Daily Archives: April 10, 2026

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.

DOI:

 

 

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

Authors: Rakesh Mehta

Abstract: The rapid escalation of global data traffic, catalyzed by the proliferation of 5G, Internet of Things (IoT) devices, and high-definition streaming services, has rendered traditional network management techniques increasingly obsolete. Packet Flow Classification serves as the foundational mechanism for Quality of Service (QoS) provisioning, resource allocation, and security enforcement. Historically, flow classification relied on port-based analysis or Deep Packet Inspection (DPI); however, the widespread adoption of end-to-end encryption protocols, such as TLS 1.3 and QUIC, alongside dynamic port allocation, has nullified these legacy methods. This review examines the paradigm shift toward Machine Learning (ML) and Deep Learning (DL) models as the primary engines for real-time traffic classification. By focusing on statistical flow features and byte-level patterns rather than plaintext payloads, ML models can identify applications and malicious intent within encrypted tunnels with unprecedented accuracy. We categorize current methodologies, ranging from classical supervised learners like Random Forests to advanced neural architectures, including Convolutional Neural Networks (CNNs) for spatial feature extraction and Recurrent Neural Networks (RNNs) for temporal sequence modeling. Furthermore, the review addresses the critical challenges of real-time processing at line speed, data imbalance in network datasets, and the necessity for Explainable AI (XAI) in network operations. By synthesizing recent academic breakthroughs and industrial implementations, this paper provides a strategic roadmap for building autonomous, "self-driving" networks. The findings suggest that ML-driven packet flow classification significantly enhances network visibility and resilience, providing the cognitive intelligence required to manage the complex, opaque traffic landscapes of the modern digital era.

DOI: https://doi.org/10.5281/zenodo.19491852

Published by: vikaspatanker

Authors: Manoj Tiwari

Abstract: Machine learning has emerged as a transformative force in cybersecurity, enabling predictive defence mechanisms that move beyond traditional reactive strategies. This review explores the evolution, methodologies, and applications of machine learning models in predictive cybersecurity defence. By leveraging large-scale data, these models can detect anomalies, anticipate threats, and automate responses in real time. Techniques such as supervised learning, unsupervised learning, and deep learning have been widely adopted to identify patterns in network traffic, user behaviour, and system logs. Predictive capabilities allow organizations to mitigate risks before attacks occur, reducing financial and operational damage. However, challenges such as adversarial attacks, data imbalance, model interpretability, and scalability persist. This article also highlights emerging trends, including federated learning, explainable AI, and hybrid defence systems that integrate human expertise with machine intelligence. Through a comprehensive analysis, the review emphasizes the need for robust, adaptive, and ethical frameworks to ensure reliable deployment of machine learning in cybersecurity. The findings suggest that while machine learning significantly enhances predictive capabilities, its effectiveness depends on data quality, continuous model updates, and integration with existing security infrastructures.

DOI: https://doi.org/10.5281/zenodo.19491834

Published by: vikaspatanker

Authors: Siti Amina

Abstract: Deep learning-based intrusion detection systems (IDS) have emerged as a transformative approach for securing enterprise networks in the face of increasingly sophisticated cyber threats. Traditional signature-based and rule-based IDS solutions struggle to detect zero-day attacks, polymorphic malware, and advanced persistent threats due to their reliance on predefined patterns. In contrast, deep learning models offer the ability to automatically learn hierarchical feature representations from large-scale network traffic data, enabling improved detection accuracy and adaptability. This review examines the evolution, methodologies, and practical implementation of deep learning-based IDS in enterprise environments. It highlights the role of architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), autoencoders, and transformer-based models in identifying anomalous and malicious activities. The study further explores data preprocessing techniques, feature engineering, and benchmark datasets commonly used for training and evaluation. Key challenges, including data imbalance, model interpretability, computational overhead, and real-time deployment constraints, are critically analyzed. Additionally, the integration of deep learning IDS with emerging technologies such as cloud computing, edge computing, and software-defined networking (SDN) is discussed. The review concludes by outlining future research directions focused on improving scalability, explainability, and resilience against adversarial attacks. Overall, deep learning-based IDS represent a promising paradigm shift in enterprise cybersecurity, offering intelligent, adaptive, and proactive defense mechanisms.

DOI: https://doi.org/10.5281/zenodo.19491764

Published by: vikaspatanker