ASPG

NCBI Medical Data Encryption with Lossless DNA Compression

Anfal Emad Lafta , Sahar Adil Kadhum

The health information data includes reports on the patient’s condition, including addresses, names, tests, treatments, diagnoses, and medical history. It is sensitive information for patients, and all means of protection must be provided to prevent third parties from manipulation or fraudulent use. It has been discovered that DNA is now a reliable and efficient biological media for securing data. Data encryption is made possible by DNA's bimolecular computing powers. In this paper proposed a new strategy of safeguard the transfer of sensitive data over an unsecured network using cryptography with non-liner function, and DNA lossless compression to enhance security. The work gains best results in compression processes, as percentages range 75%. for character compression, the different rate ranges between 91% to 94%, and the compression rate ranges from 35% to 37%. the retrieving data with an accuracy rate up to 100% without any data loss, as well as excellent percentages within the, Compression Ratio, Compression Factor, Error Rate, Accuracy measures.

Vol. 14 Issue. 2 PP. 08-17, (2024)

Deep Layered Network Model to Classify Brain Tumor in MRI Images

Saran Raj S. , S. V. Sudha , K. Padmanaban , P. Sherubha , S. P. Sasirekha

Brain tumor is a condition due to the expansion of abnormal cell growth. Tumors are rare and can take many forms; it is challenging to estimate the survival rate of a patient. These tumors are found using Magnetic Resonance (MRI) which is crucial for locating the tumor region. Moreover, manual identification is an extensive and difficult method to produce false positives. The research communities have adopted computer-aided methods to overcome these limitations. With the advancement of artificial intelligence (AI), brain tumor prediction relies on MR images and deep learning (DL) models in medical imaging. The suggested layered configurations, i.e., layered network model, are proposed to classify and detect brain tumors accurately. The modified CNN is proposed to automatically detect the important features without any supervision and the convolution layer present in the network model enhances the training feasibility. To improve the quality of the images, some essential pre-processing is used in conjunction with image-enhancing methods. Data augmentation is adopted to expand the number of data samples for our suggested model's training.  The Dataset is portioned as based on 70% for training and 30% for testing. The findings demonstrate that the proposed model works well than existing models in classification precision, accuracy, recall, and area under the curve. The layered network model beats other CNN models and achieves an overall accuracy of 99% during prediction. In addition, VGG16, hybrid CNN and NADE, CNN, CNN and KELM, deep CNN with data augmentation, CNN-GA, hybrid VGG16-NADE and ResNet+SE approaches are used for comparison.  

Vol. 14 Issue. 2 PP. 18-32, (2024)

Deep Spectral Convolution Neural Network Based Leukemia Cancer Detection Using Invariant Entity Scalar Feature Selection

B. Divyapreethi , A. Mohanarathinam

Leukemia, a cancer that attacks human white blood cells, is one of the deadliest illnesses.   Detecting affected cells in microscopic images becomes tedious because feature variants are not predicted correctly by a hematologist. Therefore image handling techniques failed to select the importance of the features scaling counts, entities, and precise size and shape of cells presented in the microscopic image. To resolve this problem, Deep Spectral Convolution Neural Network (DSCNN) based on Leukemia cancer detection using Invariant Entity Scalar Feature Selection (IESFS) is proposed to identify the risk factor of cancer for early diagnosis. Initially, preprocessing is carried out using cascade Gabor filters. Based on Structural Cascade Segmentation (SCS), the white blood cell regions are categorized into affected and non-affected margins and verify the edges using canny edge mapping. This estimates the scaling cell size, counts, entities and angular cell projection of weights from each segmented feature region. Then find the entity relation of cell projection equivalence using Color Intensive Histogram Equalization (CIHE). After segmenting the angular vector, projection scaling is applied to correlate the entity's object scaling comparator. Then scaling features were selected using Invariant Entity Scalar Feature Selection (IESFS) by averaging the mean depth values of feature weight and trained with a deep convolution neural network for predicting max equivalence entity weights for finding the affected cells and counts in microscopic images. This improves the prediction of cancer cell accuracy as well high performance in sensitivity 92.7 %, specificity 92.3 %, and f-measure 93.6 % with redundant time complexity.  

