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OpenStackDP: a scalable network security framework for SDN-based OpenStack cloud infrastructure
Network Intrusion Detection Systems (NIDS) and firewalls are the de facto solutions in the modern cloud to detect cyberattacks and minimize potential hazards for tenant networks. Most of the existing firewalls, perimeter security, and middlebox solutions are built on static rules/signatures or simple rule matching, making them inflexible, susceptible to bugs, and difficult to introduce new services. This paper aims to improve network management in OpenStack Clouds by taking advantage of the combination of software-defined networking (SDN), Network Function Virtualization (NFV), and machine learning/artificial intelligence (ML/AI) and for making networks more predictable, reliable, and secure. Artificial intelligence is being used to monitor the behavior of the virtual machines and applications running in the OpenStack SDN cloud so that when any issues or degradations are noticed, the decision can be quickly made on how to handle that issue, being able to analyze data in motion, starting at the edge. The OpenStackDP framework comprises lightweight monitoring, anomaly-detecting intelligent sensors embedded in the data plane, a threat analytics engine based on ML/AI algorithms running inside switch hardware/network co-processor, and defensive actions deployed as virtual network functions (VNFs). This network data plane-based architecture makes high-speed threat detection and rapid response possible and enables a much higher degree of security. We have built the framework with advanced streaming analytics technologies, algorithms, and machine learning to draw knowledge from this data that is in motion before the malicious traffic goes to the tenant compute nodes or long-term data store. Cloud providers and users will benefit from improved Quality-of-Services (QoS) and faster recovery from cyber-attacks and compromised switches. The multi-phase collaborative anomaly detection scheme demonstrates an accuracy of 99.81%, average latencies of 0.27 ms, and response speed within 9 s. The simulations and analysis show that the OpenStackDP network analytics framework substantially secures and outperforms prior SDN-based OpenStack solutions for Cloud architectures. 2023, The Author(s). -
Highly secured authentication and fast handover scheme for mobility management in 5G vehicular networks
The Fifth Generation (5 G) networks exhibit high flexibility and diversity in their design and deployment strategies. Transitions between base stations (BSs), heterogeneous networks (HetNets) and other cellular networks provide significant vulnerabilities and expose users to substantial risks associated with cybersecurity attacks. This article evaluates current handover authentication methods in the context of 5 G networks while proposing a set of security criteria for handover authentication. This study presents a novel authentication technique called SHK (Secure Handover Key) that utilizes SDNs (Software Defined networks). The proposed scheme integrates recycled lightweight dynamic key cryptography and combines security features such as perfect forward secrecy and robustness to leakages. The significance of the proposed approach is assessed in terms of computations, communications, signals, and energy costs on 5 G mobility applications and Vehicular Communication Networks (VCNs). The scheme employed in this study demonstrates enhanced security measures and improved changeover performance compared to conventional schemes. 2024 Elsevier Ltd -
Transformation of hydrocarbon soot to graphenic carbon nanostructures
Graphenic carbon nanostructures were synthesized from different precursors of petroleum and agricultural origin by oxidative scissoring. In the present study soot, an environmental pollutant is converted to a value-added product by facile synthesis techniques. The physicochemical changes of the nanostructures are investigated by means of XRD, AFM, FTIR, Raman spectroscopy, XPS analyses SEM-EDS and TEM analysis. XRD analysis confirms the formation of few layer oxidized carbon nanostructures with smaller lateral dimensions. Raman spectra reveal the existence of graphenic layer with a fewer defect. AFM and SEM analyses reveal the formation of stacked tiny fragments of graphenic carbon lamellae. XPS and IR analyses confirm the incorporation of oxygen functionalities into the carbon backbone. 2018 by the authors. -
Raman spectroscopy investigation of camphor Soot: Spectral analysis and structural information
Raman spectra of camphor soot has been investigated and optimised with a Raman microscope system operated at laser excitation wavelength of 514.5 nm. Several band combinations for spectral analysis have been tested, and a combination of three Lorentian bands ( G,D1,D2) at about 1580, 1350 and 1620 cm-1, respectively, with Gaussian-shaped band (D3) at 1500 cm-1and 1200 cm-1 (D4) was best suited for the first order spectra. The second-order spectra were best fitted with Lorentian shaped bands at about 2450, 2700, 2900 and 3250 cm-1. The results are discussed and compared with X-ray diifraction measurements and SEM analysis. The camphor soot shows ? and P{cyrillic} bands which reveals the presence of crystalline graphitic carbon. The SEM micrographs of camphor show the presence of carbon nanostructures. 2013 by ESG. -
Design and analysis of single stage Step-up converter for Photovoltaic applications
