A Multimodel-Based Approach for Estimating Cause of Scanning Failure and Delay in IoT Wireless Network
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, espec...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Network |
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| Online Access: | https://www.mdpi.com/2673-8732/2/4/31 |
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| author | Babatunde Ojetunde Naoto Egashira Kenta Suzuki Takuya Kurihara Kazuto Yano Yoshinori Suzuki |
| author_facet | Babatunde Ojetunde Naoto Egashira Kenta Suzuki Takuya Kurihara Kazuto Yano Yoshinori Suzuki |
| author_sort | Babatunde Ojetunde |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T16:01:45Z |
| format | Article |
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| institution | Directory Open Access Journal |
| issn | 2673-8732 |
| language | English |
| last_indexed | 2024-03-09T16:01:45Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Network |
| spelling | doaj.art-e0aa406b526d4001a84c964e73e6ca9b2023-11-24T17:05:43ZengMDPI AGNetwork2673-87322022-10-012451954410.3390/network2040031A Multimodel-Based Approach for Estimating Cause of Scanning Failure and Delay in IoT Wireless NetworkBabatunde Ojetunde0Naoto Egashira1Kenta Suzuki2Takuya Kurihara3Kazuto Yano4Yoshinori Suzuki5Wave Engineering Laboratories, Advanced Telecommunications Research Institute International, 2-2-2 Hikaridai, Seika, Soraku, Kyoto 619-0228, JapanWave Engineering Laboratories, Advanced Telecommunications Research Institute International, 2-2-2 Hikaridai, Seika, Soraku, Kyoto 619-0228, JapanWave Engineering Laboratories, Advanced Telecommunications Research Institute International, 2-2-2 Hikaridai, Seika, Soraku, Kyoto 619-0228, JapanWave Engineering Laboratories, Advanced Telecommunications Research Institute International, 2-2-2 Hikaridai, Seika, Soraku, Kyoto 619-0228, JapanWave Engineering Laboratories, Advanced Telecommunications Research Institute International, 2-2-2 Hikaridai, Seika, Soraku, Kyoto 619-0228, JapanWave Engineering Laboratories, Advanced Telecommunications Research Institute International, 2-2-2 Hikaridai, Seika, Soraku, Kyoto 619-0228, JapanThe 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.https://www.mdpi.com/2673-8732/2/4/31cyberattacksK-nearest neighborlinear discriminant analysismachine learningnetwork scanIoT |
| spellingShingle | Babatunde Ojetunde Naoto Egashira Kenta Suzuki Takuya Kurihara Kazuto Yano Yoshinori Suzuki A Multimodel-Based Approach for Estimating Cause of Scanning Failure and Delay in IoT Wireless Network Network cyberattacks K-nearest neighbor linear discriminant analysis machine learning network scan IoT |
| title | A Multimodel-Based Approach for Estimating Cause of Scanning Failure and Delay in IoT Wireless Network |
| title_full | A Multimodel-Based Approach for Estimating Cause of Scanning Failure and Delay in IoT Wireless Network |
| title_fullStr | A Multimodel-Based Approach for Estimating Cause of Scanning Failure and Delay in IoT Wireless Network |
| title_full_unstemmed | A Multimodel-Based Approach for Estimating Cause of Scanning Failure and Delay in IoT Wireless Network |
| title_short | A Multimodel-Based Approach for Estimating Cause of Scanning Failure and Delay in IoT Wireless Network |
| title_sort | multimodel based approach for estimating cause of scanning failure and delay in iot wireless network |
| topic | cyberattacks K-nearest neighbor linear discriminant analysis machine learning network scan IoT |
| url | https://www.mdpi.com/2673-8732/2/4/31 |
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