Vol. 14 Issue. 2 PP. 33-52, (2024)

Dense-BiGRU: Densely Connected Bi-directional Gated Recurrent Unit based Heart Failure Detection using ECG Signal

Vinitha V. , V. Parthasarathy , R. Santhosh

Heart failure, a state marked by the heart's inefficiency in pumping blood adequately., can lead to serious health complications and reduced quality of life. Detecting heart failure early is crucial as it allows for timely intervention and management strategies to prevent progression and improve patient outcomes. The effectiveness of integrating ECG and AI for heart failure detection stems from AI's capacity to meticulously analyze extensive ECG datasets, facilitating the early identification of nuanced cardiac irregularities and enhancing diagnostic precision. While the current research lacks sufficient accuracy and is burdened by complexity issues. To overcome this issue, we proposed a novel Densely Connected Bi-directional Gated Recurrent Unit (Dense-BiGRU) model for accurate heart failure detection. In this work, we enhanced collected ECG signal in terms of performing multiple data pre-treatment including as denoising, powerline interference and normalization utilizing Collaborative Empirical Mode Decomposition (CEMD) algorithm, Adaptive Least Mean Square (Adaptive LMS) and min-max normalization method, respectively. Here, we utilized the LiteStream_Net layer for extracting appropriate feature from pre-processed signal. Finally, based on extracted features heart failure detection is implemented through introducing Dense-BiGRU algorithm. The proposed research is implemented using MATLAB simulation tools, and its validation is conducted through various simulation metrics including accuracy, recall, precision, F1-score, and AUC. The results of the implementation demonstrate that the proposed research surpasses existing state-of-the-art methodologies.

Vol. 14 Issue. 2 PP. 53-69, (2024)

LSTM-NAS-Net: Enhanced Brain Tumor Segmentation in MRI and CT Images using LSTM-Autoencoder-based Neural Architecture Search

Santhosh Kumar , S. P. Sasirekha , R. Santhosh

Brain Tumour (BT) a mass or a lump or a growth which occurs due to abnormal cell division or unusual growth of cells in the brain tissue. Initially, the two major types of BT are Primary BT and Secondary BT, the tumour that originate from brain is known as Primary BT and it may be cancerous or non-cancerous. The tumour the initiates from other part of the body and spreads to the brain is stated as secondary BT.  Diagnosing BT generally involves a multiple investigation method, such as MRI, CT, PET, SPECT as well as the neurological examinations and blood investigations, whereas some of the patients may need biopsies to evaluate the tumour size and stage. Here we use MRI and CT images for BT segmentation whereas these modalities play a major role in diagnosing, treating, planning and monitoring the BT patients. Moreover, the multimodal data can provide a quantitative information’s about the tumour size, shape, volume and texture. While segmenting the BT the lack of segmentation methods and the interpretability of the segmented regions are limited. To overcome this, we propose a novel LSTM autoencoder bas NAS method which is used for the extracting the BT features and these features can be fused using Contextual Integration Module (CIM) and segmented using the Segmentation Guided Regulizer (SGR) which helps to overcome the stated issues. Finally, the performance metrices are calculated by comparing with the state-of -the -art methods and our method achieves a best segmenting metrices.

Vol. 14 Issue. 2 PP. 70-86, (2024)

Modelling a Request and Response-Based Cryptographic Model For Executing Data Deduplication in the Cloud

Doddi Suresh Kumar , Nulaka Srinivasu

Cloud storage is one of the most crucial components of cloud computing because it makes it simpler for users to share and manage their data on the cloud with authorized users. Secure deduplication has attracted much attention in cloud storage because it may remove redundancy from encrypted data to save storage space and communication overhead. Many current safe deduplication systems usually focus on accomplishing the following characteristics regarding security and privacy: Access control, tag consistency, data privacy and defence against various attacks. But as far as we know, none can simultaneously fulfil all four conditions. In this research, we offer a safe deduplication method that is effective and provides user-defined access control to address this flaw. Because it only allows the cloud service provider to grant data access on behalf of data owners, our proposed solution (Request-response-based Elliptic Curve Cryptography) may effectively delete duplicates without compromising the security and privacy of cloud users. A thorough security investigation reveals that our approved safe deduplication solution successfully thwarts brute-force attacks while dependably maintaining tag consistency and data confidentiality. Comprehensive simulations show that our solution surpasses the evaluation in computing, communication, storage overheads, and deduplication efficiency.

