The field of intrusion detection and synthetic data generation is rapidly evolving, with a focus on developing more effective and realistic methods for evaluating and improving intrusion detection systems (IDS). Researchers are exploring new approaches to benchmarking IDS, including the use of evasion-focused Capture-the-Flag competitions and universal adversarial perturbations. Additionally, the generation of synthetic data is becoming increasingly important for training and testing IDS, with techniques such as generative adversarial networks (GANs) being used to create realistic synthetic traffic. However, the privacy implications of synthetic data generation are also being investigated, with a focus on developing metrics and methods for quantifying and minimizing privacy leakage. Noteworthy papers in this area include: StealthCup, which presents a novel evaluation methodology for IDS using an evasion-focused Capture-the-Flag competition. A Novel and Practical Universal Adversarial Perturbations, which proposes a new attack against Deep Reinforcement Learning-based IDS. Synthetic Data: AI's New Weapon Against Android Malware, which introduces a malware synthetic data generation methodology using a conditional GAN. Quantifying the Privacy Implications of High-Fidelity Synthetic Network Traffic, which evaluates the vulnerability of different generative models to privacy attacks.