Research & Review: Machine Learning and Cloud Computing (e-ISSN: 2583-4835)

AI-Based Malware Behavior Classification and Mitigation System Using Machine Learning

Sivaharish R. — 2026-10-03

The continuous evolution and technical sophistication of modern cyber threats present severe challenges to enterprise networks, endpoint devices, and personal data privacy. Conventional security measures that rely predominantly on static file signatures and hardcoded indicators of compromise frequently fail against advanced polymorphic, packed, and fileless malware strains that dynamically alter their binary structures upon replication. To overcome these critical defensive limitations, this study proposes an advanced, integrated behavior-based malware classification and automated mitigation framework specifically engineered for Windows operating environments. Instead of examining static file hashes on disk prior to execution, the proposed system captures and analyzes high-velocity, multi-variable runtime telemetry in real time. This comprehensive telemetry monitoring encompasses low-level process creation paths, parent-child execution hierarchies, CPU and memory resource utilization percentages, file system modification counts, active network socket connections, and system event triggers. These dynamic behavioral attributes are processed through optimized supervised machine learning algorithms, specifically Decision Trees, Random Forests, Support Vector Machines, and Logistic Regression, to accurately classify software execution patterns as benign or malicious while predicting specific threat types. Furthermore, the framework introduces a multi-class risk-scoring mechanism (categorizing threats into Low, Medium, High, and Critical tiers) to streamline Security Operations Center (SOC) incident triage. Upon identifying malicious behavior exceeding predefined risk thresholds, an automated mitigation pipeline initiates immediate defensive protocols, including real-time security alerts and the instantaneous termination of hazardous processes. All monitored telemetry streams, classification outputs, risk evaluations, and mitigation actions are dynamically rendered through a responsive, interactive React.js web dashboard. Comprehensive experimental benchmarking reveals that the Random Forest model achieves the highest classification accuracy at 89.2%, outperforming alternative algorithms and demonstrating strong efficacy for proactive endpoint protection and automated threat containment.

Fake News Detection Using Machine Learning: An Evaluation of Classification Models

Raman Shukla — 2026-09-02

Social media fake news attacks free speech and makes people question the media. This heavily affects elections and public health. Human fact-checkers cannot keep up with the amount of content posted on the web. Because of this, there is great interest in the use of Machine Learning (ML) for news verification. This paper surveys five ML techniques used for fake news verification: Naïve Bayes, Logistic Regression, Support Vector Machine (SVM), Random Forest, and Long Short-Term Memory (LSTM). The effectiveness of the techniques is compared using accuracy, precision, recall, and F1-score based on previous studies performed on three commonly used benchmark datasets. The survey shows that with the right number of resources, LSTM can provide the best accuracy, while Naïve Bayes and Support Vector Machine (SVM) are the best and most resource-efficient algorithms. The paper lists the limitations of existing systems such as the lack of real-time verification, the difficulty of detection in a different context (domain), and the sparseness of labelled training data.

Energy-Aware Adaptive AI Inference in Edge–Cloud Computing

Nimesh Yadav — 2026-10-05

Adaptive AI inference distributes the computation across devices, edge servers, and cloud infrastructure to optimize task quality, latency, and energy. This study provides a critical overview of the literature on offloading, split inference, early exits, model routing, hardware adaptation, and speculative decoding selected from 2018 to 2026. Targeted public-source searches and full-text inspection enable comparison of mechanisms, experimental settings, and energy boundaries; exhaustive systematic coverage and meta-analysis are not claimed. The synthesis differentiates between measurements of client-energy and wider node measurements, analytical estimates, and communication and monetary proxies. Qualified numerical examples demonstrate the importance of having baseline choices and differences to go with reported savings. The evaluation requirements go beyond traditional DNN inference, as the models are based on language and multimodal models introduce repeated verification, rejected computation, and response-length dependence. The primary recommendation is to consider policies at specified quality points and latency goals and to factor in communication, monitoring, transitions, and escalation. It is important to have a clear view of accounting and reproducible workloads to determine if adaptation ultimately improves overall energy efficiency or moves energy between tiers.

From Function-as-a-Service to the Edge: A Meta-synthesis of Serverless Computing Research, Platforms, and Open Challenges

Suraj R. Nalawade — 2026-09-14

Serverless computing has moved from an experimental deployment style to a mainstream option for building cloud-native applications, shifting responsibility for provisioning, scaling, and fault tolerance from the developer to the platform. This paper synthesizes findings from two large-scale systematic reviews of the serverless computing literature, one spanning 164 papers and another spanning 275 papers published between 2016 and 2020, together with supporting studies on architecture, platform performance, and application domains, to build a consolidated picture of the field. The synthesis indicates that research attention has concentrated heavily on performance engineering, programming frameworks, and resource management, while comparatively less work addresses benchmarking standardization, resource-aware pricing, and privacy protection for Internet-of-Things deployments. Commercial platforms such as AWS Lambda, Microsoft Azure Functions, and Google Cloud Functions dominate industry adoption, while open-source, container-based alternatives offer greater portability at the cost of additional operational overhead. The review concludes that closing these specific gaps rather than further validating already well-studied performance claims represents the most productive direction for near-term serverless computing research.