Distributed denial of service (DDoS) attacks are one of the most common global challenges faced by service providers on the web. It leads to network disturbances, interruption of communication and significant damage to services. Researchers seek to develop intelligent algorithms to detect and prevent DDoS attacks. The present study proposes an efficient DDoS attack detection model. This model relies mainly on dimensionality reduction and machine learning algorithms. The principal component analysis (PCA) and the linear discriminant analysis (LDA) techniques perform the dimensionality reduction in individual and hybrid modes to process and improve the data. Subsequently, DDoS attack detection is performed based on random forest (RF) and decision tree (DT) algorithms. The model is implemented and tested on the CICDDoS2019 dataset using different data dimensionality reduction test scenarios. The results show that using dimensionality reduction techniques along with the ML algorithms with a dataset containing high-dimensional data significantly improves the classification results. The best accuracy result of 99.97% is obtained when the model operates in a hybrid mode based on a combination of PCA, LDA and RF algorithms, and the data reduction parameter equals 40.

The web speech API has made it possible to integrate audio data into web applications and make it a unique experience for all customers and users of modern applications. The website can only be accessed through devices equipped with a which stands for graphical user interface (GUI) and screen. For this to be done, there must be a physical attraction with such devices. This paper presents speech recognition using a web browser (SRWB) which permits browsing or surfing the internet with the use of a standard voice-only and vocal user interface (VUL) development. The SRWB system input from the users in form of vocal commands and covers these voice commands to HTTP requests. The SRWB system will send the voice commands to the web server for processing purposes and when the processing is done, the converted or translated HTTP response is outputted to the end-users in a voice format made audible with the attached loudspeakers. SAPI, developed by Microsoft, allows the use of SRWB in Windows applications. The algorithm is implemented by the system to achieve its goal for web content, classifying, analyzing, and sending important parts of web pages back to the end-user.