Quarterly journal published in SPbPU
and edited by prof. Dmitry Zegzhda
Peter the Great St. Petersburg Polytechnic University
Institute of computer sciences and technologies
information security of computer systems
Information Security Problems. Computer Systems
Published since 1999.
ISSN 2071-8217
DDOS ATTACKS DETECTION BASED ON A MODULAR NEURAL NETWORK
A. I. Sergadeeva, D. S. Lavrova Peter the Great St. Petersburg Polytechnic University (SPbPU)
Annotation: The paper proposes an approach to detection of Distributed Denial of Service (DDoS) attacks using a modular neural network, which is a series of connected neural networks that solve the problem step by step. The task of DDoS attack detection is decomposed into three interrelated subtasks: detection of anomalous network traffic, detection of DDoS attack traffic and identification of the type of realized DDoS attack, which is especially important due to the tendency of implementing multi-vector DDoS attacks. The results of experimental studies on the quality of performance of the constructed modular neural network confirmed the effectiveness of the proposed approach.
Keywords: DDoS attacks, modular neural network, decomposition, machine learning.
Pages 111-118