the cause of scan failure/delay with an average of 98% correct estimation (i.e., normal--1.0, SNR insufficient--0.98, and NW congestion--0.97 correct estimation ratio) of the cause of scan failure/delay in various scenarios. The MM-LDA outperformed various conventional machine learning classification methods.  
In the future, we will consider extending the proposed method to other networks, such as Wi-Fi, Wi-SUN, LoRa, etc., by performing a network scan on the IoT wireless devices that are connected to the network and conducting evaluations that assume more realistic situations. In addition, the analysis of the computational and storage complexity of the proposed method compared to the conventional approaches will be examined. Moreover, we will conduct experiments to evaluate our proposed method and consider an estimation based on a deep learning method.  
**Author Contributions:** Conceptualization, B.O., N.E., T.K. and K.Y.; methodology, B.O.; software, B.O., K.S. and T.K.; validation, B.O., K.S. and K.Y.; formal analysis, B.O.; investigation, B.O. and K.Y.; resources, K.S., K.Y. and Y.S.; data curation, B.O.; writing--original draft preparation, B.O.; writing--review and editing, B.O., N.E., K.S., T.K., K.Y. and Y.S.; visualization, B.O.; supervision, K.Y. and Y.S.; project administration, K.Y. and Y.S.; funding acquisition, K.Y. and Y.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research is a result of the project entitled \"Research and Development of Technologies of Efficient Network Scanning for Wide-Area IoT Wireless Networks\", which is supported by the Ministry of Internal Affairs and Communications as part of the research program \"R&D for Expansion of Radio Wave Resources (JPJ000254)\".  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  
**Sample Availability:** Not applicable.