Wireless Sensor Networks (WSN) have become increasingly one of the hottest research areas in computer science due to their wide range of applications including critical military and civilian applications. Such applications have created various security threats, especially in unattended environments. To ensure the security and dependability of WSN services, an Intrusion Detection System (IDS) should be in place. This IDS has to be compatible with the characteristics of WSNs and capable of detecting the largest possible number of security threats. In this paper a specialized dataset for WSN is developed to help better detect and classify four types of Denial of Service (DoS) attacks: Blackhole, Grayhole, Flooding, and Scheduling attacks. This paper considers the use of LEACH protocol which is one of the most popular hierarchical routing protocols in WSNs. A scheme has been defined to collect data from Network Simulator 2 (NS-2) and then processed to produce 23 features. The collected dataset is called WSN-DS. Artificial Neural Network (ANN) has been trained on the dataset to detect and classify different DoS attacks. The results show that WSN-DS improved the ability of IDS to achieve higher classification accuracy rate. WEKA toolbox was used with holdout and 10-Fold Cross Validation methods. The best results were achieved with 10-Fold Cross Validation with one hidden layer. The classification accuracies of attacks were 92.8%, 99.4%, 92.2%, 75.6%, and 99.8% for Blackhole, Flooding, Scheduling, and Grayhole attacks, in addition to the normal case (without attacks), respectively.

Nowadays, credit card fraud has emerged as a major problem. People are becoming increasingly using credit cards to pay for their transactions, it has become more popular and essential in our lives. Fraudsters are developing new strategies and techniques over time, and it is not easy for humans to manually check out all transactions. The cost of fraudulent transactions is significant and without prevention mechanisms it is rising. Finding the best methodology to detect fraudulent transactions is a crucial asset to the industry to reduce the fraud financial loss. Artificial neural networks (ANN) technique is considered as one of the effective techniques that has proved its efficiency in detecting credit card fraud transactions with high precision and minimum cost. In this paper, we propose a multilayer perceptron (MLP) ANN-based model solution to improve the accuracy of the detection process. The performance of the methodology is measured based on the precision, sensitivity, specificity, accuracy, F-measure, area under curve (AUC) and root mean square error (RMSE). Moreover, we illustrate the performance results of these measures with a descriptive analysis. Experimental results have shown that the proposed ANN-based model is efficient and does improve the accuracy of the detection of fraudulent transactions.

Recent developments in e-payment systems have led to increased financial fraud, such as credit card fraud. It is therefore essential to implement detection mechanisms for credit card fraud. However, due to the unbalanced class distribution in the credit card dataset, the machine learning techniques are used to train the model based on the majority class, resulting in inaccurate fraud predictions. Therefore, this paper mainly focuses on processing unbalanced data using the oversampling technique called Elbow Fuzzy Noice filtering SMOTE (EFN-SMOTE). This method divides the dataset into multiple clusters. The number of clusters is determined by an algorithm known as the Elbow method, after which noise filtering is applied to each cluster, after that, we use the SMOTE in each cluster to synthesize a new minority instance based on the nearest majority instance of each minority instance to effectively perceive the decision boundary which leads to a balanced database by oversam-pling technique. On the other hand, the result show that EFN-SMOTE achieved better classification performance using Artificial Neural Network (ANN) with four hidden layers with 0.999 accuracy, 0.998 precision, 0.999 sensitivity, 0.998 specificity, 0.999 F-measure, and 0.999 G-Mean.