The internet plays an important role in all areas of society from the economy to the government. Security means permitting things you do want, while preventing things you don’t want from performing. Computer security means storing the information on a computer in a secure manner. Nowadays Computer Security is pretentious by malicious data. Malware represents a serious threat to the security of computer systems. Traditional malware detection techniques like signature-based, heuristic-based, Specification-based detection are used to detect the known malware. These techniques detect the known malware accurately, but unable to detect the new, unknown malware. This paper presents a malware detection system based on the data mining and machine learning technique. The proposed method consists of disassemble process, feature extraction process and feature selection process. Three classification algorithms are employed on dataset to generate and train the classifiers named as Ripper, C4.5, IBk. The ensemble method i.e. Voting method is used to improve the accuracy of results. Here majority voting and veto voting are implemented the expected output is decided on the basis of majority and veto voting. The decision strategy of veto is improved by introducing the trust-based veto voting. This paper efficiently compared the results of majority voting, veto voting and trust-based veto voting.