Know abnormal, find evil : frequent pattern mining for ransomware threat hunting and intelligence

Homayoun, S, Dehghantanha, A ORCID:, Ahmadzadeh, M, Hashemi, S and Khayami, R 2020, 'Know abnormal, find evil : frequent pattern mining for ransomware threat hunting and intelligence' , IEEE Transactions on Emerging Topics in Computing, 8 (2) , pp. 341-351.

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Emergence of crypto-ransomware has significantly changed the cyber threat landscape. A crypto ransomware removes data custodian access by encrypting valuable data on victims’ computers and requests a ransom payment to reinstantiate custodian access by decrypting data. Timely detection of ransomware very much depends on how quickly and accurately system logs can be mined to hunt abnormalities and stop the evil. In this paper we first setup an environment to collect activity logs of 517 Locky ransomware samples, 535 Cerber ransomware samples and 572 samples of TeslaCrypt ransomware. We utilize Sequential Pattern Mining to find Maximal Frequent Patterns (MFP) of activities within different ransomware families as candidate features for classification using J48, Random Forest, Bagging and MLP algorithms. We could achieve 99% accuracy in detecting ransomware instances from goodware samples and 96.5% accuracy in detecting family of a given ransomware sample. Our results indicate usefulness and practicality of applying pattern mining techniques in detection of good features for ransomware hunting. Moreover, we showed existence of distinctive frequent patterns within different ransomware families which can be used for identification of a ransomware sample family for building intelligence about threat actors and threat profile of a given target.

Item Type: Article
Schools: Schools > School of Computing, Science and Engineering > Salford Innovation Research Centre
Journal or Publication Title: IEEE Transactions on Emerging Topics in Computing
Publisher: IEEE
ISSN: 2168-6750
Related URLs:
Funders: European Council 268 International Incoming Fellowship
Depositing User: Dr. Ali Dehghantanha
Date Deposited: 24 Nov 2017 10:36
Last Modified: 15 Feb 2022 22:41

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