Authors: Vishva Shedge, Shubham Lashkar, Swaraj Pawar, Aaditya Shinde, Prof. A. N. Dubey

Abstract: This paper presents the design and implementation of a Mobile Phone Detection System intended for deployment in restricted environments such as examination halls, secure meeting rooms, and classified zones. The proposed system integrates an ESP32 microcontroller with an NMC5883L digital compass module (HMC5883L-compatible) to detect the electromagnetic and magnetic field signatures associated with active mobile devices. Upon detection, the system triggers an audible alarm via a buzzer and displays status information on a 16×2 LCD screen. Wireless data transmission using the NRF24L01 module enables communication between multiple sensor nodes and a central monitoring unit. The system is designed to be low-cost, energy-efficient, and scalable for multi-zone surveillance. Experimental results confirm reliable detection of active mobile phones within a defined proximity range, demonstrating the practical viability of the proposed approach.

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Published by: vikaspatanker

Authors: Andi Pratama

Abstract: The unprecedented escalation in global data traffic, driven by 5G expansion, hyperscale cloud computing, and the Internet of Things (IoT), has fundamentally altered the threat landscape for high-speed networks. Traditional Network Intrusion Detection Systems (NIDS) that rely on manual signature matching or basic statistical thresholds are increasingly incapable of processing traffic at terabit-per-second scales, leading to significant visibility gaps. This review examines the paradigm shift toward Machine Learning (ML)-based anomaly detection as a solution to the "data deluge" in high-speed environments. By focusing on flow-level metadata and statistical behavioral patterns rather than computationally expensive deep packet inspection (DPI), ML models can identify malicious intent within microseconds. We categorize current methodologies, ranging from unsupervised clustering for zero-day discovery to deep learning architectures like Convolutional Neural Networks (CNNs) for spatial traffic analysis and Long Short-Term Memory (LSTM) networks for temporal sequence modeling. This article explores how these models mitigate "alert fatigue" by providing high-precision filtering of benign noise while identifying subtle "low and slow" adversarial tactics. Furthermore, the review addresses the critical challenges of real-time inference at the network edge, the necessity for model quantization to fit within limited hardware buffers, and the emerging risk of adversarial machine learning. By synthesizing recent academic breakthroughs and industrial implementations, this paper provides a strategic roadmap for building "Cognitive Defense" systems. The findings suggest that ML-integrated anomaly detection is the only viable mechanism for maintaining network resilience and integrity in an increasingly automated and high-velocity digital ecosystem.

DOI: https://doi.org/10.5281/zenodo.19492447

Published by: vikaspatanker

Authors: Dmitry Kuznetsov

Abstract: The escalating complexity of modern network infrastructures, characterized by the convergence of multi-cloud environments, microservices, and massive IoT deployments, has pushed traditional network monitoring beyond its structural limits. Traditional monitoring, which relies on static thresholds and reactive alerting, fails to provide the deep "internal state" visibility required for modern digital resilience. This review examines the paradigm shift toward AI-powered network observability systems. Unlike traditional monitoring, observability leverages high-cardinality telemetry data—including logs, metrics, and traces—to enable the "Unknown-Unknown" discovery of system behaviors. By integrating Artificial Intelligence (AI) and Machine Learning (ML), these systems transition from simple data aggregation to "Cognitive Insight" engines. We categorize the core methodologies of AI-driven observability, including the use of unsupervised learning for real-time anomaly detection, Graph Neural Networks (GNNs) for mapping relational topologies, and Natural Language Processing (NLP) for parsing unstructured log telemetry. This article explores how these systems automate Root Cause Analysis (RCA) and enable "Self-Healing" network architectures. Furthermore, the review addresses critical challenges, such as the "Data Silo" problem, the computational overhead of real-time inference at the network edge, and the necessity for Explainable AI (XAI) to foster operator trust. By synthesizing recent breakthroughs in Deep Learning and AIOps, this paper provides a strategic roadmap for building "Autonomous Observability" frameworks. The findings suggest that AI-powered observability is the foundational technology required to manage the invisible complexity of the 6G and hyper-connected era, ensuring that network operations move from reactive troubleshooting to proactive, foresight-driven optimization.

DOI: https://doi.org/10.5281/zenodo.19492433

Published by: vikaspatanker

Authors: Dr. M. S. Yadhav, Mrs. S. V. Zanjad, Abhishek Prakash Lohar, Malhar Ravindra Kale, Vivek Surendra Gadekar, Avinash Mariba Paikrao.D

Abstract: The Carbon Purification System is designed to improve the quality of gas produced during the decomposition of organic waste. Biogas generated from kitchen waste or other biodegradable materials contains useful methane gas along with unwanted impurities such as hydrogen sulfide, carbon dioxide, and bad odor. These impurities reduce the efficiency and usability of the gas. Therefore, purification of biogas is necessary before it can be used for practical applications. This project focuses on developing a simple and cost-effective carbon purification system that uses activated carbon as the main filtering material. Activated carbon has a very large surface area with many tiny pores that can absorb harmful gases and impurities through the process of adsorption. In this system, the raw gas produced from the digester passes through different filter layers such as a pre-filter, activated carbon layer, and cotton layer, which help remove dust particles, toxic gases, and unpleasant smell. The purification chamber is designed using simple materials so that it can be easily implemented in small-scale applications such as homes, laboratories, and small biogas plants. As the gas passes through the filter layers, harmful substances are trapped and the output gas becomes cleaner and safer to use.

