| Title | Accuracy and Generalization of Deep Learning Applied to Large Scale Attacks |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Freas, Christopher B., Shah, Dhara, Harrison, Robert W. |
| Conference Name | 2021 IEEE International Conference on Communications Workshops (ICC Workshops) |
| Date Published | jun |
| Keywords | attack detection, attack vectors, Computational modeling, Conferences, correlation coefficient, Deep Learning, denial-of-service attack, Flow analysis, Human Behavior, machine learning, networks, Neural networks, pubcrawl, Resiliency, Scalability, telecommunication traffic |
| Abstract | Distributed denial of service attacks threaten the security and health of the Internet. Remediation relies on up-to-date and accurate attack signatures. Signature-based detection is relatively inexpensive computationally. Yet, signatures are inflexible when small variations exist in the attack vector. Attackers exploit this rigidity by altering their attacks to bypass the signatures. Our previous work revealed a critical problem with conventional machine learning models. Conventional models are unable to generalize on the temporal nature of network flow data to classify attacks. We thus explored the use of deep learning techniques on real flow data. We found that a variety of attacks could be identified with high accuracy compared to previous approaches. We show that a convolutional neural network can be implemented for this problem that is suitable for large volumes of data while maintaining useful levels of accuracy. |
| DOI | 10.1109/ICCWorkshops50388.2021.9473824 |
| Citation Key | freas_accuracy_2021 |
Accuracy and Generalization of Deep Learning Applied to Large Scale Attacks