Peppes Nikolaos, Alexakis Theodoros, Adamopoulou Evgenia, Demestichas Konstantinos
School of Electrical and Computer Engineering, National Technical University of Athens, 15773 Athens, Greece.
Department of Agricultural Economy and Development, Agricultural University of Athens, 15855 Athens, Greece.
Sensors (Basel). 2023 Jan 12;23(2):900. doi: 10.3390/s23020900.
Digitization of most of the services that people use in their everyday life has, among others, led to increased needs for cybersecurity. As digital tools increase day by day and new software and hardware launch out-of-the box, detection of known existing vulnerabilities, or zero-day as they are commonly known, becomes one of the most challenging situations for cybersecurity experts. Zero-day vulnerabilities, which can be found in almost every new launched software and/or hardware, can be exploited instantly by malicious actors with different motives, posing threats for end-users. In this context, this study proposes and describes a holistic methodology starting from the generation of zero-day-type, yet realistic, data in tabular format and concluding to the evaluation of a Neural Network zero-day attacks' detector which is trained with and without synthetic data. This methodology involves the design and employment of Generative Adversarial Networks (GANs) for synthetically generating a new and larger dataset of zero-day attacks data. The newly generated, by the Zero-Day GAN (ZDGAN), dataset is then used to train and evaluate a Neural Network classifier for zero-day attacks. The results show that the generation of zero-day attacks data in tabular format reaches an equilibrium after about 5000 iterations and produces data that are almost identical to the original data samples. Last but not least, it should be mentioned that the Neural Network model that was trained with the dataset containing the ZDGAN generated samples outperformed the same model when the later was trained with only the original dataset and achieved results of high validation accuracy and minimal validation loss.
人们在日常生活中使用的大多数服务的数字化，除其他外，导致了对网络安全的需求增加。随着数字工具日益增多，新的软件和硬件不断开箱即用，检测已知的现有漏洞，即通常所说的零日漏洞，成为网络安全专家面临的最具挑战性的情况之一。零日漏洞几乎可以在每一个新推出的软件和/或硬件中找到，怀有不同动机的恶意行为者可以立即利用这些漏洞，对终端用户构成威胁。在此背景下，本研究提出并描述了一种整体方法，该方法从生成表格格式的零日类型但逼真的数据开始，到评估一个使用和不使用合成数据进行训练的神经网络零日攻击检测器。该方法涉及设计和应用生成对抗网络（GAN）来合成生成一个新的、更大的零日攻击数据数据集。然后，由零日GAN（ZDGAN）新生成的数据集用于训练和评估一个用于零日攻击的神经网络分类器。结果表明，以表格格式生成零日攻击数据在大约5000次迭代后达到平衡，并生成与原始数据样本几乎相同的数据。最后但同样重要的是，应该提到的是，使用包含ZDGAN生成样本的数据集进行训练的神经网络模型，在仅使用原始数据集进行训练时，其性能优于相同模型，并取得了高验证准确率和最小验证损失的结果。