DOI: https://doi.org/10.5281/zenodo.19492379

 

Published by: vikaspatanker

Authors: Siti Rahmawati

 

 

Abstract: The rapid proliferation of cloud-native applications, hybrid work models, and bandwidth-intensive services has fundamentally challenged the static nature of traditional Wide Area Networks (WAN). Software-Defined WAN (SD-WAN) introduced a centralized control plane to decouple network software from hardware, yet the manual definition of steering policies often fails to account for the highly volatile nature of internet transport circuits. This review examines the paradigm shift toward Intelligent SD-WAN Management powered by Artificial Intelligence (AI) and Machine Learning (ML). By leveraging deep learning architectures and reinforcement learning agents, SD-WAN controllers can now transition from reactive, threshold-based switching to proactive, intent-driven optimization. This article explores the core methodologies of AI-integrated management, focusing on predictive traffic engineering, automated root cause analysis, and self-healing infrastructure. We analyze how AI models optimize Quality of Experience (QoE) for mission-critical applications—such as VoIP and real-time video—by analyzing multi-dimensional telemetry including jitter, latency, and packet loss in real-time. Furthermore, the review addresses the critical challenges of model interpretability in network operations, the "cold start" problem in new deployments, and the necessity for federated learning to ensure data privacy across multi-tenant SD-WAN environments. By synthesizing recent academic breakthroughs and industrial implementations, this paper provides a strategic roadmap for building "Self-Driving WANs." The findings suggest that AI-integrated management not only reduces operational expenditure by automating complex routing decisions but also provides the cognitive intelligence required to manage the unpredictable performance of commodity internet underlays in a global digital economy.

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Published by: vikaspatanker

Authors: Farah Syazwani

Abstract: Explainable Artificial Intelligence (XAI) has emerged as a critical paradigm in enhancing trust, transparency, and accountability in cybersecurity systems. As cyber threats become increasingly sophisticated, traditional black-box machine learning models often fail to provide interpretable insights into their decision-making processes, thereby limiting their adoption in high-stakes environments. This review explores the integration of explainable AI techniques within cybersecurity frameworks, focusing on how interpretability improves threat detection, incident response, and risk assessment. The article highlights key methodologies such as feature attribution, model-agnostic explanations, and rule-based learning that enable analysts to understand and validate model outputs. Additionally, the role of XAI in regulatory compliance and ethical AI deployment is examined, emphasizing the need for transparency in automated decision systems. Challenges such as trade-offs between accuracy and interpretability, adversarial manipulation of explanations, and scalability issues are also discussed. Emerging trends, including hybrid explainability approaches and human-in-the-loop systems, are presented as promising directions for future research. By bridging the gap between complex machine learning models and human understanding, XAI holds significant potential to transform cybersecurity decision-making into a more reliable and interpretable process. This review provides a comprehensive overview of current advancements and outlines future pathways for integrating explainable intelligence into cybersecurity infrastructures.

DOI: https://doi.org/10.5281/zenodo.19492116

Published by: vikaspatanker

Authors: Tharushi Silva

Abstract: The rapid evolution of cyber threats, coupled with the increasing complexity of distributed computing environments, has necessitated a paradigm shift in enterprise security strategies. Zero Trust Security Architecture (ZTSA), which operates on the principle of “never trust, always verify,” has emerged as a robust framework to mitigate modern attack vectors. However, traditional Zero Trust implementations often struggle with scalability, dynamic policy enforcement, and real-time threat adaptation. The integration of Artificial Intelligence (AI) into Zero Trust frameworks introduces a transformative approach by enabling adaptive, context-aware, and predictive security mechanisms. AI-augmented Zero Trust architectures leverage machine learning, behavioral analytics, and automation to continuously evaluate trust levels, detect anomalies, and enforce granular access controls. This review explores the convergence of AI and Zero Trust, highlighting architectural components, implementation strategies, and challenges. It further examines how AI enhances identity verification, network segmentation, and threat intelligence, while addressing issues such as data privacy, model bias, and operational complexity. By synthesizing current research and industry practices, this article presents a comprehensive overview of AI-driven Zero Trust systems and their role in securing next-generation digital infrastructures.

