Speech corpus being the basic requirement for the development of Automatic speech recognition (ASR) system, it should be done with much accuracy in order to enhance the performance of the system. This paper describes the proposed procedure to abide while collecting the speech corpus of Swahili language from the native and non native speaker for the development of Automatic Speech Recognition system in Swahili language.

Increasing interest and advancement of internet and communication technologies have made network security rise as a vibrant research domain. Network intrusion detection systems (NIDSs) have developed as indispensable defense mechanisms in cybersecurity that are employed in discovery and prevention of malicious network activities. In the recent years, researchers have proposed deep learning approaches in the development of NIDSs owing to their ability to extract better representations from large corpus of data. In the literature, convolutional neural network architecture is extensively used for spatial feature learning, while the long short term memory networks are employed to learn temporal features. In this paper, a novel hybrid method that learn the discriminative spatial and temporal features from the network flow is proposed for detecting network intrusions. A two dimensional convolution neural network is proposed to intelligently extract the spatial characteristics whereas a bi-directional long short term memory is used to extract temporal features of network traffic data samples consequently, forming a deep hybrid neural network architecture for identification and classification of network intrusion samples. Extensive experimental evaluations were performed on two well-known benchmarks datasets: CIC-IDS 2017 and the NSL-KDD datasets. The proposed network model demonstrated state-of-the-art performance with experimental results showing that the accuracy and precision scores of the intrusion detection model are significantly better than those of other existing models. These results depicts the applicability of the proposed model in the spatial-temporal feature learning in network intrusion detection systems.

This paper provides an Extended Client Based Technique (ECBT) that performs classification on emails using the Bayessian classifier that attain in-depth defense by performing textual analysis on email messages and attachment extensions to detect and flag snooping emails. The technique was implemented using python 3.6 in a jupyter notebook. An experimental research method on a personal computer was used to validate the developed technique using different metrics. The validation results produced a high acceptable percentage rate based on the four calculated validation metrics indicating that the technique was valid. The cosine of similarity showed a high percentage rate of similarity between the validation labels indicating that there is a high rate of similarity between the known and output message labels. The direction for further study on this paper is to conduct a replica experiments, which enhances the classification and flagging of the snooped emails using an advanced classification method.

Speech has much capability as an interface between human and computer which comes under the Human Computer interaction (HCI). The major challenge has been the nature of voice is ever varying speech signal.The paper presents the development of the speech recognition system using Swahili speech database which was collected in three sets: digits, isolated words and sentences from both native and non native speakers of Swahili language.Different feature extraction techniques deployed in the system are: Linear Prediction Coding (LPC) and Mel-Frequency Coefficients (MFCC). We have used the 12 coefficient features from MFCC and 20 coefficients features from LPC. All these features extracted techniques are applied and tested for the own developed Swahili speech database.Recognition and verification were done using confusion matrix and Support Vector Machine (SVM) as a classifier for the classification purpose. LDA was tested for the entire dataset for the dimension reduction. LDA gave a good clustering. The performance of the system was checked on basis of their accuracy; Confusion with MFCC 50.9%, confusion with LPC 50.1%, the higher recognition rate in each data set were as follows numeric data: MFCC: 75%, LCP:72% , isolated word data: MFCC: 65.2% LPC: 66.67%, sentence data MFCC: 63.8%, LPC: 59.6.

Network Intrusion Detection Systems (NIDSs) have become standard security solutions that endeavours to discover unauthorized access to an organizational computer network by scrutinizing incoming and outgoing network traffic for signs of malicious activity. In recent years, deep learning based NIDSs have emerged as an active area of research in cybersecurity and several surveys have been done on these systems. Although a plethora of surveys exists covering this burgeoning body of research, there lacks in the literature an empirical analysis of the different hybrid deep learning models. This paper presents a review of hybrid deep learning models for network intrusion detection and pinpoints their characteristics which researchers and practitioners are exploiting to develop modern NIDSs. The paper first elucidates the concept of network intrusion detection systems. Secondly, the taxonomy of hybrid deep learning techniques employed in designing NIDSs is presented. Lastly, a survey of the hybrid deep learning based NIDS is presented. The study adopted the systematic literature review methodology, a formal and systematic procedure by conducting bibliographic review, while defining explicit protocols for obtaining information. The survey results suggest that hybrid deep learning-based models yield desirable performance compared to other deep learning algorithms. The results also indicate that optimization, empirical risk minimization and model complexity control are the most important characteristics in the design of hybrid deep learning-based models. Lastly, key issues in the literature exposed in the research survey are discussed and then propose several potential future directions for researchers and practitioners in the design of deep learning methods for network intrusion detection.