The motivation behind exploring anomaly detection in self-organizing networks lies in the evolving landscape of telecommunications and network management. Conventional methods for identifying network anomalies often struggle to adapt to the dynamic and complex nature of modern self-organizing networks. The problem addressed in this research is the efficacy of anomaly detection methods in self-organizing networks (SONs) within the context of telecommunications and network management. As SONs become increasingly prevalent to meet the demands of modern, highly dynamic wireless communication systems, the (...) need for robust anomaly detection mechanisms is paramount. Conventional anomaly detection approaches in SONs are often based on predefined rules and thresholds, which may struggle to adapt to the intricate and rapidly evolving network behaviors. These methods can result in false alarms, missed anomalies, and inefficient resource allocation. Furthermore, emerging SONs incorporate a multitude of diverse technologies, including 5G, IoT, and edge computing, compounding the complexity of anomaly detection. Contemporary machine learning techniques hold promise in addressing these challenges by enabling the automatic and adaptive detection of anomalies, leveraging the abundance of data generated in SONs. However, the suitability, performance, and scalability of these methods in dynamic and large-scale SON environments remain critical concerns. This research aims to compare and evaluate conventional anomaly detection methods against contemporary machine learning approaches in SONs to assess their accuracy, efficiency, and adaptability. The goal is to provide insights into the most effective anomaly detection strategies, ultimately enhancing network stability, minimizing downtime, and ensuring the secure and efficient operation of modern telecommunications systems. (shrink)

Artificial intelligence (AI) systems are now integral to decision-making across industries, yet concerns around fairness, transparency, and accountability continue to undermine public trust. As models grow in complexity, understanding how data flows through these systems becomes essential. AI-driven lineage offers a solution by providing dynamic, real-time tracking of data and model transformations, enabling transparency throughout the AI lifecycle. This paper explores the critical role of lineage in establishing fair and accountable AI systems. We analyze existing tools, frameworks, and standards, propose (...) a methodology for implementing AI-driven lineage, and present a layered framework that supports ethical governance and regulatory compliance. (shrink)

Artificial intelligence (AI) systems are now integral to decision-making across industries, yet concerns around fairness, transparency, and accountability continue to undermine public trust. As models grow in complexity, understanding how data flows through these systems becomes essential. AI-driven lineage offers a solution by providing dynamic, real-time tracking of data and model transformations, enabling transparency throughout the AI lifecycle. This paper explores the critical role of lineage in establishing fair and accountable AI systems. We analyze existing tools, frameworks, and standards, propose (...) a methodology for implementing AI-driven lineage, and present a layered framework that supports ethical governance and regulatory compliance. (shrink)

Security is a major concern on the internet, and Distributed Denial of Service (DDoS) attacks are a significant threat. These attacks overwhelm network resources and use up bandwidth, making it difficult for legitimate users to access services. One challenge in dealing with DDoS attacks is telling the difference between a sudden increase in traffic from real users, known as a "flash crowd," and actual attack traffic. This paper looks at different existing solutions for detecting DDoS attacks and explains how these (...) methods help reduce their impact. As more businesses move to cloud-based services, the risk of DDoS attacks has increased. These attacks can seriously disrupt organizations by overloading their network resources and making important services unavailable. Traditional defense methods, like firewalls, are no longer enough to handle the scale and complexity of modern DDoS attacks. To address this issue, We propose a new approach that utilizes the implementation of deep learning models to detect DDoS attacks in cloud environments. Our method uses various features from network traffic to train machine learning algorithms, aiming to effectively identify DDoS attacks in real-time with high accuracy. (shrink)