Chitkara University Institute of Engineering and Technology, Chitkara University, Chandigarh, Punjab, India.
Robotics and Internet-of-Things Laboratory, Prince Sultan University, Riyadh, Saudi Arabia.
PLoS One. 2024 Sep 12;19(9):e0308206. doi: 10.1371/journal.pone.0308206. eCollection 2024.
In response to the rapidly evolving threat landscape in network security, this paper proposes an Evolutionary Machine Learning Algorithm designed for robust intrusion detection. We specifically address challenges such as adaptability to new threats and scalability across diverse network environments. Our approach is validated using two distinct datasets: BoT-IoT, reflecting a range of IoT-specific attacks, and UNSW-NB15, offering a broader context of network intrusion scenarios using GA based hybrid DT-SVM. This selection facilitates a comprehensive evaluation of the algorithm's effectiveness across varying attack vectors. Performance metrics including accuracy, recall, and false positive rates are meticulously chosen to demonstrate the algorithm's capability to accurately identify and adapt to both known and novel threats, thereby substantiating the algorithm's potential as a scalable and adaptable security solution. This study aims to advance the development of intrusion detection systems that are not only reactive but also preemptively adaptive to emerging cyber threats." During the feature selection step, a GA is used to discover and preserve the most relevant characteristics from the dataset by using evolutionary principles. Through the use of this technology based on genetic algorithms, the subset of features is optimised, enabling the subsequent classification model to focus on the most relevant components of network data. In order to accomplish this, DT-SVM classification and GA-driven feature selection are integrated in an effort to strike a balance between efficiency and accuracy. The system has been purposefully designed to efficiently handle data streams in real-time, ensuring that intrusions are promptly and precisely detected. The empirical results corroborate the study's assertion that the IDS outperforms traditional methodologies.
针对网络安全中不断演变的威胁形势，本文提出了一种用于稳健入侵检测的进化机器学习算法。我们专门解决了新威胁的适应性和跨不同网络环境的可扩展性等挑战。我们的方法使用两个不同的数据集进行验证：BoT-IoT，反映了一系列特定于物联网的攻击，以及 UNSW-NB15，提供了更广泛的网络入侵场景，使用基于 GA 的混合 DT-SVM。这种选择有助于全面评估算法在不同攻击向量下的有效性。精心选择了准确性、召回率和误报率等性能指标，以证明算法能够准确识别和适应已知和新的威胁，从而证明算法作为一种可扩展和自适应的安全解决方案的潜力。本研究旨在推进入侵检测系统的发展，这些系统不仅具有反应性，而且能够主动适应新兴的网络威胁。
在特征选择步骤中，使用遗传算法根据进化原则发现和保留数据集中最相关的特征。通过使用这种基于遗传算法的技术，对特征子集进行了优化，从而使后续的分类模型能够专注于网络数据的最相关部分。为了实现这一点，DT-SVM 分类和 GA 驱动的特征选择被集成在一起，以在效率和准确性之间取得平衡。该系统旨在高效地实时处理数据流，确保及时准确地检测入侵。实证结果证实了该研究的断言，即 IDS 优于传统方法。
