Anomaly Detection

Market manipulation remains the biggest concern of investors in today’s securities market. The development of technologies and complex trading algorithms seems to facilitate stock market manipulation and make it inevitable for regulators to use Deep Learning models to prevent manipulation. In this research, a Denoising GAN-based model has been designed. The proposed model (GAN-DAE4) consists of a three-layer encoder along with a 2-dimension encoder as the discriminator and a three-layer decoder as the generator. First, using statistical methods such as sequence, skewness, and kurtosis tests and some unsupervised learning methods such as Contextual Anomaly Detection (CAD) and some visual and graphical methods, the manipulated stocks have been detected in the Tehran Stock Exchange from 2015 to 2020; then GAN-DAE4 and some supervised deep learning models have been applied to the prepared data set. The results show that GAN-DAE4 outperformed other deep learning models (with F2-measure 73.71%) such as Decision Tree (C4.5), Random Forest, Neural Network, and Logistic Regression.

This study aims to present a novel generative AI-driven system for hyper-personalized health monitoring. Dynamic data processing, predictive modeling, and flexible learning improve real-time health evaluations. By combining weighted feature aggregation, iterative least squares estimation, and selective feature extraction, the suggested strategy makes predictions that are more accurate while using less computer power. Abnormality detection methods like adaptive thresholding and Kalman filtering provide accurate health monitoring. Attention, gradient-based optimization, and sequence learning improve health trend forecasts as the model improves. Generative AI-driven wearables outperform conventional and AI-based alternatives in many key performance tests. These evaluations include prediction accuracy (94%), real-time monitoring efficiency (93%), adaptability (92%), data integration quality (95%), and system reaction time (90 ms). These devices are safer (96%), have longer battery life (32 hours), and are simpler, more comfortable, and scalable. The results suggest that creative AI can transform personal healthcare into something more adaptable, safe, and affordable. Generative AI-powered smart gadgets are the most sophisticated means to monitor health in real time and deliver individualized, data-driven medical treatment. Future research will concentrate on improving prediction models and developing AI-driven modification approaches to make them more effective in additional healthcare scenarios.

Background: Contemporary multi-protocol networks necessitate scalability, reliability, energy efficiency, and security due to the increasing number of devices and the diversification of network traffic. Conventional network management methods are inadequate to meet these demands, necessitating sophisticated solutions. Artificial intelligence (AI) has emerged as a significant field, offering advanced methods including predictive maintenance, anomaly detection, and intelligent resource management. Objective: This article aims to critically evaluate the effectiveness, flexibility, and productivity of AI-based applications in addressing major challenges in network management, including performance, scalability, energy consumption, threat detection rates, and cost. Methods: The study employs simulations and modeled datasets to assess AI-oriented solutions across various network environments, such as industrial IoT, smart cities, and telecommunications. The evaluation encompasses factors including Mean Time Between Failure (MTBF), resource utilization, delay minimization, and operating cost reduction. Digital twins, intelligent routing algorithms, and self-attention-based anomaly detection models are utilized, and the overall performance of these integrated technologies is analyzed. Results: The analysis demonstrates that AI-powered systems achieve near-optimal performance across all evaluated indicators. Specifically, the Manufacturing and Automotive Knowledge (MAK) sector observed a 52% increase in MTBF, the Banking, Financial Services, and Insurance (BFSI) sector noted a 32.39% improvement in energy efficiency, and the Defense and Public Enterprise (DPE) sector experienced a 94% increase in advanced threat detection. Conclusion: The findings indicate that AI solutions can effectively address many of the challenges present in current networks, offering cost-efficient and secure methods for implementing new communication networks with vast potential. Nonetheless, further empirical research is necessary to generalize these results and validate their applicability in real-world scenarios.

Cyber threats are changing constantly and these days more than 560,000 new malware varieties are launched daily, which means that rudimentary measures of protecting networks from attacks cannot be of much help in handling real time threats. Single-static security control and manual intervention are insufficient to address APTs, Zero Day, and high-volume DDoS attacks. This is where the application of AI in network security lays its foundation, where real time threat response programs become possible where they are trained to automatically identify, categorize, and mitigate highly complex attacks without requiring massive amount of time and effort. The changing role of AI in network security is examined in this work since it can contribute to the improvement of threat detection, decrease response time, and minimize reliance on human factors. This research reviews more than 150 AI-based security frameworks, and 25 case studies of different industries including finance, healthcare, telecommunications, to assess the efficiency of machine learning and deep learning algorithms for autonomous threat response. The insights show that in challenging contexts, AI-based solutions provide anomaly detection scores of up to 97%, which are far higher than those obtained by conventional systems with average scores of 80%. The response time increased up to 75% as the AI systems responded under 3 seconds during the large scale cyberattack simulation operations. Significant achievement of scalability was across networks with number of nodes more than ten thousand nodes at 90% reliability in different threat scenarios. These findings underscore the importance of AI as the cornerstone of today’s cybersecurity: delivering accurate and timely threat coverage and demonstrating high resilience to threat evolution. However, issues like, algorithm bias, ethical concerns, and resistance to adversarial perturbation calls the need for research to develop effective measures towards the longevity of banking security systems integrated with AI. This study emphasizes the importance of search for new strategies to strengthen current digital environments against the increasing number of threats.

This study introduces a federated learning-based architecture designed to support highly scalable and decentralized anomaly detection in IoT-integrated aquaponics systems. Emphasizing rigorous data privacy, the framework employs PrefixSpan for sequential pattern mining to extract significant temporal behaviors from heterogeneous distributed datasets. IoT sensors deployed across 11 aquaponic ponds collected extensive datasets, each exceeding 170,000 entries, capturing vital indicators such as temperature, pH, turbidity, and fish growth metrics. The proposed FL model demonstrated strong correlations—exceeding 0.9—between water quality conditions and fish development, validating the system’s predictive robustness. Notably, Pond 6 and Pond 10 yielded 1269 and 1339 sequential patterns respectively, confirming the exceptional scalability of the model. The architecture also achieved a 35% reduction in communication latency compared to conventional centralized systems, enabling responsive and efficient anomaly detection in real time. In parallel, a Top-k mining approach was employed to benchmark pattern interpretability as well as computational efficiency because it revealed trade-offs in sensitivity versus frequency-based simplification. Recent studies that focus upon aquaponics have also validated the operational superiority of the system in anomaly detection that is privacy-aware via comparison across models. The comparison highlighted its alignment to sustainable smart farming objectives. By addressing the limitations of centralized data handling, this framework offers a resilient, scalable, and privacy-aware approach to intelligent aquaponics management.