DOI: https://zenodo.org/records/19491997

Published by: vikaspatanker

Authors: Elena Volkova

Abstract: In the modern era of decentralized workforces and cloud-native architectures, the traditional perimeter-based security model has collapsed, giving way to identity as the new primary security boundary. Identity and Access Management (IAM) systems are now the gatekeepers of enterprise resources, yet they face an unprecedented volume of sophisticated attacks, ranging from credential stuffing to advanced social engineering. This review examines the paradigm shift toward AI-Powered Identity and Access Management Systems. By integrating Machine Learning (ML) and Deep Learning (DL) algorithms, modern IAM frameworks have transitioned from static, rule-based engines to dynamic, risk-aware ecosystems. These systems leverage User and Entity Behavior Analytics (UEBA) to establish granular baselines of normal activity, allowing for the real-time detection of anomalies that signal compromised credentials or insider threats. This article categorizes current AI methodologies, including the use of neural networks for biometric authentication and reinforcement learning for adaptive access control policies. We explore how AI mitigates "entitlement creep" and automates the complex lifecycle of identity governance. Furthermore, the review addresses the integration of AI within Zero Trust Architectures (ZTA), where continuous authentication replaces the "authenticate once, access forever" model. By synthesizing recent research and industrial deployments, this paper provides a strategic roadmap for the next generation of identity security. The findings suggest that while AI significantly enhances the precision of access decisions, its success depends on data privacy, model transparency, and resilience against adversarial manipulation.

DOI: https://doi.org/10.5281/zenodo.19491983

Published by: vikaspatanker

Authors: Sanjay Mishra

Abstract: The escalating complexity of modern network infrastructures, characterized by the convergence of 5G, software-defined networking (SDN), and hyperscale cloud-to-edge continuums, has rendered traditional reactive maintenance models obsolete. In these high-velocity environments, a single link failure or hardware malfunction can trigger a cascade of service disruptions, resulting in significant financial losses and reputational damage. This review examines the paradigm shift toward Predictive Network Failure Analysis (PNFA) powered by Machine Learning (ML). By leveraging high-fidelity telemetry data, including syslog entries, SNMP traps, and flow metrics, ML models can identify the subtle "pre-cursor" signatures of impending hardware exhaustion, optical signal degradation, or software anomalies. This article categorizes current methodologies, focusing on the use of Long Short-Term Memory (LSTM) networks for temporal fault forecasting and Random Forests for multi-variate root cause analysis. We explore how predictive models enable the transition from "Break-Fix" to "Proactive Remediation," where maintenance is triggered by a probability score rather than a catastrophic event. Furthermore, the review addresses critical challenges, such as the "data imbalance" problem, where failure events are rare compared to normal operations, and the necessity for Explainable AI (XAI) to ensure operator trust in automated diagnostics. By synthesizing recent academic breakthroughs and industrial frameworks, this paper provides a strategic roadmap for building "Self-Healing Networks." The findings suggest that ML-driven predictive analysis significantly reduces the Mean Time to Repair (MTTR) and improves overall network availability, providing the cognitive foundation required for the next generation of autonomous digital infrastructure.

DOI: https://doi.org/10.5281/zenodo.19491925

Published by: vikaspatanker

Authors: Deepak Chauhan

Abstract: The digital infrastructure of the modern enterprise is undergoing a radical transformation, driven by the widespread adoption of cloud-native applications, real-time collaboration tools, and high-bandwidth multimedia services. In this dynamic landscape, traditional Quality of Service (QoS) mechanisms, which rely on static priority queuing and manually defined traffic classes, are increasingly incapable of managing the volatility of network demand. This review explores the paradigm shift toward Machine Learning (ML)-based QoS optimization. By transitioning from reactive, threshold-based management to proactive, intent-driven architectures, ML enables enterprise networks to achieve "Cognitive Traffic Engineering." This article examines how various ML paradigms—including supervised learning for traffic classification, unsupervised learning for anomaly detection, and reinforcement learning for dynamic resource allocation—can be synthesized into a unified optimization fabric. We analyze the efficacy of Deep Learning models, such as Convolutional Neural Networks and Long Short-Term Memory units, in identifying application-layer requirements within encrypted tunnels without the need for Deep Packet Inspection. Furthermore, the review addresses the architectural integration of ML within Software-Defined Networking (SDN) and SD-WAN frameworks, enabling the "Self-Driving Network" vision. Critical challenges, such as model interpretability, real-time inference latency at the network edge, and data drift in multi-tenant environments, are discussed in depth. By synthesizing recent academic breakthroughs and industrial implementations, this paper provides a strategic roadmap for building resilient, high-performance enterprise networks. The findings suggest that ML-driven QoS is the foundational technology required to satisfy the stringent Service Level Agreements of the modern digital enterprise, ensuring that network resources are distributed with machine-speed precision and contextual intelligence.

DOI: https://doi.org/10.5281/zenodo.19491907

Published by: vikaspatanker