Vol. 14 Issue. 2 PP. 87-100, (2024)

Panoptic Segmentation with Multi-Modal Dataset Using an Improved Network Model

Koppagiri Jyothsna Devi , Gouranga Mandal

For biomedical image analysis, instance segmentation is crucial. It is still difficult because of the intricate backdrop elements, the significant variation in object appearances, the large number of overlapping items, and the hazy object borders. Deep learning-based techniques, which may be separated into proposal-free and proposal-based approaches, have been frequently employed recently to overcome these challenges. The existing approaches experience information loss due to their concentration on either local-level instance features or global-level semantics. To solve this problem, this work proposes an improved dense Net ( ) that mixes instance and semantic data. The suggested  promotes the acquisition of semantic contextual information by the instance branch by linking instance prediction and semantic features via a residual attention feature integration strategy. The confidence score of each item is then matched with the accuracy of the prediction using a dense quality sub-branch that is created. A consistency regularisation technique is also proposed for the robust learning of segmentation for instance branches and the semantic segments tasks. By proving its utility, the proposed  outperforms prevailing approaches on various biomedical datasets.

Vol. 14 Issue. 2 PP. 101-114, (2024)

Drought Prediction with Feature Enhanced LSTM Model using Metaheuristic Optimization Algorithms

Leelavathy S. R. , A. Mary Mekala

The impact of drought builds on all three fronts of economy, environment, and society is devastating. Predicting its arrival and duration is highly important to arrange any sort of mitigation plans. The association of detailed relationship between multiple variables makes drought prediction a highly complex task. Especially influence of global warming, polar sea extent variations and their influence on overall ocean temperature have altered the seasonal rainfall behaviors all over the world. In the midst of it, predictions centered on the history of rainfall levels become inaccurate. The proposed system is an optimized deep learning prediction model integrating indigenous knowledge (IK) is proposed to predict the drought. IK expressed in human language is translated using fuzzy function and fed to an improved Long Short Term Memory (LSTM) model. The LSTM model hyperparameters are optimized using a hybrid of Particle Swarm Optimization (PSO) with firefly to produce the meta-heuristics algorithm which will provide the best performance in presence of integration of IK features into modern meteorological features which solves the problem of local minima in LSTM hyperparameter optimization. The performance of the proposed results were tested compared with the meteorological information gathered by the Karnataka Natural Disaster Monitoring Centre (KNDMC) for the district named Chitradurga of the Karnataka state in India. The proposed system which is  Indigenous Knowledge merged along the cross model attention network can produce at least 1.4% higher Nash–Sutcliffe model efficiency coefficient (NSE) and 30% lower Mean Absolute Error (MAE) in the prediction of Standard Precipitation Index (SPI) compared to Convolution Neural Networks (CNN) and LSTM based time series prediction models.

Vol. 14 Issue. 2 PP. 115-131, (2024)

Integrating Novel Mechanisms for Threat Detection in Enhanced Data Classification using Ant Colony Optimization with Recurrent Neural Network

Vivek alias M. Chidambaram , Karthik Painganadu Chandrasekaran

In new technologies like fog computing, edge computing, cloud computing, and the Internet of Things (IoT), cybersecurity concerns and cyber-attacks have surged. The demand for better threat detection and prevention systems has increased due to the present global uptick in phishing and computer network attacks. In order to identify irregularities and attacks on the network, which have increased in scale and prevalence, threat identification is essential. However, the community is forced to investigate and create novel threat detection approaches that are capable of detecting threats using anomalies due to the increase in network threats, the growth of new methods of attack and computations, and the requirement to ensure security measures. A novel mechanism is employed to identify threats in a data based on optimized deep learning. The main aim of this paper is the usage of data classification system based on Deep Learning (DL). The proposed mechanism employed the TCP (Transmission Control Protocol) communication protocol to extract data from loud IoT (Internet of Things) networks for the purpose of threat detection. To perform feature extraction an Ant Colony Optimization (ACO) is utilised, through Recurrent Neural Network (RNN), the attacks in data are classified and detected. Additionally, the suggested approach has been evaluated and trained using the BOUN DDoS contemporary dataset, which comprises a variety of attack types and allows for the effectiveness of the framework to be determined to compare it to previous approaches. The Findings indicate that the suggested approach achieved higher accuracy in DDoS attack identification in comparison with Traditional deep learning methods. The existing method detects the generic attack with lower efficiency however; the proposed mechanism achieves better accuracy in both the detection of the DDoS attack and the detection of regular traffic.  

