With the ever-increasing reliance on digital networks for various aspects of
modern life, ensuring their security has become a critical challenge. Intrusion
Detection Systems play a crucial role in ensuring network security, actively
identifying and mitigating malicious behaviours. However, the relentless
advancement of cyber-threats has rendered traditional/classical approaches
insufficient in addressing the sophistication and complexity of attacks. This
paper proposes a novel 3-stage intrusion detection system inspired by a
simplified version of the Lockheed Martin cyber kill chain to detect advanced
multi-step attacks. The proposed approach consists of three models, each
responsible for detecting a group of attacks with common characteristics. The
detection outcome of the first two stages is used to conduct a feasibility
study on the possibility of predicting attacks in the third stage. Using the
ToN IoT dataset, we achieved an average of 94% F1-Score among different stages,
outperforming the benchmark approaches based on Random-forest model. Finally,
we comment on the feasibility of this approach to be integrated in a real-world
system and propose various possible future work.

提出了一种受 Lockheed Martin 网络攻击链启发的新型 3 阶段入侵检测系统，用于检测复杂多步骤的攻击，通过第一个和第二个阶段的检测来预测第三个阶段的攻击，使用 ToN IoT 数据集，实现了平均 94% 的 F1 分数，性能优于基于随机森林模型的基准方法。

基于图神经网络的多阶段攻击检测与预测：IoT 可行性研究

Multi-stage Attack Detection and Prediction Using Graph Neural Networks:  An IoT Feasibility Study

Although fast adversarial training has demonstrated both robustness and
efficiency, the problem of "catastrophic overfitting" has been observed. This
is a phenomenon in which, during single-step adversarial training, the robust
accuracy against projected gradient descent (PGD) suddenly decreases to 0%
after a few epochs, whereas the robust accuracy against fast gradient sign
method (FGSM) increases to 100%. In this paper, we demonstrate that
catastrophic overfitting is very closely related to the characteristic of
single-step adversarial training which uses only adversarial examples with the
maximum perturbation, and not all adversarial examples in the adversarial
direction, which leads to decision boundary distortion and a highly curved loss
surface. Based on this observation, we propose a simple method that not only
prevents catastrophic overfitting, but also overrides the belief that it is
difficult to prevent multi-step adversarial attacks with single-step
adversarial training.

本文研究了如何防止在单步对抗训练中出现的 “灾难性过拟合” 的问题，并提出了一种简单的方法，不仅可以预防此问题，而且能够在单步对抗训练中防止多步对抗攻击。