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Identification and characterization of malicious behavior in behavioral graphs
 Varga, Adam ; Burget, Radim (oponent) ; Hajný, Jan (vedoucí práce)
In recent years, there has been an increase in work involving comprehensive malware detection. It is often useful to use a graph format to capture behavior. This is the case with the Avast antivirus program, whose behavioral shield detects malicious behavior and stores it in the form of graphs. Since this is a proprietary solution and Avast antivirus works with its own set of characterized behavior, it was necessary to design our own detection method that will be built on top of these behavioral graphs. This work analyzes graphs of malware behavior captured by the behavioral shield of the Avast antivirus program for the process of deeper detection of malware. Detection of malicious behavior begins with the analysis and abstraction of patterns from the behavioral graph. Isolated patterns can more effectively identify dynamically changing malware. Behavior graphs are stored in the Neo4j graph database and thousands of them are captured every day. The aim of this work was to design an algorithm to identify the behavior of malicious software with emphasis on tagging speed and uniqueness of identified patterns of behavior. Identification of malicious behavior consists in finding the most important properties of trained classifiers and subsequent extraction of a subgraph consisting only of these important properties of nodes and the relationships between them. Subsequently, a rule for the evaluation of the extracted subgraph is proposed. The diploma thesis took place in cooperation with Avast Software s.r.o.
Úplný záznam
Identification and characterization of malicious behavior in behavioral graphs
 Varga, Adam ; Burget, Radim (oponent) ; Hajný, Jan (vedoucí práce)
In recent years, there has been an increase in work involving comprehensive malware detection. It is often useful to use a graph format to capture behavior. This is the case with the Avast antivirus program, whose behavioral shield detects malicious behavior and stores it in the form of graphs. Since this is a proprietary solution and Avast antivirus works with its own set of characterized behavior, it was necessary to design our own detection method that will be built on top of these behavioral graphs. This work analyzes graphs of malware behavior captured by the behavioral shield of the Avast antivirus program for the process of deeper detection of malware. Detection of malicious behavior begins with the analysis and abstraction of patterns from the behavioral graph. Isolated patterns can more effectively identify dynamically changing malware. Behavior graphs are stored in the Neo4j graph database and thousands of them are captured every day. The aim of this work was to design an algorithm to identify the behavior of malicious software with emphasis on tagging speed and uniqueness of identified patterns of behavior. Identification of malicious behavior consists in finding the most important properties of trained classifiers and subsequent extraction of a subgraph consisting only of these important properties of nodes and the relationships between them. Subsequently, a rule for the evaluation of the extracted subgraph is proposed. The diploma thesis took place in cooperation with Avast Software s.r.o.
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