TY - GEN
T1 - Insights into attacks’ progression
AU - Abusnaina, Ahmed
AU - Abuhamad, Mohammed
AU - Nyang, Dae Hun
AU - Chen, Songqing
AU - Wang, An
AU - Mohaisen, David
N1 - Funding 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).
Publisher Copyright:
© Springer Nature Switzerland AG 2020.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - DDoS Attacks Prediction
KW - Deep learning
UR - http://www.scopus.com/inward/record.url?scp=85098250553&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-65299-9_27
DO - 10.1007/978-3-030-65299-9_27
M3 - Conference contribution
AN - SCOPUS:85098250553
SN - 9783030652982
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 362
EP - 374
BT - Information Security Applications - 21st International Conference, WISA 2020, Revised Selected Papers
A2 - You, Ilsun
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 26 August 2020 through 28 August 2020
ER -