Supervised detection of network attacks has always been a critical part of network intrusion detection systems (NIDS). Nowadays, in a pivotal time for artificial intelligence (AI), with even more sophisticated attacks that utilize advanced techniques, such as generative artificial intelligence (GenAI) and reinforcement learning, it has become a vital component if we wish to protect our personal data, which are scattered across the web. In this paper, we address two tasks, in the first unified multi-modal NIDS dataset, which incorporates flow-level data, packet payload information and temporal contextual features, from the reprocessed CIC-IDS-2017, CIC-IoT-2023, UNSW-NB15 and CIC-DDoS-2019, with the same feature space. In the first task we use machine learning (ML) algorithms, with stratified cross validation, in order to prevent network attacks, with stability and reliability. In the second task we use adversarial learning algorithms to generate synthetic data, compare them with the real ones and evaluate their fidelity, utility and privacy using the SDV framework, f-divergences, distinguishability and non-parametric statistical tests. The findings provide stable ML models for intrusion detection and generative models with high fidelity and utility, by combining the Synthetic Data Vault framework, the TRTS and TSTR tests, with non-parametric statistical tests and f-divergence measures.

翻译：网络攻击的有监督检测一直是网络入侵检测系统（NIDS）的关键组成部分。如今，在人工智能（AI）发展的关键时期，随着利用生成式人工智能（GenAI）和强化学习等先进技术的更复杂攻击的出现，若我们想保护散落在网络中的个人数据，这已成为至关重要的环节。本文在首个统一的多模态NIDS数据集上处理两项任务，该数据集整合了来自重新处理的CIC-IDS-2017、CIC-IoT-2023、UNSW-NB15和CIC-DDoS-2019的流级数据、数据包载荷信息及时间上下文特征，并具有相同的特征空间。第一项任务中，我们采用机器学习（ML）算法，结合分层交叉验证，以稳定可靠地防御网络攻击。第二项任务中，我们利用对抗学习算法生成合成数据，将其与真实数据进行比较，并借助SDV框架、f-散度、可区分性及非参数统计检验，评估其保真度、效用性和隐私性。研究结果综合运用Synthetic Data Vault框架、TRTS和TSTR测试、非参数统计检验及f-散度度量，为入侵检测提供了稳定的ML模型，并生成了具有高保真度和高效用性的生成模型。