Nowadays, cloud computing provides easy access to a set of variable and configurable computing resources based on user demand through the network. Cloud computing services are available through common internet protocols and network standards. In addition to the unique benefits of cloud computing, insecure communication and attacks on cloud networks cannot be ignored. There are several techniques for dealing with network attacks. To this end, network anomaly detection systems are widely used as an effective countermeasure against network anomalies. The anomaly-based approach generally learns normal traffic patterns in various ways and identifies patterns of anomalies. Network anomaly detection systems have gained much attention in intelligently monitoring network traffic using machine learning methods. This paper presents an efficient model based on autoencoders for anomaly detection in cloud computing networks. The autoencoder learns a basic representation of the normal data and its reconstruction with minimum error. Therefore, the reconstruction error is used as an anomaly or classification metric. In addition, to detecting anomaly data from normal data, the classification of anomaly types has also been investigated. We have proposed a new approach by examining an autoencoder’s anomaly detection method based on data reconstruction error. Unlike the existing autoencoder-based anomaly detection techniques that consider the reconstruction error of all input features as a single value, we assume that the reconstruction error is a vector. This enables our model to use the reconstruction error of every input feature as an anomaly or classification metric. We further propose a multi-class classification structure to classify the anomalies. We use the CIDDS-001 dataset as a commonly accepted dataset in the literature. Our evaluations show that the performance of the proposed method has improved considerably compared to the existing ones in terms of accuracy, recall, false-positive rate, and F1-score metrics.

As technology advanced and e-commerce services expanded, credit cards became one of the most popular payment methods, resulting in an increase in the volume of banking transactions. Furthermore, the significant increase in fraud requires high banking transaction costs. As a result, detecting fraudulent activities has become a fascinating topic. In this study, we consider the use of class weight-tuning hyperparameters to control the weight of fraudulent and legitimate transactions. We use Bayesian optimization in particular to optimize the hyperparameters while preserving practical issues such as unbalanced data. We propose weight-tuning as a pre-process for unbalanced data, as well as CatBoost and XGBoost to improve the performance of the LightGBM method by accounting for the voting mechanism. Finally, in order to improve performance even further, we use deep learning to fine-tune the hyperparameters, particularly our proposed weight-tuning one. We perform some experiments on real-world data to test the proposed methods. To better cover unbalanced datasets, we use recall-precision metrics in addition to the standard ROC-AUC. CatBoost, LightGBM, and XGBoost are evaluated separately using a 5-fold cross-validation method. Furthermore, the majority voting ensemble learning method is used to assess the performance of the combined algorithms. LightGBM and XGBoost achieve the best level criteria of ROC-AUC = 0.95, precision 0.79, recall 0.80, F1 score 0.79, and MCC 0.79, according to the results. By using deep learning and the Bayesian optimization method to tune the hyperparameters, we also meet the ROC-AUC = 0.94, precision = 0.80, recall = 0.82, F1 score = 0.81, and MCC = 0.81. This is a significant improvement over the cutting-edge methods we compared it to.

The book reviews core concepts of machine learning (ML) while focusing on modern applications. It is aimed at those who want to advance their understanding of ML by providing technical and practical insights. It does not use complicated mathematics to explain how to benefit from ML algorithms. Unlike the existing literature, this work provides the core concepts with emphasis on fresh ideas and real application scenarios. It starts with the basic concepts of ML and extends the concepts to the different deep learning algorithms. The book provides an introduction and main elements of evaluation tools with Python and walks you through the recent applications of ML in self-driving cars, cognitive decision making, communication networks, security, and signal processing. The concept of generative networks is also presented and focuses on GANs as a tool to improve the performance of existing algorithms. In summary, this book provides a comprehensive technological path from fundamental theories to the categorization of existing algorithms, covers state-of-the-art, practical evaluation tools and methods to empower you to use synthetic data to improve the performance of applications.