Collaborative Intelligence for IoT: Decentralized Net security and confidentiality

Kiran Sree; Ajay; Krishan Kant Singh; Pratibha; Pavithra; Laith

doi:https://doi.org/10.54216/JISIoT.130216

International Journal of Neutrosophic Science | 2024

IDLTM-DMT: Intelligent Deep Learning based Trust Management with Decision Making Tool for Healthcare Internet of Things and Big Data Environment with Neutrosophic Set Analysis

Over the last few years development of Internet of Things (IoT) devices and communication technologies have resulted in the massive generation of health-related data. In the context of healthcare, IoT offers several advantages, including being able to observe patients very closely and using data for analytics. A major challenging issue that exists in the usage of IoT and big data in the medical field is security. As healthcare data is highly vulnerable and becomes a target for attacks, there are significant privacy issues related to the usage of big data analytics. Besides, implementing new data analysis tools and strategies for handling big data decision-making is a major issue. The capability to examine this amount of data is a significant aspect of big data in health care.  For resolving these issues, this paper presents a new intelligent deep learning-based trust management with decision making tool (IDLTM-DMT) for IoT healthcare big data environments, incorporating Neutrosophic Set Analysis (NSA). The proposed IDLTM-DMT model enables IoT devices to gather healthcare data. The IDLTM-DMT model involves a DL based bidirectional long short-term memory (BiLSTM) model for vulnerability detection and thereby identifies the malicious traffic in the Network. Hadoop MapReduce is used for handling big data and a decision-making tool using Deep Stacked Auto Encoder (DSAE) is used for the classification of diseases that exist in big data. To optimize the DSAE model's hyperparameters and improve classification performance, the Sandpiper Optimization (SPO) Algorithm is employed. Neutrosophic Set Analysis is integrated to manage the indeterminacy and inconsistency of the data, enhancing the decision-making process. Extensive experimental analysis is conducted on the EEG Eye State Dataset, with results analyzed using various performance measures. The findings indicate that the proposed method achieves improved accuracy compared to existing methods, demonstrating the effectiveness of incorporating Neutrosophic Set Analysis in IoT healthcare big data environments.

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C K Marigowda mail -

Thriveni J mail -

Gowrishankar S mail

link https://doi.org/10.54216/IJNS.240430

Volume & Issue

Vol. Volume 24 / Iss. Issue 4

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International Journal of Advances in Applied Computational Intelligence | 2025

An Effective Workload Prediction with Rnn-Lstm For Efficient Resource Autoscaling In Private Cloud Environments

The research focuses on an accurate workload prediction approach for auto-scaling resources in the Private Cloud using improved Time-Series models. Although many factors still result in dynamic workloads of cloud systems, an accurate forecast becomes vital for service quality and cost. The chapter discusses a Proactive Prediction Engine (PPE) framework using Auto Regressive Integrated Moving Average (ARIMA) and Recurrent Neural Network Long Short-Term, to forecast CPU utilization. Real-time datasets of OpenStack private cloud and Amazon AWS were used for experimental evaluation. The analyses show that the RNN_LSTM model performs far better than ARIMA by reducing the MAE and RMSE values by roughly 40 percent in each set. This has further reinforced that RNN_LSTM can model non-linearity and handle correlation issues in the workload data. Automated scaling of the instances with the Open Stack based on the predicted CPU load is made possible by the integration of RNN_LSTM prediction with OpenStack, supported by Terraform. This strategy reduces times of service outages and enables the efficient use of resources in the network. Regarding accuracy and automation, the proposed method can be a relevant solution for workload management for private cloud infrastructure. In this respect, the results support the implementation of deep learning-based predictive models to optimize the performance of autoscaling.

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Narek Badjajian mail -

Sandy Montajab Hazzouri mail

link https://doi.org/10.54216/IJAACI.070105

Volume & Issue

Vol. Volume 7 / Iss. Issue 1

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Journal of Cybersecurity and Information Management | 2024

Advanced Cyber Attack Detection Using Generative Adversarial Networks and NLP

A key difficulty in the ever-changing cybersecurity scene is the detection of sophisticated cyber-attacks. Because new threats are so much more sophisticated and difficult to detect, traditional tactics typically fail. A new technique to improving cyber-attack detection skills is explored in this study. It uses Generative Adversarial Networks (GANs) and Natural Language Processing (NLP). Using GANs' realistic data generation capabilities, possible attack paths are simulated, creating a strong dataset for training detection systems. At the same time, natural language processing (NLP) methods are used to decipher the mountain of textual information produced by cyberspace, including incident reports, communication patterns, and logs.  Our approach is based on building a fake dataset using GANs that mimics the features of advanced cyberattacks. A detection model is then trained using this dataset. Simultaneously, we improve the detection model's capacity to spot intricate and nuanced assault patterns by processing and analysing text-based data using natural language processing approaches. We use a benchmark cybersecurity dataset to test the integrated method. The experimental findings show that our GAN-NLP based detection system outperforms existing systems, which have an average accuracy of 85.3%, by a wide margin. It achieves a recall of 93.2%, precision of 92.5%, and accuracy of 94.7%. These findings prove that GANs and NLP work well together to identify complex cyberattacks. Finally, GANs and NLP together provide a potent instrument for better cyber-attack detection. A scalable solution that can adapt to the ever-changing nature of cyber threats is offered by this integrated approach, which also increases detection accuracy and efficiency. Improving the models and investigating their use in a real-world cybersecurity setting will be the primary goals of future research.

