Smart City Gnosys
Smart city article details
| Title | Enhancing Internet Of Things Security: Evaluating Machine Learning Classifiers For Attack Prediction |
|---|---|
| ID_Doc | 23835 |
| Authors | Arabiat A.; Altayeb M. |
| Year | 2024 |
| Published | International Journal of Electrical and Computer Engineering, 14, 5 |
| DOI | http://dx.doi.org/10.11591/ijece.v14i5.pp6036-6046 |
| Abstract | The internet of things (IoT) has contributed to improving the quality of service and operational efficiency in many areas, such as smart cities, but this technology has faced a major dilemma: the problem of cyber-attacks of various types. In this study, we relied on the use of machine learning (ML) and deep learning (DL) techniques to present a proposed model of an intrusion detection system (IDS) for detecting different types of IoT attacks that include ARP_poisoning, DOS_SYN_Hping, MQTT_Publish, NMAP_FIN_SCAN, NMAP_OS_DETECTION, and Thing_Speak. However, the proposed model is built using Orange3 data mining tools. The model consists of random forest (RF), artificial neural network (ANN), logistic regression (LR), and support vector machine (SVM) classifiers. On the other hand, the data set that is used was obtained from the Kaggle platform's real-time IoT infrastructure data set, called RT-IoT2022. The data set consists of a huge number of records, which are processed and then reduced to 7,481 records using linear discriminant analysis. In the next stage, the data set is fed to the Orange3 data mining tool, which is divided into 70% of the training dataset and 30% of the test dataset, in addition to using fold-cross validation to increase accuracy and avoid overfitting. Thus, the experimental results showed the superiority of RF with a classification accuracy of (99.9%), while the accuracy in ANN reached (99.8%), (97.8%) in LR, and finally, for SVM, the accuracy reached (92.9%). © 2024 Institute of Advanced Engineering and Science. All rights reserved. |
| Author Keywords | Cyber attack; Internet of things; Intrusion detection system; Machine learning; Orange3 |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
24715  | 0.918 | Ali M.; Pervez S.; Hosseini S.E.; Siddhu M.K. | Evaluation And Detection Of Cyberattack In Iot-Based Smart City Networks Using Machine Learning On The Unsw-Nb15 Dataset | International Journal of Online and Biomedical Engineering, 21, 2 (2025) |
| 33346 | 0.916 | Berhili M.; Chaieb O.; Benabdellah M. | Intrusion Detection Systems In Iot Based On Machine Learning: A State Of The Art | Procedia Computer Science, 251 (2024) |
| 33508 | 0.915 | Saini K.S.; Chaudhary S. | Investigation On Attack Detection In Iot Networks: A Study And Analysis Of The Existing Machine Learning And Deep Learning Techniques | 3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025 (2025) |
| 32898 | 0.909 | Kaur B.; Dadkhah S.; Shoeleh F.; Neto E.C.P.; Xiong P.; Iqbal S.; Lamontagne P.; Ray S.; Ghorbani A.A. | Internet Of Things (Iot) Security Dataset Evolution: Challenges And Future Directions | Internet of Things (Netherlands), 22 (2023) |
| 37199 | 0.908 | Al-Ambusaidi M.; Yinjun Z.; Muhammad Y.; Yahya A. | Ml-Ids: An Efficient Ml-Enabled Intrusion Detection System For Securing Iot Networks And Applications | Soft Computing, 28, 2 (2024) |
| 33032 | 0.908 | Dawoud A.; Sianaki O.A.; Shahristani S.; Raun C. | Internet Of Things Intrusion Detection: A Deep Learning Approach | 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020 (2020) |
| 6991 | 0.907 | Aljohani R.; Bushnag A.; Alessa A. | Ai-Based Intrusion Detection For A Secure Internet Of Things (Iot) | Journal of Network and Systems Management, 32, 3 (2024) |
| 30732 | 0.907 | Amine M.S.; Nada F.A.; Hosny K.M. | Improved Model For Intrusion Detection In The Internet Of Things | Scientific Reports, 15, 1 (2025) |
| 9197 | 0.907 | Janani Pandeeswari G.; Jeyanthi S. | Analysis Of Intrusion Detection Using Machine Learning Techniques | 2nd IEEE International Conference on Advanced Technologies in Intelligent Control, Environment, Computing and Communication Engineering, ICATIECE 2022 (2022) |
| 36897 | 0.903 | Alamareen A.B.; Al-Mashagbeh M.H.; Abuasal S.; Hussein A.S. | Methodology To Improving Iot Network Security With Machine Learning Using The Iot Intrusion Dataset | Studies in Systems, Decision and Control, 572 (2025) |