Main novelty of the proposed work is dual leg single stage DC-AC converter for DC\AC grid and solar based applications. Operating principles, components design and modulation techniques are presented. Initially proposed concept is simulated in MATLAB Simulink platform and after validated in a real time prototype model is the future work. Proposed idea has some advantages like few passive components, less leakage current due to few switching frequency components, wide range voltage with absence of DC link capacitor. High efficiency due to single stage operation so this circuit is highly suitable for high\low voltage photo-voltaic energy conversion. Electromagnetic interference also less with continuous current. 2023 IEEE. -
Identification of Dry Bean Varieties Based on Multiple Attributes Using CatBoost Machine Learning Algorithm
Dry beans are the most widely grown edible legume crop worldwide, with high genetic diversity. Crop production is strongly influenced by seed quality. So, seed classification is important for both marketing and production because it helps build sustainable farming systems. The major contribution of this research is to develop a multiclass classification model using machine learning (ML) algorithms to classify the seven varieties of dry beans. The balanced dataset was created using the random undersampling method to avoid classification bias of ML algorithms towards the majority group caused by the unbalanced multiclass dataset. The dataset from the UCI ML repository is utilised for developing the multiclass classification model, and the dataset includes the features of seven distinct varieties of dried beans. To address the skewness of the dataset, a Box-Cox transformation (BCT) was performed on the dataset's attributes. The 22 ML classification algorithms have been applied to the balanced and preprocessed dataset to identify the best ML algorithm. The ML algorithm results have been validated with a 10-fold cross-validation approach, and during validation, the CatBoost ML algorithm achieved the highest overall mean accuracy of 93.8 percent, with a range of 92.05 percent to 95.35 percent. 2023 S. Krishnan et al. -
Pore size matters!a critical review on the supercapacitive charge storage enhancement of biocarbonaceous materials
A circular economy targets zero waste converting both natural and synthetic wastes to valuable products, thereby promoting sustainable development. The porous nanocarbon synthesized from bio-waste is one such product used in applications such as energy storage, catalysis, and sensors. Different techniques are employed for synthesizing carbon from the biowastes and each route results in different properties toward end-user applications. Among them, surface area and porosity are the two critical factors that influence the energy storage capabilities of these synthesized carbon nanostructures. Besides the high surface area of the bio-derived carbons, the hindrance in supercapacitive performance is owing to its low porosity. Fewer review/research papers report the porosity tuning of these carbons for their influence on enhancing the performance of energy storage devices (supercapacitors). This critical review analyses the importance of porosity in these bio-derived carbons and reviews the recent development in its synthesis techniques along with its improvement in the energy storage capability. Special attention is also delivered to identify the ambient source of biowaste for carbon electrodes (fabrication) in supercapacitors. The recent research progress in tuning the porosity of these bio-derived carbons and the influence of electrolyte with porosity in affecting its supercapacitive energy storage is elucidated here. The research challenges, future research recommendations, and opportunities in the synthesis of bio-derived porous carbon for supercapacitor applications are briefed. 2022 Taylor & Francis Group, LLC. -
Physical Unclonable Function and OAuth 2.0 Based Secure Authentication Scheme for Internet of Medical Things
With ubiquitous computing and penetration of high-speed data networks, the Internet of Medical Things (IoMT) has found widespread application. Digital healthcare helps medical professionals monitor patients and provide services remotely. With the increased adoption of IoMT comes an increased risk profile. Private and confidential medical data is gathered across various IoMT devices and transmitted to medical servers. Privacy breach or unauthorized access to personal medical data has far-reaching consequences. However, heterogeneity, limited computational resources, and lack of standardization in authentication schemes prevent a robust IoMT security framework. This paper introduces a secure lightweight authentication and authorization scheme. The use of the Physical Unclonable Function (PUF) reduces pressure on computational resources and establishes the authenticity of the IoMT. The use of OAuth 2.0 open standard for authorization allows interoperability between different vendors. The resilience of the model to impersonation and replay attacks is analyzed. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Zero Trust-Based Adaptive Authentication using Composite Attribute Set
Rapid evolution of internet-oriented applications has increased the threats to confidential data. Single-factor authentication approaches are no longer sufficient to ensure user credibility. Multi-factor authentication schemes are also not tamper-proof. A Zero Trust, adaptive authentication-based approach that uses the user's past behavior can offer protection in this scenario. This paper proposes a system that collects a composite attribute set that includes the user behavior, attributes of the application through which the user is requesting access, and the device used. The enhanced collection allows the creation of detailed context that allows granular variance calculation and risk score. 2021 IEEE. All Rights Reserved. -