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P. Ramya mail -

Himagiri Chandra Guntupalli mail

link https://doi.org/10.54216/JCIM.140211

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

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Journal of Cybersecurity and Information Management | 2024

Optimized Group-Centric Data Routing in Heterogeneous Wireless Sensor Networks for Enhanced Energy Efficiency

Wireless Sensor Networks (WSNs) are increasingly being utilized in environments where human presence is limited or dangerous. The main goal is to enhance the data processing capabilities of these components to extend the overall lifespan of the design. Researchers have explored conventional energy-saving methods to address the energy constraints of sensor nodes. However, it became clear that traditional routing methods, specifically those based on packet grouping, were inadequate. The proposed system, known as Optimized Group-Centric Data Routing (OGC-DR), introduces an efficient method of data routing by utilizing the concept of grouping nodal points. This approach enhances data routing management by differentiating between routing within a nodal group and routing between adjacent nodal groups. Group Heading Nodes (GHN) are assigned to each group of sensory nodes according to fitness criteria. The implementation of a tree-based routing structure improves data routing by creating a "meeting-zone" and strategically selecting intermediary nodes between the source and destination node. To improve data privacy, a sender and receiver engage in an asymmetric secret-key exchange at nodal points. Data is then directed to its ultimate destination via predetermined intermediary nodes and Group Heading Nodes. Simulations of the proposed method indicate several advantages, such as lower end-to-end delays, reduced energy consumption, higher active node count, and enhanced packet delivery rates. Furthermore, it improves data privacy for all communication within the sensory architecture.

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P. Muthusamy mail -

A. Rajan mail -

R. Praveena mail -

Sundara Rajulu Navaneethakrishnan mail -

T. R. Ganesh Babu mail -

K. Sakthi Murugan mail

link https://doi.org/10.54216/JCIM.140212

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

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Journal of Cybersecurity and Information Management | 2024

Integrating Quantum Computing and NLP for Advanced Cyber Threat Detection

The exponential growth of digital data and the increasing sophistication of cyber threats demand more advanced methods for threat analysis. This paper explores the integration of quantum computing and natural language processing (NLP) to enhance cyber threat analysis. Traditional computing methods struggle to keep up with the scale and complexity of modern cyber threats, but quantum computing offers a promising avenue for accelerated data processing, while NLP provides sophisticated tools for interpreting and understanding human language, crucial for analysing threat intelligence. Our proposed framework leverages quantum algorithms for rapid anomaly detection and advanced NLP techniques for precise threat identification and analysis. The methodology includes data collection from diverse sources, pre-processing for normalization, quantum-assisted data processing using Grover's search and Quantum Approximate Optimization Algorithm (QAOA), NLP analysis with transformers and BERT-based models, and integration of findings to build comprehensive threat profiles. Experimental results demonstrate significant improvements: quantum algorithms reduced data processing time by up to 50%, NLP models achieved 92% accuracy in threat identification, and the false positive rate was reduced by 30%. These findings indicate a promising direction for next-generation cybersecurity solutions, enabling more proactive and efficient threat mitigation. Future work will focus on refining quantum algorithms, enhancing NLP models, and expanding the framework for real-time threat detection capabilities.

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P. Ramya mail -

R. Anitha mail -

J. Rajalakshmi mail -

R. Dineshkumar mail

link https://doi.org/10.54216/JCIM.140213

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

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Journal of Cybersecurity and Information Management | 2024

Trustworthy-Based Authentication Model with Intrusion Detection for IoT-Enabled Networks with Deep Learning Algorithm

In the burgeoning field of the Internet of Things (IoT), ensuring secure and trustworthy communication between devices is paramount. This paper proposes a novel Trustworthy-Based Authentication Model (TBAM) integrated with Intrusion Detection Systems (IDS) leveraging deep learning algorithms to secure IoT-enabled networks. The proposed model addresses the dual challenges of authenticating legitimate devices and detecting malicious intrusions. Specifically, we employ a Convolutional Neural Network (CNN) to analyse network traffic patterns for intrusion detection, leveraging its prowess in feature extraction and classification. Additionally, a Long Short-Term Memory (LSTM) network is utilized for continuous monitoring and anomaly detection, capturing temporal dependencies in data flows that are indicative of potential security threats. The authentication mechanism integrates a trust evaluation system that assigns trust scores to devices based on their behaviour, enhancing the model's capability to distinguish between trusted and malicious entities. Our extensive experiments on real-world IoT datasets demonstrate that the TBAM significantly outperforms traditional security models in terms of detection accuracy, false-positive rate, and computational efficiency. Specifically, our model achieves a detection accuracy of 98.7%, a false-positive rate of 1.2%, and a processing time reduction of 30% compared to baseline models. This work contributes a robust, scalable, and efficient solution to the pressing security concerns in IoT networks, paving the way for more secure and reliable IoT applications.