Vol. 14 Issue. 2 PP. 132-147, (2024)

Fuzzy Sampling Strategy Based on IPD

V. Jemmy Joyce , K. Rebbeca Jebaseeli Edna , Evanzlin P. , Bazil Wilfred C.

If a production process modification is implemented with the intention of enhancing product quality, IPD models a suitable probability distribution for the number of sample defects. If a manufacturing process intervention is done with the purpose of increasing product quality, IPD models a suitable probability distribution for the sample's total number of defects. When the production process with the interference parameter is considered, fuzzy sampling plans based on IPD are found to be more effective than the current strategy. The Intervened Poisson distribution is used to develop single sampling strategy for such lots when there is ambiguity regarding the percentage of defective items. With fuzzy probability, the plan's operating characteristic curve is obtained. The mean of outgoing quality is derived using fuzzy parameters.  

Vol. 14 Issue. 2 PP. 148-160, (2024)

Advanced Cyber Attack Detection Using Generative Adversarial Networks and NLP

P. Ramya , Himagiri Chandra Guntupalli

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.

Vol. 14 Issue. 2 PP. 161-172, (2024)

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

P. Muthusamy , A. Rajan , R. Praveena , Sundara Rajulu Navaneethakrishnan , T. R. Ganesh Babu , K. Sakthi Murugan

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.

Vol. 14 Issue. 2 PP. 173-185, (2024)

Integrating Quantum Computing and NLP for Advanced Cyber Threat Detection

P. Ramya , R. Anitha , J. Rajalakshmi , R. Dineshkumar

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.

Vol. 14 Issue. 2 PP. 186-197, (2024)

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

M. Rajendiran , Jayanthi .E , Suganthi .R , M. Jamuna Rani , S. Vimalnath

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.

Vol. 14 Issue. 2 PP. 198-213, (2024)

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

Ramani Perumal , Subbiah Bharathi Venkatachalam

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.

Vol. 14 Issue. 2 PP. 214-238, (2024)

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

N. Senthilkumaran

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.

Vol. 14 Issue. 2 PP. 239-252, (2024)

A Hybrid Genetic Algorithm and Neural Network-Based Cyber Security Approach for Enhanced Detection of DDoS and Malware Attacks in Wide Area Networks

Anusooya .S , N. Revathi , Sivakamasundari .P , A. N. Duraivel , S. Prabu

This study addresses the growing threat of network attacks by exploring their types and analyzing the challenges associated with their precise detection. To mitigate these threats, we propose a novel cyber security approach that integrates Genetic Algorithm (GA) and neural network architecture. The GA is employed for the selection and optimization of attributes that represent DDoS and malware attack features. These optimized features are then fed into a neural network for training and classification. The effectiveness of the proposed approach was evaluated through precision, recall, and F-measure analyses, demonstrating superior detection capabilities for DDoS and malware attacks compared to existing methods. Furthermore, we introduce a hybrid approach that combines Swarm Intelligence (SI) and nature-inspired techniques. The GA is utilized to select features and reduce the dataset size, followed by the application of Discrete Wavelet Transform (DWT) with Artificial Bee Colony (ABC) to further filter irrelevant features. The results show that this hybrid approach significantly enhances the accuracy and efficiency of network attack detection in wide area networks.