A JSON Web Signature Based Adaptive Authentication Modality for Healthcare Applications
In the era of fast internet-centric systems, the importance of security cannot be stressed more. However, stringent and multiple layers of security measures tend to be a hindrance to usability. This even prompts users to bypass multi-factor authentication schemes recommended by enterprises. The need to balance security and usability gave rise to Adaptive authentication. This system of utilizing the user's behavioral context and earlier access patterns is gaining popularity. Continuously analyzing the user's request patterns and attributes against an established contextual profile helps maintain security while challenging the user only when required. This paper proposes an Open standards based authentication modality that can seamlessly integrate with an Adaptive Authentication system. The proposed authentication modality uses JavaScript Object Notation(JSON), JSON Web Signature(JWS) and supports a means of verifying the authenticity of the requesting client. The proposed authentication modality has been formally verified using Scyther and all the claims have been validated. 2022 IEEE. -
Mythistorical Construction of Divinity and Femininity in Early Mohiniyattam Manuals
The history of Indian classical dances has been shaped by nation-building ideologies that constructed a culturally sanctioned past. For Mohiniyattam, a classical dance from Kerala, dance manuals have constructed a religious and gendered narrative, often relying on myths to comprehend its fragmented history. This paper explores the mythistorical narratives in the dance manuals of four early Mohiniyattam practitioners: Kalamandalam Kalyanikutty Amma, Kalamandalam Sathyabhama, Dr. Kanak Rele, and Bharati Shivaji. Through textual analysis and contextual interpretation, we explain how mythistorical accounts of divinity and femininity shape the sociocultural and bodily dynamics of the performance and the performer. 2026 Taylor & Francis Group, LLC. -
Nature metaphors in Kalyanikutty Ammas Mohiniyattam body aesthetics
Mohiniyattam is a classical dance tradition from the state of Kerala in India, which is bestowed with a feminine identity. Scholars have theorised this feminine identity as self-enforced, constructed and regional. Unique to Mohiniyattam is the inherent relationship with nature. Nature association can be observed in the tradition of Kalamandalam Kalyanikutty Amma, one of the revivalists of Mohiniyattam. This paper will closely read the dance manual Mohiniyattam: Charithravum Aattaprakaaravum by Kalyanikutty Amma, focussing on Ammas use of nature metaphor for body movement. Dance theorist Sondra Fraleighs ideation of the lived and poetic body will be used to understand the experience and expression of nature as a movement quality. Further, Kavya Krishnas theorisation of gender performativity in Mohiniyattam and the discussions on regionality and naturality in movement will bring into effect an ecofeminist reading of Ammas dance aesthetics. This paper will also examine the metaphorical nomenclature that constructs nature as a feminine quality in the body movements and aesthetics of Mohiniyattam. 2025 Informa UK Limited, trading as Taylor & Francis Group. -
Nature metaphors in Kalyanikutty Ammas Mohiniyattam body aesthetics
Mohiniyattam is a classical dance tradition from the state of Kerala in India, which is bestowed with a feminine identity. Scholars have theorised this feminine identity as self-enforced, constructed and regional. Unique to Mohiniyattam is the inherent relationship with nature. Nature association can be observed in the tradition of Kalamandalam Kalyanikutty Amma, one of the revivalists of Mohiniyattam. This paper will closely read the dance manual Mohiniyattam: Charithravum Aattaprakaaravum by Kalyanikutty Amma, focussing on Ammas use of nature metaphor for body movement. Dance theorist Sondra Fraleighs ideation of the lived and poetic body will be used to understand the experience and expression of nature as a movement quality. Further, Kavya Krishnas theorisation of gender performativity in Mohiniyattam and the discussions on regionality and naturality in movement will bring into effect an ecofeminist reading of Ammas dance aesthetics. This paper will also examine the metaphorical nomenclature that constructs nature as a feminine quality in the body movements and aesthetics of Mohiniyattam. 2025 Informa UK Limited, trading as Taylor & Francis Group. -
Bridging the Rural Digital Divide: Machine-Learning-Driven Predictive Modeling of Digital Literacy Program Outcomes