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M. Rajendiran mail -

Jayanthi .E mail -

Suganthi .R mail -

M. Jamuna Rani mail -

S. Vimalnath mail

link https://doi.org/10.54216/JCIM.140214

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

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Journal of Cybersecurity and Information Management | 2024

A Machine Learning Approach for Automated Detection and Classification of Cracks in Ancient Monuments using Image Processing Techniques

Stone monuments stand as enduring testaments to human history and cultural heritage, yet they are susceptible to deterioration over time. In this paper, we propose a comprehensive approach for the automated detection and classification of cracks in ancient monuments, integrating machine learning and advanced image processing techniques. Our method addresses the pressing need for efficient and objective assessment of structural integrity in these invaluable artifacts. The proposed algorithm begins with preprocessing steps, including image enhancement using adaptive histogram equalization to improve crack visibility. Subsequently, feature extraction techniques such as Grey Level Co-occurrence Matrix (GLCM) and Local Binary Pattern (LBP) are applied to capture essential characteristics of crack patterns. Central to our approach are the Back Propagation Neural Network (BPNN) and Improved Support Vector Machine (ISVM) classifiers, which are trained on the extracted features to detect and classify cracks with high accuracy. The BPNN learns complex relationships between input features and crack types, while the ISVM leverages a margin-based approach for robust classification. Through extensive experimentation on a diverse dataset of ancient monuments, we demonstrate the effectiveness of our approach in accurately identifying and categorizing cracks. The proposed method offers a scalable and objective solution for monitoring the structural health of ancient monuments, contributing to proactive conservation efforts and the preservation of cultural heritage.

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Ramani Perumal mail -

Subbiah Bharathi Venkatachalam mail

link https://doi.org/10.54216/JCIM.140215

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

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Journal of Cybersecurity and Information Management | 2024

Automated Brain Tumor Detection and Classification in MRI Images: A Hybrid Image Processing Techniques

Due to the complex structure of brain images, accurately detecting and segmenting brain tumors with Magnetic Resonance Imaging (MRI) is a difficult process. This paper suggests an automated brain tumor identification and segmentation approach employing hybrid salient segmentation with K-Means clustering and hybrid CLEACH-median filter algorithm on MRI images. The proposed method enhances the contrast and detail of MRI images using a hybrid CLEACH-median filter algorithm, and segments the most important features of the images using a hybrid salient segmentation method with K-Means clustering. The proposed method includes a stages classification step to determine the stage of the brain tumor. The findings show that the suggested approach outperformed existing methods in terms of efficiency and accuracy for both detecting and segmenting brain tumors. The suggested technique can be a useful tool for automating the detection and segmentation of brain tumors, which will help radiologists and physicians make quicker and more accurate diagnosis.

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N. Senthilkumaran mail

link https://doi.org/10.54216/JCIM.140216

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

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American Journal of Business and Operations Research | 2025

Blockchain-based smart contracts and their potential to develop some financial and banking operations in Iraq

This research explores the great potential of blockchain based smart contracts in Iraq’s financial and banking sector. It looks into how this technology can improve financial operations by automating transactions and reducing operational cost, increasing transparency, and reducing intermediaries. The research also tackles the challenges of adoption such as lack of digital infrastructure and lack of legal frameworks and cybersecurity risks. The findings show that smart contracts can lead to higher operational efficiency and more strategic flexibility for financial institutions in Iraq. Therefore, the research recommends developing digital infrastructure and establish comprehensive regulatory frameworks to support smart contracts and digital transformation in the financial and banking sector according to international standards.

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Laith Haleem Al-Hchemi mail

link https://doi.org/10.54216/AJBOR.120204

Volume & Issue

Vol. Volume 12 / Iss. Issue 2

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Journal of Intelligent Systems and Internet of Things | 2024

Collaborative Intelligence for IoT: Decentralized Net security and confidentiality

This research compares federated and centralized learning paradigms to discover the best machine learning privacy-model accuracy balance. Federated learning allows model training across devices or clients without data centralization. It's innovative distributed machine learning. Keeping data on individual devices reduces the hazards of centralized data storage, improving user privacy and security. However, centralized learning concentrates data on a server, which raises privacy and security problems. It evaluates two learning approaches using simulated data in a simple regression problem framework. Federated learning seems to be as accurate as centralized learning while protecting privacy. The paper also shows how federated learning works in popular machine learning frameworks like TensorFlow Federated. This research shows that federated learning protects privacy while producing accurate machine learning models. It challenges the idea that machine learning must constantly choose between privacy and accuracy. Empirical facts and theoretical ideas from this study advance machine learning methodology discussions. In the digital era, it promotes privacy-conscious, dispersed learning frameworks.

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Kiran Sree Pokkuluri mail -

Ajay Kumar mail -

Krishan Kant Singh Gautam mail -

Pratibha Deshmukh mail -

Pavithra G mail -

Laith Abualigah mail

link https://doi.org/10.54216/JISIoT.130216

Volume & Issue

Vol. Volume 13 / Iss. Issue 2

Details open_in_new