Abstract
Currently, different forms of ransomware are increasingly threatening users. Modern ransomware encrypts important user data and it is only possible to recover it once a ransom has been paid [14]. In this paper, we classify ransomware in 10 classes which are labeled using avclass tool. In this classification, we based on artificial neural networks with multilayer perceptron function. To do this, it was necessary to build the learning base based on ransomware files. We then implemented programs in java allowing the extraction of the key strings from ransomwares files intended for the learning stage and for the test one. Once the learning and testing databases have been prepared, we started the classification with the weka tool. The objective of this contribution is to investigate if the neural networks are an effective means for the classification of this kind of ransomwares or it will be necessary to think to another method of classification.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Clonezilla: The Free and Open Source Software for DiskImaging and Cloning. http://clonezilla.org/
Viper: Binary Management and Analysis Framework. http://viper.li/
Cuckoo Foundation: Cuckoo Sandbox: Automated Malware Analysis. cuckoosandbox.org
Digital Corpora: Producing the Digital Body. http://digitalcorpora.org/
Mbol, F., Robert, J.M., Sadighian, A.: An efficient approach to detect torrent-locker ransomware in computer systems. In: International Conference on Cryptology and Network Security, pp. 532–541. Springer (2016)
Jain, A.K.: Data clustering: 50 years beyond K-means. Pattern Recogn. Lett. 31, 651–666 (2010)
Palisse, A., Durand, A., Le Bouder, H., Le Guernic, C., Lanet, J.-L.: Data Aware Defense (DaD): towards a generic and practical ransomware countermeasure. In: NordSec2017 - Nordic Conference on Secure IT Systems, Tartu, Estonia, 8–10 November 2017
Malware Online Repository (2018). https://malwr.com
Malware Online Repository (2018). http://malwaredb.malekal.com
Malware Online Repository (2018). https://virusshare.com
Sebastián, M., Rivera, R., Kotzias, P., Caballero, J.: Avclass: a tool for massive malware labeling. In: International Symposium on Research in Attacks, Intrusions, and Defenses, pp. 230–253. Springer (2016)
Swasti, S., Monika, J.: A study on WEKA tool for data preprocessing, classification and clustering. Int. J. Innov. Technol. Explor. Eng. (IJITEE) 2(6) (2013). ISSN 2278–3075
Marc, P.: Réseaux de Neurones (Le perceptron multicouche et son algorithme de retropropagation des erreurs), Laval University, Laval, 272 p. (2004)
Krzysztof, C., Wojciech, M.: Using Software-defined networking for ransomware mitigation: the case of CryptoWall. IEEE Netw. 30(6), 14–20 (2016)
Joshua S., Konstantin B.: Deep neural network based malware detection using two dimensional binary program features. In: The 10th International Conference on Malicious and Unwanted Software (MALWARE). IEEE Xplore (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Ouerdi, N., Hajji, T., Palisse, A., Lanet, JL., Azizi, A. (2019). Classification of Ransomware Based on Artificial Neural Networks. In: Rocha, Á., Serrhini, M. (eds) Information Systems and Technologies to Support Learning. EMENA-ISTL 2018. Smart Innovation, Systems and Technologies, vol 111. Springer, Cham. https://doi.org/10.1007/978-3-030-03577-8_43
Download citation
DOI: https://doi.org/10.1007/978-3-030-03577-8_43
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-03576-1
Online ISBN: 978-3-030-03577-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)