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Crypto-ransomware remains a significant threat to governments and companiesalike, with high-profile cyber security incidents regularly making headlines.Many different detection systems have been proposed as solutions to theever-changing dynamic landscape of ransomware detection. In the majority ofcases, these described systems propose a method based on the result of a singletest performed on either the executable code, the process under investigation,its behaviour, or its output. In a small subset of ransomware detectionsystems, the concept of a scorecard is employed where multiple tests areperformed on various aspects of a process under investigation and their resultsare then analysed using machine learning. The purpose of this paper is topropose a new majority voting approach to ransomware detection by developing amethod that uses a cumulative score derived from discrete tests based oncalculations using algorithmic rather than heuristic techniques. The paperdescribes 23 candidate tests, as well as 9 Windows API tests which arevalidated to determine both their accuracy and viability for use within aransomware detection system. Using a cumulative score calculation approach toransomware detection has several benefits, such as the immunity to theoccasional inaccuracy of individual tests when making its final classification.The system can also leverage multiple tests that can be both comprehensive andcomplimentary in an attempt to achieve a broader, deeper, and more robustanalysis of the program under investigation. Additionally, the use of multiplecollaborative tests also significantly hinders ransomware from masking ormodifying its behaviour in an attempt to bypass detection.

Majority Voting Approach to Ransomware Detection