Logistic Regression Ensemble Classifier for Intrusion Detection System in Internet of Things
The Internet of Things (IoT) is a powerful technology that connect its users worldwide with everyday objects without any human interference. On the contrary, the utilization of IoT infrastructure in different fields such as smart homes, healthcare and transportation also raises potential risks of at...
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MDPI AG
2023-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/23/9583 |
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| author | Silpa Chalichalamala Niranjana Govindan Ramani Kasarapu |
| author_facet | Silpa Chalichalamala Niranjana Govindan Ramani Kasarapu |
| author_sort | Silpa Chalichalamala |
| collection | DOAJ |
| description | The Internet of Things (IoT) is a powerful technology that connect its users worldwide with everyday objects without any human interference. On the contrary, the utilization of IoT infrastructure in different fields such as smart homes, healthcare and transportation also raises potential risks of attacks and anomalies caused through node security breaches. Therefore, an Intrusion Detection System (IDS) must be developed to largely scale up the security of IoT technologies. This paper proposes a Logistic Regression based Ensemble Classifier (LREC) for effective IDS implementation. The LREC combines AdaBoost and Random Forest (RF) to develop an effective classifier using the iterative ensemble approach. The issue of data imbalance is avoided by using the adaptive synthetic sampling (ADASYN) approach. Further, inappropriate features are eliminated using recursive feature elimination (RFE). There are two different datasets, namely BoT-IoT and TON-IoT, for analyzing the proposed RFE-LREC method. The RFE-LREC is analyzed on the basis of accuracy, recall, precision, F1-score, false alarm rate (FAR), receiver operating characteristic (ROC) curve, true negative rate (TNR) and Matthews correlation coefficient (MCC). The existing researches, namely NetFlow-based feature set, TL-IDS and LSTM, are used to compare with the RFE-LREC. The classification accuracy of RFE-LREC for the BoT-IoT dataset is 99.99%, which is higher when compared to those of TL-IDS and LSTM. |
| first_indexed | 2024-03-09T01:42:02Z |
| format | Article |
| id | doaj.art-662d5228ccfe497798c2a73a0e20e2bb |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T01:42:02Z |
| publishDate | 2023-12-01 |
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| series | Sensors |
| spelling | doaj.art-662d5228ccfe497798c2a73a0e20e2bb2023-12-08T15:26:30ZengMDPI AGSensors1424-82202023-12-012323958310.3390/s23239583Logistic Regression Ensemble Classifier for Intrusion Detection System in Internet of ThingsSilpa Chalichalamala0Niranjana Govindan1Ramani Kasarapu2Department of Computer Science and Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai 603203, IndiaDepartment of Computing Technologies, School of Computing, SRM Institute of Science and Technology, Kattankulathur, Chennai 603203, IndiaSchool of Computing, Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College), Tirupati 517102, IndiaThe Internet of Things (IoT) is a powerful technology that connect its users worldwide with everyday objects without any human interference. On the contrary, the utilization of IoT infrastructure in different fields such as smart homes, healthcare and transportation also raises potential risks of attacks and anomalies caused through node security breaches. Therefore, an Intrusion Detection System (IDS) must be developed to largely scale up the security of IoT technologies. This paper proposes a Logistic Regression based Ensemble Classifier (LREC) for effective IDS implementation. The LREC combines AdaBoost and Random Forest (RF) to develop an effective classifier using the iterative ensemble approach. The issue of data imbalance is avoided by using the adaptive synthetic sampling (ADASYN) approach. Further, inappropriate features are eliminated using recursive feature elimination (RFE). There are two different datasets, namely BoT-IoT and TON-IoT, for analyzing the proposed RFE-LREC method. The RFE-LREC is analyzed on the basis of accuracy, recall, precision, F1-score, false alarm rate (FAR), receiver operating characteristic (ROC) curve, true negative rate (TNR) and Matthews correlation coefficient (MCC). The existing researches, namely NetFlow-based feature set, TL-IDS and LSTM, are used to compare with the RFE-LREC. The classification accuracy of RFE-LREC for the BoT-IoT dataset is 99.99%, which is higher when compared to those of TL-IDS and LSTM.https://www.mdpi.com/1424-8220/23/23/9583adaptive synthetic samplingInternet of Thingsintrusion detection systemlogistic regression-based ensemble classifierrecursive feature elimination |
| spellingShingle | Silpa Chalichalamala Niranjana Govindan Ramani Kasarapu Logistic Regression Ensemble Classifier for Intrusion Detection System in Internet of Things Sensors adaptive synthetic sampling Internet of Things intrusion detection system logistic regression-based ensemble classifier recursive feature elimination |
| title | Logistic Regression Ensemble Classifier for Intrusion Detection System in Internet of Things |
| title_full | Logistic Regression Ensemble Classifier for Intrusion Detection System in Internet of Things |
| title_fullStr | Logistic Regression Ensemble Classifier for Intrusion Detection System in Internet of Things |
| title_full_unstemmed | Logistic Regression Ensemble Classifier for Intrusion Detection System in Internet of Things |
| title_short | Logistic Regression Ensemble Classifier for Intrusion Detection System in Internet of Things |
| title_sort | logistic regression ensemble classifier for intrusion detection system in internet of things |
| topic | adaptive synthetic sampling Internet of Things intrusion detection system logistic regression-based ensemble classifier recursive feature elimination |
| url | https://www.mdpi.com/1424-8220/23/23/9583 |
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