Unsupervised AutoML and Dimensionality Reduction for Autonomous DDoS Attack Prediction

Author(s)

de Neira, Anderson B. ; Borges, Ligia F. ; Batista, Daniel M. ; Nogueira, Michele

• Document type: Journal article
• Source: 2024 IEEE LATIN-AMERICAN CONFERENCE ON COMMUNICATIONS, LATINCOM; v. N/A, p. 6-pg., 2024-01-01.

Abstract

Machine learning models and feature selection are crucial for predicting Distributed Denial of Service (DDoS) attacks. Predicting attacks with high accuracy allows security teams to reduce attack damage. However, diversity in attacks and models limits predictions. Moreover, the dependence on labeled data and the utilization of unexpressive features restrict the performance of prediction models. This work proposes the AUTO-SEE technique to solve this problem. The technique engineers new features to reveal signals of attack preparation and selects the best features and the optimal machine learning model without using labeled data. This enables the technique to operate autonomously and predict different DDoS attack types, also increasing the protection against 0-day attacks. The results indicate that AUTO-SEE reduces error by up to 44.15%, reaching an accuracy between 72.41 and 100% in predicting DDoS attacks. (AU)

• FAPESP's process: 23/13773-0 - Early Warning Signals Engineering for Predicting DDoS Attacks
• Grantee: Anderson Bergamini de Neira
• Support Opportunities: Scholarships in Brazil - Technical Training Program - Technical Training
• FAPESP's process: 21/06995-0 - Starling: security and resource allocation on B5G via artificial intelligence techniques
• Grantee: Daniel Macêdo Batista
• Support Opportunities: Regular Research Grants
• FAPESP's process: 22/06840-0 - The impact of the correlation of heterogeneous sources on botnets and DDoS prediction
• Grantee: Ligia Francielle Borges
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• FAPESP's process: 14/50937-1 - INCT 2014: on the Internet of the Future
• Grantee: Fabio Kon
• Support Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 15/24485-9 - Future internet for smart cities
• Grantee: Fabio Kon
• Support Opportunities: Research Projects - Thematic Grants