Ensemble Models for Intrusion Detection System Classification.
Ensemble Models for Intrusion Detection System Classification.
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用于入侵检测系统分类的集成模型。
DOI：
10.47893/ijssan.2022.1209
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
I. Alsmadi
中科院分区：
文献类型：
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作者：
Geethamanikanta Jakka;I. Alsmadi
由于入侵检测与防御系统（IDS/IPS）问题的演化性质和主要影响因素的变化，将数据分析应用于入侵检测与防御系统是一个持续研究的问题。该领域的主要挑战是设计能够预测未知领域恶意软件的规则，处理问题的复杂性以及对高精度检测和高效率的冲突要求。在这个范围内，我们评估了最先进的集成学习模型在提高IDS/IPS性能和效率方面的使用。我们将我们的方法与使用该领域流行的开源数据集的其他现有方法进行了比较。
Using data analytics in the problem of Intrusion Detection and Prevention Systems (IDS/IPS) is a continuous research problem due to the evolutionary nature of the problem and the changes in major influencing factors. The main challenges in this area are designing rules that can predict malware in unknown territories and dealing with the complexity of the problem and the conflicting requirements regarding high accuracy of detection and high efficiency. In this scope, we evaluated the usage of state-of-the-art ensemble learning models in improving the performance and efficiency of IDS/IPS. We compared our approaches with other existing approaches using popular open-source datasets available in this area.