ISSN 0021-3454 (print version)
ISSN 2500-0381 (online version)
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vol 67 / February, 2024
Article

DOI 10.17586/0021-3454-2018-61-11-1005-1011

UDC 004.056

COMBINING HADOOP AND SNORT TECHNOLOGIES FOR DETECTION OF NETWORK ATTACKS

N. A. Komashinsky
St. Petersburg Institute for Informatics and Automation of the RAS, Laboratory of Cyber-Security Problems; Junior Scientist; Post-Graduate Student


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Abstract. A method of information processing on the base of Big Data technologies aimed at computer at-tacks detection is studied. The need to create specialized approaches and design methods that will im-prove the efficiency of processing the received information is justified. The possibilities and effectiveness assessments of parallel data processing with the purpose of computer influences detection using a functional approach, as well as the key principles of working with Big Data, are considered. The mathematical model by means of which the technique of intrusion detection is developed is presented. The principle of implementation of the tasks of information processing and anomaly detection based on integration of Hadoop, Snort platforms is described. Main results of the experimental evaluation of the method used to detect computer attacks are presented
Keywords: Big Data, Hadoop, information system, information security, computer attack, Snort, anomaly, data processing

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