Cloud-Based Intrusion Detection Approach Using Machine Learning Techniques
Cloud computing (CC) is a novel technology that has made it easier to access network and computer resources on demand such as storage and data management services. In addition, it aims to strengthen systems and make them useful. Regardless of these advantages, cloud providers suffer from many securi...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2023-09-01
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| Series: | Big Data Mining and Analytics |
| Subjects: | |
| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2022.9020038 |
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| _version_ | 1832544828745842688 |
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| author | Hanaa Attou Azidine Guezzaz Said Benkirane Mourade Azrour Yousef Farhaoui |
| author_facet | Hanaa Attou Azidine Guezzaz Said Benkirane Mourade Azrour Yousef Farhaoui |
| author_sort | Hanaa Attou |
| collection | DOAJ |
| description | Cloud computing (CC) is a novel technology that has made it easier to access network and computer resources on demand such as storage and data management services. In addition, it aims to strengthen systems and make them useful. Regardless of these advantages, cloud providers suffer from many security limits. Particularly, the security of resources and services represents a real challenge for cloud technologies. For this reason, a set of solutions have been implemented to improve cloud security by monitoring resources, services, and networks, then detect attacks. Actually, intrusion detection system (IDS) is an enhanced mechanism used to control traffic within networks and detect abnormal activities. This paper presents a cloud-based intrusion detection model based on random forest (RF) and feature engineering. Specifically, the RF classifier is obtained and integrated to enhance accuracy (ACC) of the proposed detection model. The proposed model approach has been evaluated and validated on two datasets and gives 98.3% ACC and 99.99% ACC using Bot-IoT and NSL-KDD datasets, respectively. Consequently, the obtained results present good performances in terms of ACC, precision, and recall when compared to the recent related works. |
| format | Article |
| id | doaj-art-831bac8cf73e4f2f855189504d29f54d |
| institution | Kabale University |
| issn | 2096-0654 |
| language | English |
| publishDate | 2023-09-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-831bac8cf73e4f2f855189504d29f54d2025-02-03T09:17:07ZengTsinghua University PressBig Data Mining and Analytics2096-06542023-09-016331132010.26599/BDMA.2022.9020038Cloud-Based Intrusion Detection Approach Using Machine Learning TechniquesHanaa Attou0Azidine Guezzaz1Said Benkirane2Mourade Azrour3Yousef Farhaoui4Technology Higher School Essaouira, Cadi Ayyad University, Marrakech 44000, Morocco.Technology Higher School Essaouira, Cadi Ayyad University, Marrakech 44000, Morocco.Technology Higher School Essaouira, Cadi Ayyad University, Marrakech 44000, Morocco.STI Laboratory, the IDMS team, Faculty of Sciences and Techniques, Moulay Ismail University of Meknès, Errachidia 25003, Morocco.STI Laboratory, the IDMS team, Faculty of Sciences and Techniques, Moulay Ismail University of Meknès, Errachidia 25003, Morocco.Cloud computing (CC) is a novel technology that has made it easier to access network and computer resources on demand such as storage and data management services. In addition, it aims to strengthen systems and make them useful. Regardless of these advantages, cloud providers suffer from many security limits. Particularly, the security of resources and services represents a real challenge for cloud technologies. For this reason, a set of solutions have been implemented to improve cloud security by monitoring resources, services, and networks, then detect attacks. Actually, intrusion detection system (IDS) is an enhanced mechanism used to control traffic within networks and detect abnormal activities. This paper presents a cloud-based intrusion detection model based on random forest (RF) and feature engineering. Specifically, the RF classifier is obtained and integrated to enhance accuracy (ACC) of the proposed detection model. The proposed model approach has been evaluated and validated on two datasets and gives 98.3% ACC and 99.99% ACC using Bot-IoT and NSL-KDD datasets, respectively. Consequently, the obtained results present good performances in terms of ACC, precision, and recall when compared to the recent related works.https://www.sciopen.com/article/10.26599/BDMA.2022.9020038cloud securityanomaly detectionfeatures engineeringrandom forest |
| spellingShingle | Hanaa Attou Azidine Guezzaz Said Benkirane Mourade Azrour Yousef Farhaoui Cloud-Based Intrusion Detection Approach Using Machine Learning Techniques Big Data Mining and Analytics cloud security anomaly detection features engineering random forest |
| title | Cloud-Based Intrusion Detection Approach Using Machine Learning Techniques |
| title_full | Cloud-Based Intrusion Detection Approach Using Machine Learning Techniques |
| title_fullStr | Cloud-Based Intrusion Detection Approach Using Machine Learning Techniques |
| title_full_unstemmed | Cloud-Based Intrusion Detection Approach Using Machine Learning Techniques |
| title_short | Cloud-Based Intrusion Detection Approach Using Machine Learning Techniques |
| title_sort | cloud based intrusion detection approach using machine learning techniques |
| topic | cloud security anomaly detection features engineering random forest |
| url | https://www.sciopen.com/article/10.26599/BDMA.2022.9020038 |
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