Version 1
: Received: 14 April 2024 / Approved: 19 April 2024 / Online: 19 April 2024 (07:40:08 CEST)
How to cite:
Nędza, P.; Domżał, J. Detecting DoS Attacks through Synthetic User Behavior with Long-Short Term Memory Network. Preprints2024, 2024041314. https://doi.org/10.20944/preprints202404.1314.v1
Nędza, P.; Domżał, J. Detecting DoS Attacks through Synthetic User Behavior with Long-Short Term Memory Network. Preprints 2024, 2024041314. https://doi.org/10.20944/preprints202404.1314.v1
Nędza, P.; Domżał, J. Detecting DoS Attacks through Synthetic User Behavior with Long-Short Term Memory Network. Preprints2024, 2024041314. https://doi.org/10.20944/preprints202404.1314.v1
APA Style
Nędza, P., & Domżał, J. (2024). Detecting DoS Attacks through Synthetic User Behavior with Long-Short Term Memory Network. Preprints. https://doi.org/10.20944/preprints202404.1314.v1
Chicago/Turabian Style
Nędza, P. and Jerzy Domżał. 2024 "Detecting DoS Attacks through Synthetic User Behavior with Long-Short Term Memory Network" Preprints. https://doi.org/10.20944/preprints202404.1314.v1
Abstract
With the escalation of size and complexity of modern Denial of Service attacks, there is a need for research in the context of Machine Learning (ML) used in attack execution and defense against such attacks. This paper investigates the potential use of ML in generating behavioral telemetry data using Long-Short Term Memory network and spoofing requests for the analyzed traffic to look legitimate. For this research, a custom testing environment was built, that listens for mouse and keyboard events and analyzes them accordingly. While the economic feasibility of this attack currently limits its immediate threat, advancements in technology could make it more cost-effective for attackers in the future. Therefore, proactive development of countermeasures remains essential to mitigate potential risks and stay ahead of evolving attack methods.
Keywords
denial of service; machine learning; behavioral telemetry; long-short term memory
Subject
Computer Science and Mathematics, Computer Networks and Communications
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
