COMBINING HADOOP AND SNORT TECHNOLOGIES FOR DETECTION OF NETWORK ATTACKS
Annotation
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
Постоянный URL
Articles in current issue
- MATHEMATICAL AND SOFTWARE FOR SYNTHESIS OF TECHNOLOGIES AND SCHEDULES OF CYBER-PHYSICAL SYSTEMS
- MODELS AND PROGRAM COMPLEX FOR SOLVING PLANNING PROBLEMS OF MEASURING AND COMPUTING OPERATIONS IN CYBER-PHYSICAL SYSTEMS
- THE PROBLEM OF KNOWLEDGE RETRIEVING WITH THE USE OF PRECEDENT-BASED REASONING
- INTELLECTUAL COMPLEX FOR AUTOMATED DESIGN OF INFORMATION AND ANALYTICAL SYSTEMS SUPPORT OF COMPLEX OBJECTS LIFE CYCLE
- MODEL-ORIENTED APPROACH TO DESIGNING USER INTERFACES FOR INTELLIGENT SYSTEMS
- CLASSIFICATION OF IMAGE SEGMENTATION ALGORITHMS
- SOFTWARE TOOLS FOR COMPLEX MODELING IN MONITORING AND FORECASTING OF EMERGENCIES USING THE EARTH REMOTE SENSING DATA
- ANALYSIS OF SECURITY EVENTS PROPERTIES FOR DETECTION OF INFORMATION OBJECTS AND THEIR TYPES IN UNCERTAIN INFRASTRUCTURES
- COMBINING HADOOP AND SNORT TECHNOLOGIES FOR DETECTION OF NETWORK ATTACKS