A Hybrid Intelligence-based Deep Learning Model with Reptile Search Algorithm for Effective Channel Estimation in massive MIMO Communication Systems

Nallamothu; Penke

doi:https://doi.org/10.54216/JCIM.150116

International Journal of Neutrosophic Science | 2025

New approach for subbisemiring of bisemiring is applied to complex cubic anti neutrosophic set and its extension

We construct and analyze the concept of complex cubic anti neutrosophic subbisemiring (ComCANSBS). We analyze the important properties and homomorphic aspects of ComCANSBS. For bisemirings, we propose the ComCANSBS level sets. A complex neutrosophic subset of bisemiring Ⓢ is represented by the symbol Γ if and only if each non-empty level set R(&weierp;,κ), where R) = |&real;⊤Γ ·eiθ z}|{&image;⊤Γ ,z}|{&real; גΓ ·eiθz}|{&image; גΓ ,z}|{&real;Γ ·eiθz}|{&image;Γ ,&real;⊤Γ ·eiθ&image;⊤Γ ,&real; גΓ · eiθ&image; גΓ,&real;Γ · eiθ&image;Γ ) is a ComCANSBS of Ⓢ. Let Υ be a ComCANSBS of bisemiring Ⓢ. If and only if Υ is a ComCANSBS of Ⓢ × Ⓢ, then Γ is a ComCANSBS of bisemiring Ⓢ. Let Γ be the strongest complex anti neutrosophic relation of bisemiring Ⓢ. We show that homomorphic images of all ComCANSBSs are ComCANSBSs, and homomorphic pre-images of all ComCANSBSs are ComCANSBSs. There are examples given to illustrate our results.

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Aiyared Iampan mail -

Murugan Palanikumar mail

link https://doi.org/10.54216/IJNS.250209

Volume & Issue

Vol. Volume 25 / Iss. Issue 2

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Journal of Cybersecurity and Information Management | 2025

Modelling a Dense Convolutional Model for Crop Yield Prediction Using Kernel Computation

Crop yield prediction is performed based on crop, water, soil and environmental parameters, which is now a potential research field. Machine-learning approaches are extensively utilized for extracting significant crop features. ML approaches help in handling the issues over the crop prediction process. Some essential issues like linear and non-linear data mapping among the crop yielding values and input data need to be analyzed. However, the performance relies on the quality of extracted features. Here, a novel dense convolutional Network model with a kernel is designed to resolve the challenges identified. Based on feature learning, the anticipated model predicts the crop yielding value and linearly maps the crop yielding output with a nominal threshold value. Here, MATLAB 2020a simulator is used and various metrics like precision, accuracy, recall, F1-score, MAPE, RMSE and value are evaluated with various approaches. The model shows a superior trade-off than other approaches and intends to give better prediction accuracy. The model preserves the original data without disturbing the overall incoming values.

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Bhavani Vasantha mail -

G. Pradeepini mail

link https://doi.org/10.54216/JCIM.150108

Volume & Issue

Vol. Volume 15 / Iss. Issue 1

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Journal of Cybersecurity and Information Management | 2025

HEART SAVIOUR: A Dense Network Four Way Transformer Network for Remote Heart Disease Monitoring using Medical Sensors for Blockchain Cloud Assisted Healthcare

Internet of Things (IoT) with Cloud Computing (CC) offers seamless connectivity in the healthcare environment which provide remote monitoring and diagnosis to the patients based on their health status. However, remote healthcare environment faced with security, privacy, bandwidth, and latency constraints which can be addressed by adopting blockchain, CC, and Edge Computing (EC) with medical IoT applications. In this research, HEART SAVIOUR model is developed which ensures real time remote heart disease analysis using Deep Learning (DL) and Transformer based method. The propounded research was tested and trained on the Hungarian and Cleveland dataset from the UCI repository. Initially, the patient data are passed to the edge gateway which are pre-processed in three folds which includes missing value replacement, noise reduction, and data normalization respectively. Within the edge gateway, the pre-processed data are subjected to encryption for guaranteeing secure communication using Binary Search Encryption Algorithm (BSEA). The encrypted sensitive data is then passed to the cloud server for automated remote heart disease analysis using Dense Nested Four Way Transformer Network (DNFW-Net). The analyzed results are securely stored in the block chain and based on the request raised by the healthcare specialists the automated and reliable reports are generated and securely provided to the remote patients. We have validated the proposed research on five performance metrics with 10% to 100% data distribution in which the proposed work achieves achievable performance than the existing works. The inclusion of edge computing, encryption, and block chain technologies with advanced AI algorithms, we ensure superior remote heart disease detection performance than the prior works.

