Malware is any type of program that is intended to wreak havoc to the computer system and network. Examples of malware are bot, ransomware, adware, keyloggers, viruses, trojan horses, worms and others. The exponential growth of malware is posing a great danger to the security of confidential information. The problem with many of the existing classification algorithms is their low performance in term of their ability to detect and prevent malware from infecting the computer system. There is an urgent need to evaluate the performance of the existing Machine Learning classification algorithms used for malware detection. This will help in creating more robust and efficient algorithms that have the capacity to overcome the weaknesses of the existing algorithms. This study did the performance evaluation of some classification algorithms such as J45, LMT, Naïve Bayes, Random Forest, MLP Classifier, Random Tree, REP Tree, Bagging, AdaBoost, KStar, SimpleLogistic, IBK, LWL, SVM, and RBF Network. The performance of the algorithms was evaluated in terms of Accuracy, Precision, Recall, Kappa Statistics, F-Measure, Matthew Correlation Coefficient, Receiver Operator Characteristics Area and Root Mean Squared Error using WEKA machine learning and data mining simulation tool. Our experimental results showed that Random Forest algorithm produced the best accuracy of 99.2%. This positively indicates that the Random Forest algorithm achieves good accuracy rates in detecting malware.