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A Multi-classifier Network-based Crypto Ransomware Detection System: A Case Study Of Locky Ransomware

A MULTI-CLASSIFIER NETWORK-BASED CRYPTO RANSOMWARE DETECTION SYSTEM: A CASE STUDY OF LOCKY RANSOMWARE

  • Project Code :
  • TCREJA19_102
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A MULTI-CLASSIFIER NETWORK-BASED CRYPTO RANSOMWARE DETECTION SYSTEM: A CASE STUDY OF LOCKY RANSOMWARE

A Multi-Classifier Network-based Crypto Ransomware Detection System a Case study of Locky Ransom ware

Abstract

Ransomware is a type of malicious software, or malware, designed to deny access to a computer system or data until a ransom is paid. Ransomware typically spreads through phishing emails or by unknowingly visiting an infected website. Ransomware can be devastating to an individual or an organization. Modern host-based detection methods need to first infect the host, detect anomalies and detect malware. By the time of infection, it will be too late as some of the system's assets have already been eradicated or encrypted by malware. In contrast, network-based methods are effective in detecting ransomware attacks, as most ransomware families try to connect to the command and control servers before executing their malicious payloads. Therefore, careful analysis of ransomware network traffic is one of the key ways to identify early. This paper presents a comprehensive behavioral analysis of crypto ransomware network operations, taking Lackey as a case study of one of the most extreme families. A dedicated testbed is built and categorized into multiple types, capturing a set of valuable and informative network features. A network-based intrusion detection system is implemented, using two independent classifiers that work in parallel at different levels: packet and flow levels. Experimental evaluation of the proposed detection system demonstrates that it provides high detection accuracy, low false positive rate; valid capture features and is highly effective in tracking ransomware network operations.

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