A Novel Framework for Detecting Network Intrusions Based on Machine Learning Methods

IF 0.7 Q3 COMPUTER SCIENCE, THEORY & METHODS International Journal of Advanced Computer Science and Applications Pub Date : 2023-01-01 DOI:10.14569/ijacsa.2023.0140755
B. Omarov, Nazgul Abdinurova, Zhamshidbek Abdulkhamidov
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Abstract

—In the rapidly evolving landscape of cyber threats, the efficacy of traditional rule-based network intrusion detection systems has become increasingly questionable. This paper introduces a novel framework for identifying network intrusions, leveraging the power of advanced machine learning techniques. The proposed methodology steps away from the rigidity of conventional systems, bringing a flexible, adaptive, and intuitive approach to the forefront of network security. This study employs a diverse blend of machine learning models including but not limited to, Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and Random Forests. This research explores an innovative feature extraction and selection technique that enables the model to focus on high-priority potential threats, minimizing noise and improving detection accuracy. The framework's performance has been rigorously evaluated through a series of experiments on benchmark datasets. The results consistently surpass traditional methods, demonstrating a remarkable increase in detection rates and a significant reduction in false positives. Further, the machine learning-based model demonstrated its ability to adapt to new threat landscapes, indicating its suitability in real-world scenarios. By marrying the agility of machine learning with the concreteness of network intrusion detection, this research opens up new avenues for dynamic and resilient cybersecurity. The framework offers an innovative solution that can identify, learn, and adapt to evolving network intrusions, shaping the future of cyber defense strategies.
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基于机器学习方法的网络入侵检测新框架
在快速发展的网络威胁环境中,传统的基于规则的网络入侵检测系统的有效性越来越受到质疑。本文介绍了一个新的框架来识别网络入侵,利用先进的机器学习技术的力量。所提出的方法远离传统系统的刚性,将灵活,自适应和直观的方法带到网络安全的最前沿。本研究采用了多种机器学习模型,包括但不限于卷积神经网络(cnn)、支持向量机(svm)和随机森林。本研究探索了一种创新的特征提取和选择技术,使模型能够专注于高优先级的潜在威胁,最小化噪声并提高检测精度。通过一系列的基准数据集实验,对该框架的性能进行了严格的评估。结果始终优于传统方法,显示出显着提高检出率和显着减少假阳性。此外,基于机器学习的模型证明了其适应新威胁环境的能力,表明其在现实场景中的适用性。通过将机器学习的敏捷性与网络入侵检测的具体性相结合,本研究为动态和弹性网络安全开辟了新的途径。该框架提供了一种创新的解决方案,可以识别、学习和适应不断发展的网络入侵,塑造未来的网络防御战略。
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来源期刊
CiteScore
2.30
自引率
22.20%
发文量
519
期刊介绍: IJACSA is a scholarly computer science journal representing the best in research. Its mission is to provide an outlet for quality research to be publicised and published to a global audience. The journal aims to publish papers selected through rigorous double-blind peer review to ensure originality, timeliness, relevance, and readability. In sync with the Journal''s vision "to be a respected publication that publishes peer reviewed research articles, as well as review and survey papers contributed by International community of Authors", we have drawn reviewers and editors from Institutions and Universities across the globe. A double blind peer review process is conducted to ensure that we retain high standards. At IJACSA, we stand strong because we know that global challenges make way for new innovations, new ways and new talent. International Journal of Advanced Computer Science and Applications publishes carefully refereed research, review and survey papers which offer a significant contribution to the computer science literature, and which are of interest to a wide audience. Coverage extends to all main-stream branches of computer science and related applications
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