STLGBM-DDS: An Efficient Data Balanced DoS Detection System for Wireless Sensor Networks on Big Data Environment
Wireless Sensor Networks(WSNs) are vulnerable to a variety of unique security risks and threats in their data collection and transmission processes. One of the most common attacks on WSNs that can target all layers of the protocol stack is the DoS attack. In this study, a unique DoS Intrusion Detect...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2022-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9869814/ |
| _version_ | 1798001075790282752 |
|---|---|
| author | Murat Dener Samed Al Abdullah Orman |
| author_facet | Murat Dener Samed Al Abdullah Orman |
| author_sort | Murat Dener |
| collection | DOAJ |
| description | Wireless Sensor Networks(WSNs) are vulnerable to a variety of unique security risks and threats in their data collection and transmission processes. One of the most common attacks on WSNs that can target all layers of the protocol stack is the DoS attack. In this study, a unique DoS Intrusion Detection System (DDS) is proposed to detect DoS attacks specific to WSNs. The proposed system is an ensemble intrusion detection system called STLGBM-DDS, which is developed on Apache Spark big data platform in Google Colab environment, combining LightGBM machine learning algorithm, data balancing and feature selection processes. In order to reduce the effects of data imbalance on system performance, data imbalance processing consisting of Synthetic Minority Oversampling Technique (SMOTE) and Tomek-Links sampling methods called STL was used. In addition, Information Gain Ratio was used as a feature selection technique in the data preprocessing stage. The effects of both data balancing and feature selection stages on the detection performance of the system were investigated. The results obtained were evaluated using the Accuracy, F-Measure, Precision, Recall, ROC Curve and Precision-Recall Curve parameters. As a result, the proposed method achieved an overall accuracy of 99.95%. Also, it achieved 99.99%, 99.96%, 99.98%, 99.92%, and 99.87% accuracy performance according to Normal, Grayhole, Blackhole, TDMA and Flooding classes, respectively. According to the results obtained, the proposed method has achieved very successful results in DoS attack detection in WSNs compared to current methods. |
| first_indexed | 2024-04-11T11:30:23Z |
| format | Article |
| id | doaj.art-8428a9136083415ca6f235713dfa4204 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-11T11:30:23Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-8428a9136083415ca6f235713dfa42042022-12-22T04:26:09ZengIEEEIEEE Access2169-35362022-01-0110929319294510.1109/ACCESS.2022.32028079869814STLGBM-DDS: An Efficient Data Balanced DoS Detection System for Wireless Sensor Networks on Big Data EnvironmentMurat Dener0Samed Al1Abdullah Orman2Department of Information Security Engineering, Graduate School of Natural and Applied Sciences, Gazi University, Ankara, TurkeyDepartment of Information Security Engineering, Graduate School of Natural and Applied Sciences, Gazi University, Ankara, TurkeyDepartment of Computer Technologies, Yıldırım Beyazıt University, Ankara, TurkeyWireless Sensor Networks(WSNs) are vulnerable to a variety of unique security risks and threats in their data collection and transmission processes. One of the most common attacks on WSNs that can target all layers of the protocol stack is the DoS attack. In this study, a unique DoS Intrusion Detection System (DDS) is proposed to detect DoS attacks specific to WSNs. The proposed system is an ensemble intrusion detection system called STLGBM-DDS, which is developed on Apache Spark big data platform in Google Colab environment, combining LightGBM machine learning algorithm, data balancing and feature selection processes. In order to reduce the effects of data imbalance on system performance, data imbalance processing consisting of Synthetic Minority Oversampling Technique (SMOTE) and Tomek-Links sampling methods called STL was used. In addition, Information Gain Ratio was used as a feature selection technique in the data preprocessing stage. The effects of both data balancing and feature selection stages on the detection performance of the system were investigated. The results obtained were evaluated using the Accuracy, F-Measure, Precision, Recall, ROC Curve and Precision-Recall Curve parameters. As a result, the proposed method achieved an overall accuracy of 99.95%. Also, it achieved 99.99%, 99.96%, 99.98%, 99.92%, and 99.87% accuracy performance according to Normal, Grayhole, Blackhole, TDMA and Flooding classes, respectively. According to the results obtained, the proposed method has achieved very successful results in DoS attack detection in WSNs compared to current methods.https://ieeexplore.ieee.org/document/9869814/Wireless sensor networksDoS attacksintrusion detectiondeep learningimbalanced data |
| spellingShingle | Murat Dener Samed Al Abdullah Orman STLGBM-DDS: An Efficient Data Balanced DoS Detection System for Wireless Sensor Networks on Big Data Environment IEEE Access Wireless sensor networks DoS attacks intrusion detection deep learning imbalanced data |
| title | STLGBM-DDS: An Efficient Data Balanced DoS Detection System for Wireless Sensor Networks on Big Data Environment |
| title_full | STLGBM-DDS: An Efficient Data Balanced DoS Detection System for Wireless Sensor Networks on Big Data Environment |
| title_fullStr | STLGBM-DDS: An Efficient Data Balanced DoS Detection System for Wireless Sensor Networks on Big Data Environment |
| title_full_unstemmed | STLGBM-DDS: An Efficient Data Balanced DoS Detection System for Wireless Sensor Networks on Big Data Environment |
| title_short | STLGBM-DDS: An Efficient Data Balanced DoS Detection System for Wireless Sensor Networks on Big Data Environment |
| title_sort | stlgbm dds an efficient data balanced dos detection system for wireless sensor networks on big data environment |
| topic | Wireless sensor networks DoS attacks intrusion detection deep learning imbalanced data |
| url | https://ieeexplore.ieee.org/document/9869814/ |
| work_keys_str_mv | AT muratdener stlgbmddsanefficientdatabalanceddosdetectionsystemforwirelesssensornetworksonbigdataenvironment AT samedal stlgbmddsanefficientdatabalanceddosdetectionsystemforwirelesssensornetworksonbigdataenvironment AT abdullahorman stlgbmddsanefficientdatabalanceddosdetectionsystemforwirelesssensornetworksonbigdataenvironment |