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S. Geetha mail -

M. Vigenesh mail -

R. Santhosh mail

link https://doi.org/10.54216/JCIM.150109

Volume & Issue

Vol. Volume 15 / Iss. Issue 1

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Journal of Cybersecurity and Information Management | 2025

Optimizing Task Offloading in Vehicular Network (OTO): A Game Theory Approach Integrating Hybrid Edge and Cloud Computing

In VANETs, user equipment (UE) schedules tasks by prioritizing them based on urgency and resource availability to ensure timely and efficient communication and processing. Effective task scheduling and resource allocation in VANET are crucial for maintaining low latency, high reliability, and optimal resource utilization for real-time vehicular communications. However, existing works often face limitations such as inadequate handling of dynamic network conditions, leading to increased latency and suboptimal resource usage. In this paper, we introduced a precise model by proposing Optimizing Task Offloading in Vehicular Network named as OTO framework. Initially, UEs are clustered using an Improved Fuzzy Algorithm (IFA) to reduce latency and energy consumption, with optimal clusters determined by a cluster validity index. Clustering considers distance, location, RSSI, link stability, and trust values, and cluster heads (CH) chosen based on distance, trust, and link stability. Following this, tasks from UE are classified using a Hybrid Deep Learning (HDL) algorithm, with LiteCNN for classification into emergency and non-emergency tasks and LiteLSTM for scheduling to reduce the weight matrix and overfitting. Dual scheduling based on task length, delay sensitivity, QoS, priority, resource consumption, and queue length reduces execution time and latency. Finally, the scheduled tasks are allocated to the optimal edge server based on task load, resource availability, waiting time, and distance using the RL-based Multi-agent Deep Reinforcement Learning (MA-DRL) algorithm, where edge servers act as sellers and users as buyers, reducing latency due to high convergence. In order to, evaluate and prove the efficacy of proposed OTO framework, we performed comparative analysis in terms of several performance metrics where our proposed OTO model outperforms other existing approaches.

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Mohanapriya .M mail -

V. Anusuya mail -

K. Aravindhan mail -

N. Krishnaveni mail -

R. Santhosh mail -

D. Gowthami mail

link https://doi.org/10.54216/JCIM.150110

Volume & Issue

Vol. Volume 15 / Iss. Issue 1

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Journal of Cybersecurity and Information Management | 2025

TBBS-TO: Trustable Blockchain and Bandwidth Sensible-based Task Offloading and Resource Allocation in Cloud-IoT Network

The ability to facilitate high-performance task offloading while maintaining participant confidence is crucial, but not essential, to Cloud-Edge-Network (CEN) computing due to the geographic distribution and operation by various parties. Additionally, conflicts of interest may arise among the highly dynamic and diverse CEN members who provide resources. This study proposes a collaborative task offloading framework for CEN computing, called Trustable Block Chain and Bandwidth Sensible-based Task Offloading (TBBS-TO) and resource allocation empowered CEN. The E-PEFT consensus algorithm for block chain in task offloading optimizes resource allocation and task execution by dynamically adjusting consensus parameters based on environmental factors and performance feedback. Moreover, in our work for alleviating heterogeneous issues IoT users are mobility aware clustering is performed using Bi-directional Clustering Algorithm based on Local Density (BCALoD). In this work, block chain is essential to BC-CED's core functions, such as task delegation, resource utilization brokerage, and bandwidth sensible resource allocation. By modifying the block chain consensus procedure, TBBS-TO distinguish itself from other solutions by enabling participants to reach a consensus on task offloading. To achieve this, we formulate the offloading problem by considering both network performance and the computational capabilities of potential nodes. Using Multi-agent Double Deep Q-Network (MA-DDQN) based technique, TBBS-TO allow participants to compete for the right to produce a block by evaluating offloading policies and selecting the most effective one for the next period. Additionally, dynamically bandwidth sensible resource allocation is performed by considering significant parameters. Comprehensive testing on a commercial block chain platform has shown that TBBS-TO outperforms existing solutions in task offloading and blockchain maintenance.

