A Comparative Evaluation of Machine Learning-Based Intrusion Detection Systems for Securing Cloud Environments
Cloud computing has advanced significantly alongside the growth of communication technology and data exchange. Many businesses and organizations now adopt cloud computing solutions and services to enhance flexibility and scalability. However, despite its numerous advantages, cloud computing remains...
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| Format: | Article |
| Language: | English |
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Naif University Publishing House
2024-12-01
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| Series: | Journal of Information Security and Cybercrimes Research |
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| Online Access: | https://journals.nauss.edu.sa/index.php/JISCR/article/view/3141 |
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| _version_ | 1849236746576855040 |
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| author | Mohammad Shadi Alhakeem Khawla Bin Ajlan |
| author_facet | Mohammad Shadi Alhakeem Khawla Bin Ajlan |
| author_sort | Mohammad Shadi Alhakeem |
| collection | DOAJ |
| description | Cloud computing has advanced significantly alongside the growth of communication technology and data exchange. Many businesses and organizations now adopt cloud computing solutions and services to enhance flexibility and scalability. However, despite its numerous advantages, cloud computing remains increasingly susceptible to various security threats that can disrupt services and business operations. This highlights the critical need to strengthen the security of cloud environments. In this context, implementing robust protection measures, such as Intrusion Detection Systems (IDS), is essential to mitigate potential threats and safeguard sensitive data. To effectively counter the ever-evolving cyber threats landscape, IDS must possess adaptive capabilities. Hence, integrating Machine Learning (ML) technologies is imperative for the detection of a broad and diverse range of cyber threats, thereby enhancing the overall bolstering the security posture of the environment.
This research explores the integration of ML technologies in IDS and examines the application of feature selection methods to identify the key and most significant indicators for attack detection. The study conducts a comparative analysis of five ML techniques, employing two distinct feature selection methods to evaluate their effectiveness in strengthening the security of cloud environments. Using a recently developed, reputable dataset and concentrating on attack types that pose significant threats to cloud environments, our experimental results offer a comprehensive evaluation of these techniques, including a variety of machine learning algorithm performance metrics. |
| format | Article |
| id | doaj-art-6a519d9a5af74313ab87f381626a8af6 |
| institution | Kabale University |
| issn | 1658-7782 1658-7790 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Naif University Publishing House |
| record_format | Article |
| series | Journal of Information Security and Cybercrimes Research |
| spelling | doaj-art-6a519d9a5af74313ab87f381626a8af62025-08-20T04:02:09ZengNaif University Publishing HouseJournal of Information Security and Cybercrimes Research1658-77821658-77902024-12-017212714210.26735/RSND37402845A Comparative Evaluation of Machine Learning-Based Intrusion Detection Systems for Securing Cloud EnvironmentsMohammad Shadi Alhakeem0Khawla Bin Ajlan1Naif Arab University for Security Sciences, Riyadh, Saudi ArabiaNaif Arab University for Security Sciences, Riyadh, Saudi ArabiaCloud computing has advanced significantly alongside the growth of communication technology and data exchange. Many businesses and organizations now adopt cloud computing solutions and services to enhance flexibility and scalability. However, despite its numerous advantages, cloud computing remains increasingly susceptible to various security threats that can disrupt services and business operations. This highlights the critical need to strengthen the security of cloud environments. In this context, implementing robust protection measures, such as Intrusion Detection Systems (IDS), is essential to mitigate potential threats and safeguard sensitive data. To effectively counter the ever-evolving cyber threats landscape, IDS must possess adaptive capabilities. Hence, integrating Machine Learning (ML) technologies is imperative for the detection of a broad and diverse range of cyber threats, thereby enhancing the overall bolstering the security posture of the environment. This research explores the integration of ML technologies in IDS and examines the application of feature selection methods to identify the key and most significant indicators for attack detection. The study conducts a comparative analysis of five ML techniques, employing two distinct feature selection methods to evaluate their effectiveness in strengthening the security of cloud environments. Using a recently developed, reputable dataset and concentrating on attack types that pose significant threats to cloud environments, our experimental results offer a comprehensive evaluation of these techniques, including a variety of machine learning algorithm performance metrics.https://journals.nauss.edu.sa/index.php/JISCR/article/view/3141intrusion detection systemsmachine-learningcloud environmentsfeature selectioncybercrimes |
| spellingShingle | Mohammad Shadi Alhakeem Khawla Bin Ajlan A Comparative Evaluation of Machine Learning-Based Intrusion Detection Systems for Securing Cloud Environments Journal of Information Security and Cybercrimes Research intrusion detection systems machine-learning cloud environments feature selection cybercrimes |
| title | A Comparative Evaluation of Machine Learning-Based Intrusion Detection Systems for Securing Cloud Environments |
| title_full | A Comparative Evaluation of Machine Learning-Based Intrusion Detection Systems for Securing Cloud Environments |
| title_fullStr | A Comparative Evaluation of Machine Learning-Based Intrusion Detection Systems for Securing Cloud Environments |
| title_full_unstemmed | A Comparative Evaluation of Machine Learning-Based Intrusion Detection Systems for Securing Cloud Environments |
| title_short | A Comparative Evaluation of Machine Learning-Based Intrusion Detection Systems for Securing Cloud Environments |
| title_sort | comparative evaluation of machine learning based intrusion detection systems for securing cloud environments |
| topic | intrusion detection systems machine-learning cloud environments feature selection cybercrimes |
| url | https://journals.nauss.edu.sa/index.php/JISCR/article/view/3141 |
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