Name
Title | ||
|---|---|---|
Attack and anomaly detection in IoT sensors in IoT sites using machine learning approaches |
Abstract | ||
|---|---|---|
Attack and anomaly detection in the Internet of Things (IoT) infrastructure is a rising concern in the domain of IoT. With the increased use of IoT infrastructure in every domain, threats and attacks in these infrastructures are also growing commensurately. Denial of Service, Data Type Probing, Malicious Control, Malicious Operation, Scan, Spying and Wrong Setup are such attacks and anomalies which can cause an IoT system failure. In this paper, performances of several machine learning models have been compared to predict attacks and anomalies on the IoT systems accurately. The machine learning (ML) algorithms that have been used here are Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Artificial Neural Network (ANN). The evaluation metrics used in the comparison of performance are accuracy, precision, recall, f1 score, and area under the Receiver Operating Characteristic Curve. The system obtained 99.4% test accuracy for Decision Tree, Random Forest, and ANN. Though these techniques have the same accuracy, other metrics prove that Random Forest performs comparatively better. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.iot.2019.100059 | Internet of Things |
Keywords | Field | DocType |
Internet of Things (IoT),Machine Learning,Cybersecurity,Anomaly detection | Anomaly detection,F1 score,Decision tree,Denial-of-service attack,Computer science,Support vector machine,Data type,Artificial intelligence,Artificial neural network,Random forest,Machine learning | Journal |
Volume | ISSN | Citations |
7 | 2542-6605 | 10 |
PageRank | References | Authors |
0.77 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mahmudul Hasan | 1 | 158 | 10.17 |
| Md. Milon Islam | 2 | 10 | 0.77 |
| Ishrak Islam Zarif | 3 | 10 | 0.77 |
| M.M.A. Hashem | 4 | 10 | 1.11 |