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Distributed network attacks are often referred to as Distributed Denial of Service (DDoS) attacks. These attacks take advantage of specific limitations that apply to any arrangement asset, such as the framework of the authorized organization's site. In the existing research study, the author worked on an old KDD dataset. It is necessary to work with the latest dataset to identify the current state of DDoS attacks. This paper, used a machine learning approach for DDoS attack types classification and prediction. For this purpose, used Random Forest and XGBoost classification algorithms. To access the research proposed a complete framework for DDoS attacks prediction. For the proposed work, THE UNWS-np-15 dataset from GitHub and python used as a simulator. After applying the machine learning models generated a confusion matrix for model performance identification. In the first classification, the results showed that both Precision (PR) and Recall (RE) are 89% for Random Forest Algorithm. The average Accuracy (AC) of model is 89% which is extremely good. In the second classification, the results showed that both Precision (PR) and Recall (RE) are 90% for XGBoost. The average Accuracy (AC) of model is 90%. By comparing work to existing research work, the accuracy of defect determination improved as compare to existing research work which is 85% and 79%.

For decades, co-relating different data domains to attain the maximum potential of machines has driven research, especially in neural networks. Similarly, text and visual data (images and videos) are two distinct data domains with extensive research in the past. Recently, using natural language to process 2D or 3D images and videos with the immense power of neural nets has witnessed a promising future. Despite the diverse range of remarkable work in this field, notably in the past few years, rapid improvements have also solved future challenges for researchers. Moreover, the connection between these two domains is mainly subjected to GAN, thus limiting the horizons of this field. This review analyzes Text-to-Image (T2I) synthesis as a broader picture, Text-guided Visual-output (T2Vo), with the primary goal being to highlight the gaps by proposing a more comprehensive taxonomy. We broadly categorize text-guided visual output into three main divisions and meaningful subdivisions by critically examining an extensive body of literature from top-tier computer vision venues and closely related fields, such as machine learning and human–computer interaction, aiming at state-of-the-art models with a comparative analysis. This study successively follows previous surveys on T2I, adding value by analogously evaluating the diverse range of existing methods, including different generative models, several types of visual output, critical examination of various approaches, and highlighting the shortcomings, suggesting the future direction of research.

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The modeling of security threats is equally important as the modeling of functional requirements at the design stage of software engineering. However, unlike functional requirements modeling, the modeling of security threats is neglected, which consequently introduces software defects during the early stages of software engineering. Hence, there is a need to mitigate these threats at the design stage. Security threats, specifically authentication threats, crosscut other functional and non-functional requirements when modeled using the object-oriented paradigm. This not only makes the design complex but also results in tangling and scattering problems. We therefore model authentication threats using the aspect-oriented modeling (AOM) technique since it separates crosscutting concerns and localizes them as separate units called aspects. Our main research aim is to remove scattering and tangling in security threats modeling using all the core features of the aspect-oriented technique. In this paper, we propose a research approach to model security threats and their mitigation in mal sequence diagram. Using this approach, our contribution makes a clear difference from previous work. Our first contribution is the modeling of authentication threats in the mal sequence diagram using the security profile and AOM profile. Our second contribution is the mathematical verification of the aspect-oriented mal sequence woven model in terms of correctness and completeness. Using the proposed approach, the scattering and tangling from the resultant woven model are successfully removed at the design stage. Thus, the complexity of models and the time and effort required for future modifications of design models are reduced.