论文标题
使用KDDCUP'99和NSL-KDD数据集的支持向量机的入侵检测系统：一项全面的调查
Intrusion Detection Systems Using Support Vector Machines on the KDDCUP'99 and NSL-KDD Datasets: A Comprehensive Survey
论文作者
论文摘要
如今，随着网络攻击和网络间谍活动的不断增长，对更好，更强大的入侵检测系统（ID）的需求甚至更加有必要。 ID的基本任务是在检测Internet的攻击方面充当第一道防线。随着入侵者的入侵策略变得越来越复杂且难以检测，研究人员已经开始应用新颖的机器学习（ML）技术来有效地检测入侵者，从而保留了互联网用户对整个Internet网络安全的信息和整体信任。在过去的十年中，基于ML和深度学习（DL）架构的侵入检测技术的爆炸激增，例如DARPA，KDDCUP'99，NSL-KDD，NSL-KDD，CAIDA，CAIDA，CTU-13，CTU-13，UNSW-NB15，UNSW-NB15。在这项研究中，我们回顾了当代文献，并对采用支持向量机（SVM）算法作为分类器的不同类型的入侵检测技术进行了全面调查。我们仅关注已经在网络安全中评估过的两个数据集评估的研究，即KDDCUP'99和NSL-KDD数据集。我们提供了每种方法的摘要，确定了SVMS分类器的作用以及研究中涉及的所有其他算法。此外，我们以表格形式对每种方法进行了批判性综述，强调了所调查方法的性能指标，优势和局限性。
With the growing rates of cyber-attacks and cyber espionage, the need for better and more powerful intrusion detection systems (IDS) is even more warranted nowadays. The basic task of an IDS is to act as the first line of defense, in detecting attacks on the internet. As intrusion tactics from intruders become more sophisticated and difficult to detect, researchers have started to apply novel Machine Learning (ML) techniques to effectively detect intruders and hence preserve internet users' information and overall trust in the entire internet network security. Over the last decade, there has been an explosion of research on intrusion detection techniques based on ML and Deep Learning (DL) architectures on various cyber security-based datasets such as the DARPA, KDDCUP'99, NSL-KDD, CAIDA, CTU-13, UNSW-NB15. In this research, we review contemporary literature and provide a comprehensive survey of different types of intrusion detection technique that applies Support Vector Machines (SVMs) algorithms as a classifier. We focus only on studies that have been evaluated on the two most widely used datasets in cybersecurity namely: the KDDCUP'99 and the NSL-KDD datasets. We provide a summary of each method, identifying the role of the SVMs classifier, and all other algorithms involved in the studies. Furthermore, we present a critical review of each method, in tabular form, highlighting the performance measures, strengths, and limitations of each of the methods surveyed.