The research project performed multiple regression model evaluations to assess how effective digital literacy schemes are in rural education settings. Training program achievements relied on predicted educational proficiency scores while program evaluation relied on both comprehensive participant demographic details and process training statistics. Our study examined numerous regression approaches from basic Linear Regression forms through advanced Random Forest and Gradient Boosting models and concluded with complex methods including Stacking and XGBoost. The research analyzed prediction accuracy and model explanatory power using Mean Squared Error (MSE) and Rsquared (R2) values during the evaluation process. Multiple feature applications were the best fit for the deterministic ensemble techniques which exhibited superior performance but alternatively different analytical models displayed stable prediction results. This research proposes educational method advancement through machine learning approaches capable of creating custom solutions targeting rural user requirements. This study delivers key information to stakeholders in its combined study of digital education enhancements and sophisticated learning evaluation data analysis techniques. 2025 IEEE. -
Bridging the Rural Digital Divide: Machine-Learning-Driven Predictive Modeling of Digital Literacy Program Outcomes
The research project performed multiple regression model evaluations to assess how effective digital literacy schemes are in rural education settings. Training program achievements relied on predicted educational proficiency scores while program evaluation relied on both comprehensive participant demographic details and process training statistics. Our study examined numerous regression approaches from basic Linear Regression forms through advanced Random Forest and Gradient Boosting models and concluded with complex methods including Stacking and XGBoost. The research analyzed prediction accuracy and model explanatory power using Mean Squared Error (MSE) and Rsquared (R2) values during the evaluation process. Multiple feature applications were the best fit for the deterministic ensemble techniques which exhibited superior performance but alternatively different analytical models displayed stable prediction results. This research proposes educational method advancement through machine learning approaches capable of creating custom solutions targeting rural user requirements. This study delivers key information to stakeholders in its combined study of digital education enhancements and sophisticated learning evaluation data analysis techniques. 2025 IEEE. -
Smart Autonomous Robot for Efficient Hospitality Service
The hospitality industry is constantly striving to deliver excellent guest experience in the form of timely service and quality food. However, increasing customer expectations have been challenging the industry in the form of workload management, the recruitment of skilled personnel, and the management of operating expenses. Faced with these challenges, companies are embracing state-of-the-art technologies, and mobile robots have been viewed as a potential solution. This paper presents the concept design of a state-of-the-art autonomous robot for food delivery in hospitality establishments. Inspired by robots such as Amazon's Kiva Robots, the robot uses camera modules, path finding algorithms, and sensors to navigate through dynamic spaces while avoiding furniture and moving guests. Unlike warehouse robots, restaurant robots need to learn to adapt to uncertain environments while maintaining the friendly ambiance. By automating routine tasks such as food delivery, the robot allows staff to focus on delivering personalized customer service. Its technical features consist of environmental monitoring camera modules, path-finding algorithm sensors for obstacles detection. It adapts to dynamic environmental conditions for efficiency and safety. Innovation increases operational efficiency, saves labor costs, and improves food quality. 2025 IEEE. -
Behind the Fallout: Environmental Strategy and Innovation Gone Awry
Innovation and environmental strategy play vital roles in addressing the issues of ecological preservation and sustainability. This chapter explores the complicated nature of these concepts, along with their benefits and risks. It also aims to uncover practical lessons from its identified failures. The chapter provides an overview of innovation and environmental strategy, emphasizing their importance in today's environmental and business landscapes. It also explores the central theme of the study: the failure of innovation and environmental strategy to address the challenges of sustainability. Secondly, the chapter explores the various causes of environmental strategy malfunctions. Through a combination of case studies and analysis, it is possible to learn about the common traps, such as poor execution and resource limitations. Thirdly, the chapter focuses on the relationship between innovation and the environment, shedding light on its potential and also the obstacles it encounters in case studies of unsuccessful approaches. The impact of regulation and environmental policy on corporate strategy is explored in the fourth section, which considers how such changes can affect existing approaches, offering practical insights through case studies. Next, the importance of collaboration and communication is emphasized, in which case studies show how poor stakeholder engagement can affect the outcome of an environmental strategy. The sixth section of the chapter tackles the technological issues that can arise when implementing an environmental strategy. It delves into the cases where technological obstacles have resulted in failures. Next, the effect of culture on environmental initiatives is explored. This shows how short-term thinking and resistance to change can either hinder or support initiatives. The eighth section focuses on improving environmental strategies. It offers suggestions on identifying and rectifying issues with such approaches, emphasizing the significance of learning from failures and continuous improvement. Finally, there is a summary of the chapter's findings and a comprehensive overview. This emphasizes how important it is to learn from failures in environmental approaches, offering suggestions for future research. 2026 selection and editorial matter, Sonal Trivedi, Balamurugan Balusamy, Krishnaraj Nagappan, Dinesh Krishnan Subramaniam and Daniel Arockiam; individual chapters, the contributors. All rights reserved. -
