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
MALWARE DETECTION APPROACH BASED ON THE DETECTION OF ABNORMAL NETWORK TRAFFIC USING MACHINE LEARNING ALGORITHMS

A. A. Kriulin, M. A. Eremeev, V. S. Nefedov
Russian Technological University — MIREA

Annotation: The article considers a possibility of using machine learning technologies to detect network connections of malicious programs based on the detection of anomalies. The classification of network connections of malicious software is carried out based on statistical signs during data transmission that occur at the transport and network levels of the OSI model. It is proposed to use machine learning technologies to assess the probability of detecting malware based on their network activity.
Keywords: Machine Learning Algorithms, Malware, Intrusion Detection Tools, Network Activity.
Pages 27-33