Computer Science Department, College of Computer and Information Systems, Umm Al-Qura University, Makkah 21955, Saudi Arabia.
SAUDI ARAMCO Cybersecurity Chair, Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia.
Sensors (Basel). 2021 Oct 25;21(21):7070. doi: 10.3390/s21217070.
The significant growth in the use of the Internet and the rapid development of network technologies are associated with an increased risk of network attacks. Network attacks refer to all types of unauthorized access to a network including any attempts to damage and disrupt the network, often leading to serious consequences. Network attack detection is an active area of research in the community of cybersecurity. In the literature, there are various descriptions of network attack detection systems involving various intelligent-based techniques including machine learning (ML) and deep learning (DL) models. However, although such techniques have proved useful within specific domains, no technique has proved useful in mitigating all kinds of network attacks. This is because some intelligent-based approaches lack essential capabilities that render them reliable systems that are able to confront different types of network attacks. This was the main motivation behind this research, which evaluates contemporary intelligent-based research directions to address the gap that still exists in the field. The main components of any intelligent-based system are the training datasets, the algorithms, and the evaluation metrics; these were the main benchmark criteria used to assess the intelligent-based systems included in this research article. This research provides a rich source of references for scholars seeking to determine their scope of research in this field. Furthermore, although the paper does present a set of suggestions about future inductive directions, it leaves the reader free to derive additional insights about how to develop intelligent-based systems to counter current and future network attacks.
互联网的广泛应用和网络技术的飞速发展，使得网络攻击的风险日益增加。网络攻击是指所有未经授权访问网络的行为，包括任何试图破坏和扰乱网络的行为，通常会导致严重后果。网络攻击检测是网络安全社区中一个活跃的研究领域。在文献中，有各种描述网络攻击检测系统的方法，涉及各种基于智能的技术，包括机器学习（ML）和深度学习（DL）模型。然而，尽管这些技术在特定领域已经证明是有用的，但没有一种技术能够有效地减轻各种类型的网络攻击。这是因为一些基于智能的方法缺乏必要的能力，使它们成为可靠的系统，能够应对不同类型的网络攻击。这就是这项研究的主要动机，该研究评估了当代基于智能的研究方向，以解决该领域仍然存在的差距。任何基于智能的系统的主要组成部分是训练数据集、算法和评估指标；这些是评估本文中包含的基于智能的系统的主要基准标准。本研究为学者提供了一个丰富的参考资源，帮助他们确定在该领域的研究范围。此外，尽管本文确实提出了一系列关于未来归纳方向的建议，但它让读者可以自由地得出关于如何开发基于智能的系统来应对当前和未来网络攻击的更多见解。
