DDoS attacks are an immense threat to online services, and numerous studies have been done to detect and defend against them. DDoS attacks, however, are becoming more sophisticated and launched with different purposes, making the detection and instant defense as important as analyzing the behavior of the attack during and after it takes place. Studying and modeling the Spatio-temporal evolvement of DDoS attacks is essential to predict, assess, and combat the problem, since recent studies have shown the emergence of wider and more powerful adversaries. This work aims to model seven Spatio-temporal behavioral characteristics of DDoS attacks, including the attack magnitude, the adversaries’ botnet information, and the attack’s source locality down to the organization. We leverage four state-of-the-art deep learning methods to construct an ensemble of models to capture and predict behavioral patterns of the attack. The proposed ensemble operates in two frequencies, hourly and daily, to actively model and predict the attack behavior and evolvement, and oversee the effect of implementing a defense mechanism.
|Title of host publication
|Information Security Applications - 21st International Conference, WISA 2020, Revised Selected Papers
|Springer Science and Business Media Deutschland GmbH
|Number of pages
|Published - 2020
|21st International Conference on Information Security Applications, WISA 2020 - Jeju Island, Korea, Republic of
Duration: 26 Aug 2020 → 28 Aug 2020
|Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
|21st International Conference on Information Security Applications, WISA 2020
|Korea, Republic of
|26/08/20 → 28/08/20
Bibliographical noteFunding Information:
Acknowledgement. This work was supported by NRF grant 2016K1A1A2912757, NIST grant 70NANB18H272, and NSF grant CNS-1524462 (S. Chen), and by the Institute for Smart, Secure and Connected Systems at CWRU (A. Wang).
© Springer Nature Switzerland AG 2020.
- DDoS Attacks Prediction
- Deep learning