American scientific publishing group

As cyber threats evolve in complexity and scale, traditional detection mechanisms are increasingly inadequate. This paper presents a comprehensive survey of artificial intelligence (AI) applications in malware and network intrusion detection, emphasizing the integration of intelligent techniques for real-time threat mitigation. We categorize state-of-the-art methods across supervised, unsupervised, and reinforcement learning, including deep learning architectures such as CNNs, RNNs, LSTMs, and ensemble models. Static, dynamic, and hybrid analysis techniques are compared, with a focus on feature engineering, behavioral modeling, and real-world deployment constraints. A novel AI-based Malware Detection and Prevention Framework is proposed, combining machine learning classifiers with Network Intrusion Prevention Systems (NIPS) to enhance proactive defense capabilities. The study evaluates publicly available and synthetic datasets, addressing challenges such as class imbalance, adversarial evasion, and data scarcity. We also highlight ethical considerations including bias, privacy, and accountability in AI-enabled cybersecurity systems. Case studies from mobile and IoT ecosystems demonstrate the practicality and limitations of AI-based defenses in dynamic threat landscapes. Finally, the paper outlines future research directions in explainable AI, automated model generation (AutoML), and adaptive, context-aware intrusion prevention systems. This work serves as a critical resource for developing resilient, scalable, and intelligent cybersecurity infrastructures aligned with modern digital ecosystems

Even though the transmission and processing speeds of electronic documents have been vastly enhanced, electronic document information may be revealed, counterfeited, tampered with, or otherwise compromised. To maintain corporate success in the marketplace, network security should be essential to the protection of electronic documents. As a result, there is a rising demand for authentication and verification procedures for a variety of important documents, including those used in banking, government, and other transactions as well as certificates and other academic credentials. In recent years, there has been a fast growth of digital watermarking technology, which involves embedding invisible or hidden digital signatures into data without compromising the data's authenticity. Hence, in this paper, we utilize the watermarking technology in the encrypted data using dynamic wavelet transform algorithm to make a document more protected. Now the protected data is sent to cloud database for storage. Integrated digital signature algorithm (SHA-256 + DSA) is proposed in this research to generate digital signature for each document. When recipients download the data, the data is verified for its integrity after extracting the digital signature and encrypted data. This strategy improves record security. We also compare the suggested technique to standard practices and assess its performance based on a variety of indicators to demonstrate its effectiveness.

The internet's role in the spread of fake news has made it a serious problem that calls for sophisticated techniques for its automatic detection. To counter the quick spread of misleading material on social media sites, including Facebook, Twitter, Instagram, and WhatsApp, this study explores the domain of deep learning methods and various classification strategies. In particular, the study investigates the application of transformer-based models like BERT, recurrent neural networks (RNNs), and convolutional neural networks (CNNs) for the detection of false news. To provide context for the results, the examination covers a wide range of historical and contemporary trends in the spread of fake news. The study uses strict criteria, including precision, recall, and F1 score, to assess how effective the suggested models are. The created methodologies are validated for effectiveness by using benchmark datasets. This study emphasizes how urgent it is to address the growing problem of fake news, especially as it relates to its use in psychological warfare and clickbait to generate revenue. Through the clarification of the methods, duration, and standards of evaluation used, this research adds to the current conversation on dispelling false information in the digital era.

5G-backed resilience and quality enhancement in internet of medical things infrastructure for resilient infrastructure