Vol. 14 Issue. 2 PP. 253-262, (2024)

Multi-Fusion Biometric Authentication using Minutiae-Driven Fixed-Size Template Matching (MFTM)

B. R. Sathishkumar , K. M. Monica , D. Sasikala , M. N. Sudha

In today's digital era, ensuring robust and secure authentication mechanisms is crucial. Multi-fusion biometric authentication systems have emerged as a powerful solution to enhance security and reliability by integrating multiple biometric traits. This paper presents a novel Multi-Fusion Biometric Authentication approach using Minutiae-Driven Fixed-Size Template Matching (MFTM). The proposed method leverages the unique features of minutiae points in fingerprints and combines them with other biometric modalities, such as iris and facial recognition, to create a fixed-size template for matching. The fusion process involves extracting and normalizing minutiae points from the fingerprint, followed by their integration with iris and facial features using a robust feature fusion algorithm. The fixed-size template ensures consistency and efficiency in the matching process, addressing challenges related to template size variability and computational overhead. Extensive experiments conducted on standard biometric datasets demonstrate that the proposed MFTM approach significantly enhances authentication accuracy, reduces false acceptance and rejection rates, and provides a highly secure and scalable authentication solution suitable for various applications, including access control and identity verification. The results show an authentication accuracy of 98.7%, a false acceptance rate (FAR) of 0.2%, and a false rejection rate (FRR) of 0.5%. Additionally, the computational time for matching is reduced by 25% compared to traditional methods, highlighting the efficiency and practicality of the proposed approach.

Vol. 14 Issue. 2 PP. 263-274, (2024)

Detect and Prevent Attacks of Intrusion in IOT Devices using Game Theory with Ant Colony Optimization (ACO)

S. Aruna , Kalaivani .N , Mohammedkasim .M , D. Prabha Devi , E. Babu Thirumangaialwar

A more extensive attack surface for cyber incursions has resulted from the fast expansion of Internet of Things (IoT) devices, calling for more stringent security protocols. This research introduces a new method for protecting Internet of Things (IoT) networks against intrusion assaults by combining Game Theory with Ant Colony Optimization (ACO). Various cyber dangers are becoming more common as a result of the networked nature and frequently inadequate security measures of IoT devices. Because these threats are ever-changing and intricate, traditional security measures can't keep up. An effective optimization method for allocating resources and pathfinding is provided by ACO, which takes its cues from the foraging behavior of ants, while Game Theory provides a strategic framework for modeling the interactions between attackers and defenders. Attackers and defenders in the proposed system are modeled as players in a game where the objective is to maximize their payout. Minimizing damage by anticipating and minimizing assaults is the defender's task. The monitoring pathways are optimized and resources are allocated effectively with the help of ACO. In response to changes in network conditions, the system dynamically modifies defensive tactics by updating the game model in real time. The results of the simulation show that the suggested method successfully increases the security of the Internet of Things. Compared to 87.4% using conventional approaches, the detection accuracy increased to 95.8%. From 10.5 seconds down to 7.3 seconds, the average reaction time to identified incursions was cut in half. Furthermore, there was a 20% improvement in resource utilization efficiency, guaranteeing that defensive and monitoring resources were allocated optimally. Internet of Things (IoT) network security is greatly improved by combining Game Theory with Ant Colony Optimization. In addition to enhancing detection accuracy and reaction times, this combination method guarantees resource efficiency. The results demonstrate the practicality of this approach, which offers a solid foundation for protecting Internet of Things devices from ever-changing cyber dangers.

Vol. 14 Issue. 2 PP. 275-286, (2024)

Development of a Cryptographic Model Using Digits Classification for Cyber Security Applications

K. Jayakumar , K. Sivakami , P. Logamurthy , P. Sathiyamurthi , N. Chandrasekaran

In the digital age, the safeguarding of information through effective cybersecurity measures is paramount. This paper presents the development of a robust cryptographic model tailored for cybersecurity applications. The background underscores the increasing prevalence of cyber threats and the necessity for advanced encryption techniques to ensure data confidentiality, integrity, and authenticity. The methodology involves the design and implementation of the cryptographic model using state-of-the-art algorithms and protocols. Rigorous testing and evaluation were conducted to assess the model's performance in various cyber environments. The results indicate that the proposed model significantly enhances security, demonstrating high resistance to common cyber-attacks with an average encryption time of 0.5 seconds for a 1MB file and a decryption accuracy rate of 99.9%. The model also achieved a data integrity verification success rate of 99.8% and an overall system efficiency improvement of 45% compared to existing models. The conclusion highlights the model's effectiveness and potential for broad application in securing digital communication, offering a substantial contribution to the field of cybersecurity.

