Distributed network attacks are referred to, usually, 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 was extracted from the GitHub repository and Python was used as a simulator. After applying the machine learning models, we generated a confusion matrix for identification of the model performance. In the first classification, the results showed that both Precision (PR) and Recall (RE) are $\sim 89$ % for the Random Forest algorithm. The average Accuracy (AC) of our proposed model is ~89% which is superb and enough good. In the second classification, the results showed that both Precision (PR) and Recall (RE) are approximately 90% for the XGBoost algorithm. The average Accuracy (AC) of our suggested model is ~90%. By comparing our work to the existing research works, the accuracy of the defect determination was significantly improved which is approximately 85% and 79%, respectively.

Artificial intelligence (AI) is one of the key technologies emerging in the Industrial Revolution that could protect against cybersecurity threats. AI is a key component of big data analytics and enables accurate real-time data analysis. AI can analyze big data, but it has some issues with security, privacy, and centralization of data. Moreover, cybercriminals continue to advance, so law enforcement faces more threats. With traditional cybersecurity solutions, sophisticated cyber-attacks are harder to detect and defend against. In complex cyberspaces, AI algorithms mine valuable features from data. However, the data on the Internet is scattered and controlled by different parties, making it challenging to authorize and validate its use. The AICyber-Chain model is presented in this paper for securely storing, calculating, and distributing data on the Internet at an enterprise scale. In a large-scale Internet environment, our proposed AICyber-Chain model integrates three key components to ensure a more secure cyberspace, enhancing AI, namely: Firstly, blockchain-based data sharing guarantees ownership at a large scale, enabling real-time data sharing. Secondly, a platform powered by AI makes cyberspace more trustworthy. Thirdly, sharing data or services rewards participants financially, which promotes sharing. We also discuss a typical use scenario, an alternative deployment method, and its security and commercial efficacy. Also, we simulated our model on Ethereum’s official test network, called Rinkeby, to demonstrate its practicality and efficiency. This model speeds up authentication by 1.8 times compared to the centralized model. In addition, our proposed solution reduces gas consumption by 20 to 25%. Our paper aims to serve as a guide and reference point for cybersecurity researchers and industry practitioners, especially from an intelligent computing or AI-based technical standpoint.

The rapid growth of Consumer Internet of Things (CIoT) devices has significantly increased real-time multimedia data exchange, heightening vulnerability to attacks targeting audio, video, and image content. This paper introduces Multimedia Anomaly and Integrity Detection using Knowledge Distillation (MAID-KD), a lightweight multi-task framework that performs anomaly detection and integrity verification in CIoT environments. MAID-KD leverages a Transformer-based teacher model to extract rich spatio-temporal features from multimedia streams, while a compact CNN-LSTM student model optimized for edge deployment is trained through feature alignment, soft-target distillation, and variational projection. Experimental results demonstrate that MAID-KD achieves superior accuracy and F1-score compared to state-of-the-art baselines, while reducing model size and inference latency by over 60%. These results highlight MAID-KD’s ability to deliver scalable, privacy-preserving, and multimedia-aware security for CIoT devices such as smart surveillance systems, health-monitoring wearables, and connected home platforms.

Over the last two decades, the rapid expansion of the Internet has prompted a growing number of enterprises to deploy their work globally. Companies are increasingly reliant on software systems, which need ongoing modification, maintenance, and upgrades. The maintenance phase consumes approximately 80% of the total software budget. Therefore, these businesses need to efficiently manage their software maintenance efforts and expenses. Hence, companies have been eagerly looking for offshore outsourcing of these software systems. Choosing the best sourcing model for software maintenance projects remains elusive and challenging due to a variety of technological, social, and political factors. This study aims to analyze application maintenance offshoring related factors and addresses its decision-making process. To achieve the study objectives, factors of two datasets are analyzed based on standard deviation, mean and mean error. The Critical Success Factors (CSFs) are examined thoroughly to explore their impact on decision-making process. Additionally, the study proposes a sourcing framework based on CSFs that uses the Human Computer Interaction (HCI) principles. This framework assists clients and vendors to evaluate the projects prior to offshoring decisions. To enhance decision-making process, a case study is conducted in the IT industry and the Simple Multi Attribute Rating Technique (SMART) is applied. As the results show, SMART ranks the available options and helps in making effective offshoring decisions.

This paper deals with the examination effect of Dufour and Soret Effect on MHD heat and mass transfer with radiative heat flux in a liquid over a rotating dick. The problem is solved by HAM (Homotopy Analysis Method). The arrangement of Non-Linear Partial Differential Equations (PDEs) which administer the stream, heat and mass exchange qualities is changed into Ordinary Differential Equations (ODEs). It is discovered from the current exploration that Dufour and radiation impacts cause diminishments in the liquid temperature. The effect of suction diminishes the speed, temperature, what’s more, focus profiles essentially in the limit layer.

This article deals with the time dependent analysis of thermally conducting and Magneto-hydrodynamic (MHD) liquid film flow of a fourth order fluid past a vertical and vibratory plate. In this article have been developed for higher order complex nature fluids. The governing-equations have been modeled in the terms of nonlinear partial differential equations with the help of physical boundary circumstances. Two different analytical approaches i.e. Adomian decomposition method (ADM) and the optimal homotopy asymptotic method (OHAM), have been used for discoveryof the series clarification of the problems. Solutions obtained via two diversemethods have been compared using the graphs, tables and found an excellent contract. Variants of the embedded flow parameters in the solution have been analysed through the graphical diagrams.