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Network activity analysis of CryptoWall ransomware

Cabaj K., Gawkowski P., Grochowski K., Osojca D.

Przegląd Elektrotechniczny

2015

R. 91, nr 11

201-204

The paper presents the analysis of the CryptoWall ransomware network behaviour. In this approach a HoneyPot technology as well as the automatic run-time malware analytical system called Maltester were used. We present the practical results of the analyses, technologies and tools used, and the gained experience with dynamic analysis of ransomware software in a dedicated environment. Most of the data was collected with the use of the HoneyPot infrastructure created and deployed in the network of the Institute of Computer Science WUT.

Praca przedstawia analizę zachowania sieciowego złośliwego oprogramowania CryptoWall typu ransomware. W badaniach wykorzystano technologię HoneyPot oraz system automatycznej analizy działania złośliwego oprogramowania w czasie jego wykonywania o nazwie Maltester. Zaprezentowano zarówno uzyskane wyniki analiz, wykorzystane technologie i narzędzia jak i doświadczenia zebrane podczas analizy oprogramowania typu ransomware w autorskim środowisku analitycznym. Większość danych zebrana została z wykorzystaniem infrastruktury HoneyPot stworzonej i wdrożonej w sieci Instytutu Informatyki Politechniki Warszawskiej.

Comparative Study of Supervised Learning Methods for Malware Analysis

Kruczkowski M., Niewiadomska-Szynkiewicz E.

Journal of Telecommunications and Information Technology

2014

nr 4

24--33

Malware is a software designed to disrupt or even damage computer system or do other unwanted actions. Nowadays, malware is a common threat of the World Wide Web. Anti-malware protection and intrusion detection can be significantly supported by a comprehensive and extensive analysis of data on the Web. The aim of such analysis is a classification of the collected data into two sets, i.e., normal and malicious data. In this paper the authors investigate the use of three supervised learning methods for data mining to support the malware detection. The results of applications of Support Vector Machine, Naive Bayes and k-Nearest Neighbors techniques to classification of the data taken from devices located in many units, organizations and monitoring systems serviced by CERT Poland are described. The performance of all methods is compared and discussed. The results of performed experiments show that the supervised learning algorithms method can be successfully used to computer data analysis, and can support computer emergency response teams in threats detection.