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Towards realistic problem-space adversarial attacks against machine learning in network intrusion detection., , , and . ARES, page 113:1-113:8. ACM, (2024)Auto-scaling Applications in the Cloud by Simple Indexes with Complex Loads., , , and . WETICE, page 76-81. IEEE, (2020)2L-ZED-IDS: A Two-Level Anomaly Detector for Multiple Attack Classes., , and . AINA Workshops, volume 1150 of Advances in Intelligent Systems and Computing, page 687-696. Springer, (2020)Botnet Detection in the Internet of Things through All-in-one Deep Autoencoding., , and . ARES, page 90:1-90:7. ACM, (2022)Simpler Is Better: On the Use of Autoencoders for Intrusion Detection., , and . QUATIC, volume 1621 of Communications in Computer and Information Science, page 223-238. Springer, (2022)AutoLog: Anomaly detection by deep autoencoding of system logs., , and . Expert Syst. Appl., (2022)Machine Learning on Public Intrusion Datasets: Academic Hype or Concrete Advances in NIDS?, , and . DSN-S, page 132-136. IEEE, (2023)A Case Study with CICIDS2017 on the Robustness of Machine Learning against Adversarial Attacks in Intrusion Detection., , , and . ARES, page 74:1-74:8. ACM, (2023)No more DoS? An empirical study on defense techniques for web server Denial of Service mitigation., , and . J. Netw. Comput. Appl., (2022)A case study on the representativeness of public DoS network traffic data for cybersecurity research., , , and . ARES, page 6:1-6:10. ACM, (2020)