Provably Adaptive Trust Dynamics in Context-Aware Zero-Trust Systems: A Formal Framework for Continuous Verification
Zero-Trust (ZT) requires continuous, context-aware evaluation of authentication and authorization decisions. This paper introduces Zero-Trust Hybrid Adaptive Authentication (ZeTHAA), a continuous authentication and authorization framework integrating contextual attributes, authentication strength, behavioral evidence, and retry dynamics. ZeTHAA utilizes a probabilistic risk model and dual-policy thresholds to partition outcomes into allow, step-up, and block regions, enabling precise control over security-usability trade-offs. The system introduces a global admissibility predicate to distinguish hard violations from probabilistic soft violations. Attribute importance is dynamically derived from entropy and Beta-posterior distribution, enabling robust cold-start initialization and online recalibration. ZeTHAA presents a unified composite attack surface covering credential compromise, attribute forgery, and post-grant hijacking, modeling retry behavior with exponential risk escalation and temporal decay. A large-scale synthetic dataset capturing realistic authentication flows, adversarial and temporal patterns, was used to evaluate ZeTHAA against heuristic, logistic regression, random forest, XGBoost, and isolation forest baselines. ZeTHAA produced a more expressive risk distribution and significantly higher attack detection and efficiency while minimizing user friction. ZeTHAA outperformed baseline models, with Recall and Area Under the Curve (AUC) exceeding 79% and 15.1%, respectively. F1-Score showed increases of 48%-147%, with efficiency boost of 20-65%, while reducing the cost per attack by up to 39.6%. Benchmarks against frameworks from Dasu et al. and Matiushin et al. showed a 57.5% lead in F1-Score, more than double increase in detection rate, while blocking 70.78% more attacks. Additional analysis shows that ZeTHAA provides a mathematically grounded foundation for Zero-Trust systems, aligns with NIST standards, offering improved security guarantees and adaptive enforcement. 2013 IEEE. -
Biowaste-Derived, Highly Efficient, Reusable Carbon Nanospheres for Speedy Removal of Organic Dyes from Aqueous Solutions
The current work explores the adsorptive efficiency of carbon nanospheres (CNSs) derived from oil palm leaves (OPL) that are a source of biowaste. CNSs were synthesized at 400, 600, 800 and 1000 C, and those obtained at 1000 C demonstrated maximum removal efficiency of ~91% for malachite green (MG). Physicochemical and microscopic characteristics were analysed by FESEM, TEM, FTIR, Raman, TGA and XPS studies. The presence of surface oxygen sites and the porosity of CNSs synergistically influenced the speed of removal of MG, brilliant green (BG) and Congo red (CR) dyes. With a minimal adsorbent dosage (1 mg) and minimum contact time (10 min), and under different pH conditions, adsorption was efficient and cost-effective (nearly 99, 91 and 88% for BG, MG and CR, respectively). The maximum adsorption capacities of OPL-based CNSs for BG were 500 and 104.16 mg/g for MG and 25.77 mg/g for CR. Adsorption isotherms (Freundlich, Langmuir and Temkin) and kinetics models (pseudo-first-order, pseudo-second-order and Elovich) for the adsorption processes of all three dyes on the CNSs were explored in detail. BG and CR adsorption the Freundlich isotherm best, while MG showed a best fit to the Temkin model. Adsorption kinetics of all three dyes followed a pseudo-second-order model. A reusability study was conducted to evaluate the effectiveness of CNSs in removing the MG dye and showed ~92% efficiency even after several cycles. Highly efficient CNSs with surface oxygen groups and speedy removal of organic dyes within 10 min by CNSs are highlighted in this paper. 2022 by the authors. -
Acid Orange-7 uptake on spherical-shaped nanocarbons
Acid-dyes, typically used in textile productions, could infer poisoning harmful effects on the environment as well as on human health, if not properly treated during their disposal. Henceforth, there is an absolute necessity to achieve new efficient low-cost techniques to remove these dyes from industrial chemical waste. Here, the leaves of oil palm, which are abundant in tropical countries, were used as precursor in the development of carbon nanospheres (adsorbent) to remove hazardous acid Orange-7 (AO-7) dye (C16H11N2NaO4S). The removal efficacy of spherical-shaped nanocarbons was investigated as a function of contact period, by varying their dose (0.5, 1, 1.5, 2 and 2.5mg), pH (acidic, native and basic), and initial AO-7 concentration (10, 15, 20, 25 and 30?M). Amazingly, the oil palm leavesbased carbon nanospheres removed acid-dye up to an efficiency of about 99%. Pseudo second-order kinetics governs the adsorption mechanism and the RedlichPeterson isotherm model fits well to the adsorption results, with regression co-efficient close to unity. This study suggests the importance of natural biowaste-based carbon nanoparticles in sustainable recycling, within the worldwide demanded circular economy. The Author(s) 2021.