Vol. 14 Issue. 2 PP. 287-299, (2024)

Enhanced Visual Cryptographic Schemes with Essential Access Structures and Pixel-Wise Operations

M. Revathi , Devi .D , R. Menaha , R. Dineshkumar , S. Mohan

By splitting a picture into many parts, which, when reassembled, disclose the original image without requiring complicated math, visual cryptography is a strong method for protecting visual information. Problems with pixel enlargement, decreased picture quality, and restricted access structures are common with traditional visual cryptography techniques. Our proposed improved visual cryptography approach incorporates pixel-wise operations and critical access structures to solve these challenges and increase flexibility, picture quality, and security. To reconstruct a picture, our technique calls for building visual cryptographic shares based on critical access structures that specify the exact combinations of shares needed. In order to maintain the image's resolution and reduce pixel expansion, we use pixel-wise processes. By improving the peak signal-to-noise ratio (PSNR) by up to 20% compared to conventional approaches, experimental data show that our strategy greatly improves picture quality. In addition, the suggested approach guarantees that individual shares do not disclose any information on the original picture, thereby maintaining high security requirements. Finally, it is clear that the enhanced visual cryptographic system is well-suited for a wide range of uses in safe communications and data security due to its strong solution for secure picture sharing, increased picture quality, and adjustable access control.

Vol. 14 Issue. 2 PP. 300-310, (2024)

A Hybrid Deep Learning Model for Securing Smart City Networks Against Flooding Attack

Bashar Ahmed Khalaf , Siti Hajar Othman , Shukor Abd Razak , Alexandros Konios

Due to the increasing digitization of city processes, there has been a significant shift in how cities are governed and how people make their living. However, several types of attacks could target smart cities, and Flooding Attacks (FA) are the most dangerous type. It is also a major issue for many people and programs using the Internet nowadays. Security in smart cities refers to preventative measures necessary to shield the city and its residents from direct or indirect harm by attackers who try to crash the system and deny legitimate users the use of the services. Smart city security, in contrast to standard security mechanisms, necessitates new and creative approaches to protecting the systems and applications while considering characteristics like resource limitations, distributed architecture nature, and geographic distribution. Smart cities are vulnerable to several particular issues, including faulty communication, insufficient data, and privilege protection. Therefore, a hybrid CRNN model that consists of Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) algorithms is employed for the detection of Flood Attacks based on the classification of traffic data. Subsequently, the performance of the CRNN is tested and evaluated using the CIC-Bell-DNS-EXF-2021 dataset. The obtained accuracy results of the proposed CRNN model achieved in FA detection is 99.2%.

Vol. 14 Issue. 2 PP. 311-322, (2024)

Enhanced Credit Card Fraud Detection Using Deep Learning Techniques

Ola Imran Obaid , Ali Yakoob Al-Sultan

Credit card fraud is a huge challenge in the financial sector, causing huge losses every year. The problem is exacerbated by increased marketing and sophisticated fraudulent activities. This study addresses the important issue of accurate real-time detection of fraudulent transactions to minimize financial losses and enhance transactional security. The main objective of this study is to develop a comprehensive fraud detection algorithm using deep learning techniques, specially designed to address the complexity and volume of modern credit card transactions. Key contributions of this research include the presentation of a new deep learning algorithm optimized for credit card fraud detection, the integration of feature engineering techniques to improve the performance of the model, and a potential scalable solution analysis in real-time Significant improvement in proven rates. The results show that the proposed deep learning-based model achieves higher accuracy and lower false positive rate, giving financial institutions a significant advantage in protecting against fraudulent activities about the character. This study highlights the power of deep learning in reforming fraud detection systems, and lays the foundation for future developments in this important area.

Vol. 14 Issue. 2 PP. 323-333, (2024)

Credit Card Fraud Detection Model Based on Correlation Feature Selection

Ahmad Salim , Salah N. Mjeat , Daniah Abul Qahar Shakir , Mohammed Awad Alfwair

Credit card fraud is a widespread cybercrime that threatens financial security. Effective cybersecurity measures are essential to mitigate these risks. Machine learning has shown promising results in detecting credit card fraud by analyzing transaction data and identifying patterns of suspicious behavior. Feature selection is crucial in machine learning because it simplifies the model, improves its performance, and prevents overfitting. This research introduces a machine learning model designed for credit card fraud detection. The model makes use of three types of correlations. Pearson, Spearman, and Kendall, to identify features and enhance the fraud detection process. Testing on datasets yielded impressive results achieving category accuracies of 99.95% and 99.58% surpassing alternative approaches. Also, the results showed that Kendall correlation is the best among the three types of correlation in selecting attributes in all approved datasets.

