Daily Archives: April 10, 2026

Authors: Sanduni Fernando

Abstract: The rapid migration of organizational workloads to cloud environments has introduced unprecedented scalability but also significant financial complexity. Cloud billing is often characterized by high-volume, granular data where "anomalies"—unexpected spikes or shifts in spending—can remain undetected for weeks, leading to "cloud sprawl" and budget overruns. Traditional threshold-based monitoring systems often fail in these dynamic environments due to their inability to distinguish between legitimate scaling and genuine waste. This article reviews the shift toward Machine Learning (ML)-centric approaches for cloud cost anomaly detection. By leveraging time-series forecasting, clustering, and deep learning, ML models can learn the "seasonal" rhythms of business operations and flag deviations with high precision. This review explores the architectural foundations of these systems, evaluates supervised versus unsupervised learning paradigms, and discusses the operational challenges of implementing AI-driven FinOps. Ultimately, the integration of ML transforms cost management from a reactive reporting task into a proactive, automated defense mechanism, ensuring operational stability and financial efficiency in modern cloud-native architectures.

DOI: https://doi.org/10.5281/zenodo.19491753

Published by: vikaspatanker

Authors: Ahmad Rizal

Abstract: Insider threats represent one of the most challenging cybersecurity risks, as they originate from individuals with legitimate access to organizational systems and data. Traditional security mechanisms often fail to detect such threats due to their reliance on signature-based or rule-based approaches that lack contextual awareness. Behavioral analytics, powered by machine learning (ML), has emerged as a transformative approach for identifying anomalous patterns indicative of insider misuse, fraud, or sabotage. This review explores the integration of behavioral analytics and ML techniques to enhance insider threat detection capabilities. By leveraging user activity logs, network traffic data, and system interactions, ML models can establish baseline behavioral profiles and identify deviations in real time. The study examines supervised, unsupervised, and hybrid learning approaches, highlighting their effectiveness in detecting both known and unknown threats. Additionally, it discusses feature engineering, data preprocessing, and the role of contextual information in improving detection accuracy. Challenges such as data imbalance, privacy concerns, adversarial behavior, and model interpretability are also critically analyzed. The review further explores emerging trends, including deep learning, graph-based analytics, and explainable AI, which are shaping next-generation insider threat detection systems. Ultimately, behavioral analytics

DOI: https://doi.org/10.5281/zenodo.19491716

Published by: vikaspatanker

Authors: Muhammad Hakim

Abstract: The escalating complexity of global digital infrastructures, characterized by the convergence of 5G, massive IoT deployments, and hyperscale cloud-to-edge continuums, has rendered traditional linear network management models obsolete. At the heart of this complexity lies the network topology—the intricate map of nodes and interconnections that dictates the flow, latency, and resilience of data. This review article explores the paradigm shift toward Graph Analytics for Network Topology Optimization. Unlike traditional tabular data analysis, graph analytics treats the network as a native mathematical graph, where routers, switches, and endpoints are vertices, and the communication links are edges. This relational perspective allows for the discovery of structural properties—such as centrality, community clusters, and bottleneck bottlenecks—that are invisible to classical monitoring. We categorize the core methodologies of graph-driven optimization, including the use of Graph Neural Networks (GNNs) for predictive traffic steering and PageRank-inspired algorithms for identifying critical infrastructure vulnerabilities. The article examines how graph analytics enables "Topological Resilience," allowing networks to autonomously reconfigure their structure in response to failures or shifting demand. Furthermore, the review addresses the critical challenges of processing massive-scale dynamic graphs in real-time, the computational overhead of graph embeddings, and the necessity for explainable graph models in network operations. By synthesizing recent breakthroughs in spectral graph theory and combinatorial optimization, this paper provides a strategic roadmap for building "Self-Optimizing Topologies." The findings suggest that graph analytics is the foundational intelligence required to manage the "Relational Complexity" of the 6G era, ensuring that global networks are not just faster, but fundamentally more robust, efficient, and adaptive.

DOI: https://doi.org/10.5281/zenodo.19491714

Published by: vikaspatanker