Comparative analysis of various machine learning algorithms for ransomware detection

Recently, the ransomware attack posed a serious threat that targets a wide range of organizations and individuals for financial gain. So, there is a real need to initiate more innovative methods that are capable of proactively detect and prevent this type of attack. Multiple approaches were innovated to detect attacks using different techniques. One of these techniques is machine learning techniques which provide reasonable results, in most attack detection systems. In the current article, different machine learning techniques are tested to analyze its ability in a detection ransomware attack. The top 1000 features extracted from raw byte with the use of gain ratio as a feature selection method. Three different classifiers (decision tree (J48), random forest, radial basis function (RBF) network) available in Waikato Environment for Knowledge Analysis (WEKA) based machine learning tool are evaluated to achieve significant detection accuracy of ransomware. The result shows that random forest gave the best detection accuracy almost around 98%

Hybrid approach for spam email detection

On this era, email is a convenient way to enable the user to communicate everywhere in the world which it has the internet. It is because of the economic and fast method of communication. The email message can send to the single user or distribute to the group. Majority of the users does not know the life exclusive of e-mail. For this issue, it becomes an email as the medium of communication of a malicious person. This project aimed at Spam Email. This project concentrated on a hybrid approach namely Neural Network (NN) and Particle Swarm Optimization (PSO) designed to detect the spam emails. The comparisons between the hybrid approach for NN_PSO with GA algorithm and NN classifiers to show the best performance for spam detection. The Spambase used contains 1813 as spams (39.40%) and 2788 as non-spam (60.6%) implemented on these algorithms. The comparisons performance criteria based on accuracy, false positive, false negative, precision, recall and f-measure. The feature selection used by applying GA algorithm to reducing the redundant and irrelevant features. The performance of F-Measure shows that the hybrid NN_PSO, GA_NN and NN are 94.10%, 92.60% and 91.39% respectively. The results recommended using the hybrid of NN_PSO with GA algorithm for the best performance for spam email detection