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K. Saravanan mail -

R. Santhosh mail

link https://doi.org/10.54216/JCIM.150111

Volume & Issue

Vol. Volume 15 / Iss. Issue 1

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Journal of Cybersecurity and Information Management | 2025

A Multiclass Attack Classification Framework for IoT Using Hybrid Deep Learning Model

In recent years, the Internet of Things (IoT) has emerged as one of the most significant concepts in numerous facets of our contemporary way of life. Nonetheless, addressing the concerns over the IoT's security presents the most significant obstacle to the widespread adoption of this technology. Using an Intrusion Detection System (IDS) to detect malicious activity in the networks is one of the most essential things that can be done to solve the security concerns posed by the IoT. Hence, a Deep Learning-based IDS (DL-IDS) model is designed for the multi-class classification of attacks in the IoT networks. This DL-IDS model includes data preprocessing, feature extraction, feature selection, and classification processes. The Bot-IoT and IoT-23 datasets are used as input for the research model. In preprocessing, the datasets are normalized, and the missing data are replaced. After preprocessing, the features are extracted using the Convolutional Neural Network (CNN) architecture. The features selection process is performed from the extracted features by implementing the Quantum-based Chameleon Swarm Optimization (QCSO) algorithm, which selects features from the datasets. Based on these features selected, the multi-class classification is carried out using the Deep Belief Network (DBN) for each attack presented in the datasets. The classification performance is performed individually for both datasets and evaluated using accuracy, detection rate, precision, and f1-scores. The performances of the proposed DL-IDS model are compared with the other models analyzed from the literature survey discussed in this work. The average scores obtained using the IoT-23 data set include 99.45% accuracy, 99.47% detection rate, 99.66% f1-scores, and 99.85% precision. For the Bot-IoT data, the average scores are 99.49% accuracy, 99.52% detection rate, 99.70% f1-score, and 99.88% precision.

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Saraladeve .L mail -

Chandrasekar .A mail -

Nithya .T mail -

Mohamed Imtiaz .N mail -

Kalaiarasi .S mail -

Balaji Sampathkumar mail -

Rajendran Thanikachalam mail -

Maria Arockia Dass .J mail

link https://doi.org/10.54216/JCIM.150112

Volume & Issue

Vol. Volume 15 / Iss. Issue 1

Details open_in_new

Journal of Cybersecurity and Information Management | 2025

Unveiling the Hidden: Exploring Challenges in Dark Web Investigation Using Measurement Sensors

This study is centered on the possible methods to analyze and investigate dark web crimes by technical and non-technical users such as law enforcement agencies. Also, the study focuses on learning anonymity procedures used by malicious actors to hide their identity on the dark web and identify the challenges to making a network-level investigation. The other objective is to study the proven methods to determine the hidden services directory (HSDir), active marketplaces, crawling and indexing of the dark web pages. Methods: A Proof of Concept (PoC) experiment explores multi-level anonymity techniques used by malicious actors. Level one involves using a commercial VPN to hide system details, and level two employs a hypervisor, MAC changer, proxy server, and the Tor network. The results reveal the complexities of Tor anonymity and provide insights into the methods employed by malicious actors. The proposed methodology offers a comprehensive approach to understanding and investigating dark web crimes, combining website fingerprinting, open-source intelligence, and threat intelligence data. Findings: Investigation teams face challenges as the proven and tested methods of previous works in this study, such as network-level bulk datasets and webpages fingerprinting dataset analysis, are technology-intensive and non-technical users will face challenges. Usage of Anonymous tools and techniques used at the host level (VM), Mac change, VPN and Tor network complicates the investigation to track and trace the activities. Tor browser has hopped through random nodes to anonymize the connection before connecting to the marketplace. MAC Changer will change the Mac address flashed on the network card by the device manufacturer to anonymize the system-level details. Novelty: Identified the requirement of a comprehensive and novel methodology that is adaptable to investigate dark web crimes by the technical and non-technical teams of law enforcement an agency is proposed in this study. This methodology includes website fingerprinting, OSINT and threat intelligence data collected from various sources. This methodology shall evolve with phase-wise steps of proven techniques such as crawling, indexing, attribute-based analysis, and dataset creation to obtain actionable intelligence proposed in this paper to investigate and eradicate dark web crimes.

