The recent advances of e-commerce and e-payment systems have sparked an increase in financial fraud cases such as credit card fraud. It is therefore crucial to implement mechanisms that can detect the credit card fraud. Features of credit card frauds play important role when machine learning is used for credit card fraud detection, and they must be chosen properly. This paper proposes a machine learning (ML) based credit card fraud detection engine using ML classifiers: Decision Tree (DT), Logistic Regression (LR), Artificial Neural Network (ANN). To validate the performance, the proposed credit card fraud detection engine is evaluated using a dataset generated from European cardholders. The result demonstrated that our proposed approach outperforms existing systems.

No abstract

Development of the internet causes a major problem to the privacy and security of an organization and to personal systems. Security communities receive the huge number of malware every day, Categorization of malware to their corresponding families based on their behaviour is a complex task is to the computer security community. Traditional antivirus systems based on the signature extraction procedures fail to classify the new malware. Therefore we propose a machine learning model to classify the malware to their corresponding families using the properties of the malware. In this paper, we present a Review of Mansour Ahmadi et al.'s Feature fusion for effective Malware Family Classification system, Liu et al.'s Automatic Malware classification and detection system, Bashari et al.'s Malware classification and detection system using ANN. Ashu Sharma et al.'s Classification of advanced Malware system. Finally, we have done a comparative analysis of all the abovementioned methods.