Vol. 14 Issue. 2 PP. 334-342, (2024)

Enhanced Face Detection in Videos Based on Integrating Spatial Features (LBP, CS-LBP) with CNN Technique

Faqeda Hassen Kareem , Mohammed Abdullah Naser

Face detection is a crucial aspect of computer vision and image processing, in order to enable the automatic detection and identification of human faces in video streams, face detection is an essential component of computer vision and image processing. Applications for facial recognition, video analytics, security systems, and surveillance all depend on it. Face identification techniques face many obstacles and issues, such as positional fluctuations, illumination changes, resolution and scale issues, facial emotions, and cosmetics. Robust algorithms are required for efficient face detection. This field looks at the feature extraction process using a variety of techniques. These consist of the center symmetric local binary patterns (CS_LBP) approach and the local binary patterns (LBP) method. The YouTube Face database provided the video frames that we used for our study. In order to train the convolutional neural network (CNN) to detect human faces in the video and draw a bounding box around them. The experimental results of the suggested approaches show that. The accuracy rate was 94% higher with the LBP techniques. However, the CS_LBP technique showed the best level of accuracy in both face detection and face rectangle recognition, with an accuracy rate of 95%.

Vol. 14 Issue. 2 PP. 343-351, (2024)

An Efficient Algorithm for Stock Market Prediction Using Attention Mechanism

Zena Kreem Minsoor , Ali Yakoob Al-Sultan

Forecasting the stock market is a significant challenge in the financial industry due to its time series' complicated, noisy, chaotic, dynamic, volatile, and non-parametric nature. Nevertheless, due to computer advancements, an intelligent model can assist investors and expert analysts mitigate the risk associated with their investments. In recent years, substantial research has been conducted on deep learning models. Many studies have investigated using these techniques to anticipate stock values by analyzing historical data and technical indications. However, since the goal is to create predictions for the financial market, validating the model using profitability indicators and model performance is crucial. This article incorporates the attention mechanism model, incorporating attention from both feature and time perspectives. Utilize artificial neural networks. This approach addresses issues in time series prediction. The issue is the varying degrees of influence that many input features have on the target sequence. To tackle this, the method utilizes a feature attention mechanism to obtain the weights of distinct input features. An enhanced feature association relationship is achieved, whereas the data before and following the sequence exhibit a significant time correlation. An attention technique is employed to address this issue, allowing for the acquisition of weights at various time intervals to enhance robustness and temporal dependence. The system is applied to the three global SMs (TESLA, S&P500, and NASDAQ) datasets, the best enhancement results are 99% in Acc, and the better results improvement to minimize error in MSE, MAPE, and RMSE are 0.004, 0.004 and 0.01 respectively.

Vol. 14 Issue. 2 PP. 352-366, (2024)

Systematic Analysis of threats, Machine Learning solutions and Challenges for Securing IoT environment

Bharti Yadav , Deepak Dasaratha Rao , Yasaswini Mandiga , Nasib Singh Gill , Preeti Gulia , Piyush Kumar Pareek

The Internet of Things (IoT) has revolutionized our daily lives, impacting everything from healthcare to transportation and even home automation and industrial control systems. However, as the number of connected devices continues to rise, so do the security risks. In this review, we explore the different types of attacks that target various layers of IoT infrastructure. To counter these threats, researchers have proposed using machine learning (ML) and deep learning (DL) techniques for detecting different types of attacks. However, our examination of existing literature reveals that the effectiveness of these techniques can vary greatly depending on factors like the dataset used, the features considered, and the evaluation methods employed. Finally, we delve into the current challenges facing Intrusion Detection Systems (IDS) in their mission to protect IoT environments from evolving threats.

Vol. 14 Issue. 2 PP. 367-382, (2024)

Journal of Cybersecurity and Information Management

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