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Vinod Babu Bollikonda mail -

KVD Kiran mail

link https://doi.org/10.54216/JCIM.150113

Volume & Issue

Vol. Volume 15 / Iss. Issue 1

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Journal of Cybersecurity and Information Management | 2025

Enhancing Network Congestion Control: A Comparative Study of Traditional and AI-Enhanced Active Queue Management Techniques

The issue of multi-access services based on the rapidly expanding Internet affects communication networks and creates congestion problems in buffers, which require effective control. Buffers have previously been managed using simple algorithms such as Droptail (DT), but this method has proven to have many setbacks, such as large queue delays and frequent occurrences of global synchronizations and shutdowns. To overcome these problems, the Active Queue Management (AQM) technique was introduced, including algorithms like Random Early Detection (RED). AQM techniques predict and discharge packets or label them before the buffer reaches its capacity to prevent congestion. In recent work, these algorithms have been enhanced with deep reinforcement learning to achieve improved network performance. This paper intends to present an evaluation of different studies conducted by researchers on congestion control methods. More importantly, it aims to compare the various findings, highlight the prospects of the different methods amid their weaknesses, and discuss future research opportunities within this critical domain of network management.

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Mohammed Qassim Matrood mail -

Majid Hamid Ali mail

link https://doi.org/10.54216/JCIM.150114

Volume & Issue

Vol. Volume 15 / Iss. Issue 1

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Journal of Cybersecurity and Information Management | 2025

Digital Forensic Investigation of an Unmanned Aerial Vehicle (UAV): A Technical Case Study of a DJI Phantom III Professional Drone

Globally, drones have become increasingly popular. While there are legitimate uses of drones, there are also complaints of increasing deployment for illegal activities. With the increasing caseloads of unethical, illegal, and criminal deployments, investigators have become more interested in conducting forensic examination of drones, to reconstruct events and provide answers to key investigative questions. This technical case study is a digital forensic investigation of a DJI Phantom III Professional drone to obtain possible evidential artifacts. The paper outlines the procedures and tools that were employed to acquire, preserve, analyse, and present digital evidence from the drone and its associated accessories. The paper also discussed the current state of the body of knowledge and the challenges in the field of drone forensics. An outcome of this study was the development of a drone forensic investigation model, inspired by the DFRWS Framework. The result of this investigation produced valuable evidential artifacts deconstructing vital flight information and other parameters of the drone, obtained in a forensically sound and legally defensible manner.

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Robinson Tombari Sibe mail -

David Bekom mail

link https://doi.org/10.54216/JCIM.150115

Volume & Issue

Vol. Volume 15 / Iss. Issue 1

Details open_in_new

Journal of Cybersecurity and Information Management | 2025

A Hybrid Intelligence-based Deep Learning Model with Reptile Search Algorithm for Effective Channel Estimation in massive MIMO Communication Systems

Channel estimation poses critical challenges in millimeter-wave (mmWave) massive Multiple Input, Multiple Output (MIMO) communication models, particularly when dealing with a substantial number of antennas. Deep learning techniques have shown remarkable advancements in improving channel estimation accuracy and minimizing computational difficulty in 5G as well as the future generation of communications. The main intention of the suggested method is to use an optimal hybrid deep learning strategy to create a better channel estimation model. The proposed method, referred to as optimized D-LSTM, combines the power of a deep neural network (DNN) and long short-term memory (LSTM), and the optimization process involves the integration of the Reptile Search Algorithm (RSA) to enhance the performance of  deep learning model. The suggested hybrid deep learning method considers the correlation between the measurement matrix and the signal vectors that were received as input to predict the amplitude of the beam space channel. The newly proposed estimation model demonstrates remarkable superiority over traditional models in both Normalized Mean-Squared Error (NMSE) reduction and enhanced spectral efficiency. The spectral efficiency of the designed RSA-D-LSTM is 68.62%, 62.26%, 30.3%, and 19.77% higher than DOA, DHOA, HHO, and RSA. Therefore, the suggested system provides better channel estimation to improve its efficiency.

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Nallamothu Suneetha mail -

Penke Satyanarayana mail

link https://doi.org/10.54216/JCIM.150116

Volume & Issue

Vol. Volume 15 / Iss. Issue 1

Details open_in_new