Security Analysis of DDoS Attacks Using Machine Learning Algorithms in Networks Traffic
The recent advance in information technology has created a new era named the Internet of Things (IoT). This new technology allows objects (things) to be connected to the Internet, such as smart TVs, printers, cameras, smartphones, smartwatches, etc. This trend provides new services and applications...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-11-01
|
| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/10/23/2919 |
| _version_ | 1797507942237339648 |
|---|---|
| author | Rami J. Alzahrani Ahmed Alzahrani |
| author_facet | Rami J. Alzahrani Ahmed Alzahrani |
| author_sort | Rami J. Alzahrani |
| collection | DOAJ |
| description | The recent advance in information technology has created a new era named the Internet of Things (IoT). This new technology allows objects (things) to be connected to the Internet, such as smart TVs, printers, cameras, smartphones, smartwatches, etc. This trend provides new services and applications for many users and enhances their lifestyle. The rapid growth of the IoT makes the incorporation and connection of several devices a predominant procedure. Although there are many advantages of IoT devices, there are different challenges that come as network anomalies. In this research, the current studies in the use of deep learning (DL) in DDoS intrusion detection have been presented. This research aims to implement different Machine Learning (ML) algorithms in WEKA tools to analyze the detection performance for DDoS attacks using the most recent CICDDoS2019 datasets. CICDDoS2019 was found to be the model with best results. This research has used six different types of ML algorithms which are K_Nearest_Neighbors (K-NN), super vector machine (SVM), naïve bayes (NB), decision tree (DT), random forest (RF) and logistic regression (LR). The best accuracy result in the presented evaluation was achieved when utilizing the Decision Tree (DT) and Random Forest (RF) algorithms, 99% and 99%, respectively. However, the DT is better than RF because it has a shorter computation time, 4.53 s and 84.2 s, respectively. Finally, open issues for further research in future work are presented. |
| first_indexed | 2024-03-10T04:55:35Z |
| format | Article |
| id | doaj.art-b983c4ff0b804da09ea83fe312719d17 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T04:55:35Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-b983c4ff0b804da09ea83fe312719d172023-11-23T02:16:06ZengMDPI AGElectronics2079-92922021-11-011023291910.3390/electronics10232919Security Analysis of DDoS Attacks Using Machine Learning Algorithms in Networks TrafficRami J. Alzahrani0Ahmed Alzahrani1Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi ArabiaDepartment of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi ArabiaThe recent advance in information technology has created a new era named the Internet of Things (IoT). This new technology allows objects (things) to be connected to the Internet, such as smart TVs, printers, cameras, smartphones, smartwatches, etc. This trend provides new services and applications for many users and enhances their lifestyle. The rapid growth of the IoT makes the incorporation and connection of several devices a predominant procedure. Although there are many advantages of IoT devices, there are different challenges that come as network anomalies. In this research, the current studies in the use of deep learning (DL) in DDoS intrusion detection have been presented. This research aims to implement different Machine Learning (ML) algorithms in WEKA tools to analyze the detection performance for DDoS attacks using the most recent CICDDoS2019 datasets. CICDDoS2019 was found to be the model with best results. This research has used six different types of ML algorithms which are K_Nearest_Neighbors (K-NN), super vector machine (SVM), naïve bayes (NB), decision tree (DT), random forest (RF) and logistic regression (LR). The best accuracy result in the presented evaluation was achieved when utilizing the Decision Tree (DT) and Random Forest (RF) algorithms, 99% and 99%, respectively. However, the DT is better than RF because it has a shorter computation time, 4.53 s and 84.2 s, respectively. Finally, open issues for further research in future work are presented.https://www.mdpi.com/2079-9292/10/23/2919cyber securityIoTmachine learningintrusion detection systemIoT securityDDoS attack |
| spellingShingle | Rami J. Alzahrani Ahmed Alzahrani Security Analysis of DDoS Attacks Using Machine Learning Algorithms in Networks Traffic Electronics cyber security IoT machine learning intrusion detection system IoT security DDoS attack |
| title | Security Analysis of DDoS Attacks Using Machine Learning Algorithms in Networks Traffic |
| title_full | Security Analysis of DDoS Attacks Using Machine Learning Algorithms in Networks Traffic |
| title_fullStr | Security Analysis of DDoS Attacks Using Machine Learning Algorithms in Networks Traffic |
| title_full_unstemmed | Security Analysis of DDoS Attacks Using Machine Learning Algorithms in Networks Traffic |
| title_short | Security Analysis of DDoS Attacks Using Machine Learning Algorithms in Networks Traffic |
| title_sort | security analysis of ddos attacks using machine learning algorithms in networks traffic |
| topic | cyber security IoT machine learning intrusion detection system IoT security DDoS attack |
| url | https://www.mdpi.com/2079-9292/10/23/2919 |
| work_keys_str_mv | AT ramijalzahrani securityanalysisofddosattacksusingmachinelearningalgorithmsinnetworkstraffic AT ahmedalzahrani securityanalysisofddosattacksusingmachinelearningalgorithmsinnetworkstraffic |