Quarterly journal published in SPbPU
and edited by prof. Peter 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
ADVERSARIAL ATTACKS ON INTRUSION DETECTION SYSTEMS USING LSTM CLASSIFIER

Kulikov D.A., Platonov V.V.

Annotation: This article discusses adversarial attacks on machine learning models and their classification. Methods for assessing the resistance of an LSTM classifier to adversarial attacks are investigated. JSMA and FGSM attacks, chosen due to the portability of adversarial examples between machine learning models, are discussed in detail. An attack of "poisoning" of the LSTM classifier is proposed. Methods of protection against the considered adversarial attacks are formulated.
Keywords: adversarial attack, intrusion detection system, neural network, LSTM.
Pages 48-56