Over the past decades, a tremendous amount of research has been done on the use of machine learning for speech processing applications, especially speech recognition. However, in the past few years, research has focused on utilizing deep learning for speech-related applications. This new area of machine learning has yielded far better results when compared to others in a variety of applications including speech, and thus became a very attractive area of research. This paper provides a thorough examination of the different studies that have been conducted since 2006, when deep learning first arose as a new area of machine learning, for speech applications. A thorough statistical analysis is provided in this review which was conducted by extracting specific information from 174 papers published between the years 2006 and 2018. The results provided in this paper shed light on the trends of research in this area as well as bring focus to new research topics.

Anomaly detection has been used for decades to identify and extract anomalous components from data. Many techniques have been used to detect anomalies. One of the increasingly significant techniques is Machine Learning (ML), which plays an important role in this area. In this research paper, we conduct a Systematic Literature Review (SLR) which analyzes ML models that detect anomalies in their application. Our review analyzes the models from four perspectives; the applications of anomaly detection, ML techniques, performance metrics for ML models, and the classification of anomaly detection. In our review, we have identified 290 research articles, written from 2000-2020, that discuss ML techniques for anomaly detection. After analyzing the selected research articles, we present 43 different applications of anomaly detection found in the selected research articles. Moreover, we identify 29 distinct ML models used in the identification of anomalies. Finally, we present 22 different datasets that are applied in experiments on anomaly detection, as well as many other general datasets. In addition, we observe that unsupervised anomaly detection has been adopted by researchers more than other classification anomaly detection systems. Detection of anomalies using ML models is a promising area of research, and there are a lot of ML models that have been implemented by researchers. Therefore, we provide researchers with recommendations and guidelines based on this review.

Blockchain is a key technology that has the potential to decentralize the way we store, share, and manage information and data. One of the more recent blockchain platforms that has emerged is Hyperledger Fabric, an open source, permissioned blockchain that was introduced by IBM, first as Hyperledger Fabric v0.6, and then more recently, in 2017, IBM released Hyperledger Fabric v1.0. Although there are many blockchain platforms, there is no clear methodology for evaluating and assessing the different blockchain platforms in terms of their various aspects, such as performance, security, and scalability. In addition, the new version of Hyperledger Fabric was never evaluated against any other blockchain platform. In this paper, we will first conduct a performance analysis of the two versions of Hyperledger Fabric, v0.6 and v1.0. The performance evaluation of the two platforms will be assessed in terms of execution time, latency, and throughput, by varying the workload in each platform up to 10,000 transactions. Second, we will analyze the scalability of the two platforms by varying the number of nodes up to 20 nodes in each platform. Overall, the performance analysis results across all evaluation metrics, scalability, throughput, execution time, and latency, demonstrate that Hyperledger Fabric v1.0 consistently outperforms Hyperledger Fabric v0.6. However, Hyperledger Fabric v1.0 platform performance did not reach the performance level in current traditional database systems under high workload scenarios.

Cyber-security garnered significant attention due to the increased dependency of individuals and organizations on the Internet and their concern about the security and privacy of their online activities. Several previous machine learning (ML)-based network intrusion detection systems (NIDSs) have been developed to protect against malicious online behavior. This paper proposes a novel multi-stage optimized ML-based NIDS framework that reduces computational complexity while maintaining its detection performance. This work studies the impact of oversampling techniques on the models’ training sample size and determines the minimal suitable training sample size. Furthermore, it compares between two feature selection techniques, information gain and correlation-based, and explores their effect on detection performance and time complexity. Moreover, different ML hyper-parameter optimization techniques are investigated to enhance the NIDS’s performance. The performance of the proposed framework is evaluated using two recent intrusion detection datasets, the CICIDS 2017 and the UNSW-NB 2015 datasets. Experimental results show that the proposed model significantly reduces the required training sample size (up to 74%) and feature set size (up to 50%). Moreover, the model performance is enhanced with hyper-parameter optimization with detection accuracies over 99% for both datasets, outperforming recent literature works by 1-2% higher accuracy and 1-2% lower false alarm rate.