An Ensemble Boosting Algorithm based Intrusion Detection System for Smart Internet of Things Environment

Rami

doi:https://doi.org/10.54216/JISIoT.130222

Journal of Intelligent Systems and Internet of Things | 2024

The Healthcare IoTs as a Paradigm Shift in Healthcare Management, Patient Treatment, and Healthcare Data Processing

When it comes to hospital administration, patient care, and medical data analysis, the Healthcare Internet of Things (HIoT) is nothing short of a paradigm revolution. We dive into this new paradigm to examine its far-reaching effects and revolutionary possibilities in the healthcare system. The context is established by introducing HIoT as a game-changing development in healthcare. Using the IoT to network several devices, this model paves the way for real-time patient monitoring, streamlined inventory management, and integrated telemedicine. The healthcare industry as we know it will be transformed by HIoT as it strives to improve resource allocation, simplify operations, and provide proactive patient care. Our investigation includes a thorough appraisal of how HIoT will affect many facets of medical treatment. We use many research approaches and quality indicators for this evaluation. We may evaluate the viability and scalability of HIoT solutions by testing them in experimental settings that mimic real-world healthcare settings. To provide a precise depiction of the healthcare system, dataset environments use well maintained medical data sources. The performance and efficacy of HIoT technologies may be evaluated using measurable criteria such as sensitivity (0.94), specificity (0.89), F1-Score (0.91), ROC-AUC (0.95), and cost savings ($150,000). To determine the relative importance of each part of the HIoT ecosystem, researchers undertake "ablation studies. Our findings provide a clear picture of the disruptive potential of HIoT. Better patient outcomes may be ensured via early interventions thanks to the improved accuracy (0.92), efficiency (9.2), and satisfaction (9.2) provided by the suggested HIoT technique for patient monitoring. When healthcare and telemedicine are combined, the success rate of remote consultations increases to 95%, response times decrease to 15 minutes, and more people have access to medical treatment.

groups

Amit Kumar Chandanan mail -

Prabha Rani Sikdar mail -

C. Raja mail -

Saiyed Faiayaz Waris mail -

Manoj Kumar .T mail -

Kiran Bhopate mail

link https://doi.org/10.54216/JISIoT.130220

Volume & Issue

Vol. Volume 13 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2024

Harnessing Artificial Intelligence for Enhanced Efficiency in Academic Writing and Research

In the recent past, there has been a surge in the use of artificial intelligence (AI) in the development of smart technologies for the purpose of improving efficiency in writing academic papers and conducting researches. However, the potential of using AI in the improvement of scholarly processes has not been optimally realized due to low awareness and visibility of the tool among the users. In this respect, this paper aims to describe the following tools of AI which can be applied in the research process including literature search and manuscript preparation. To assess the AI technology, the current literature in form of case studies was reviewed and this included the automated literature search engines, citation management software, natural language processing tools and data analysis tools. It also reveals that AI approaches can also help in decreasing the amount of time spent in article and data search, citation, citation management, and even in the generation of quality publications. This essay also examines the ethical issues of using artificial intelligence in research and any bias that may be present. In conclusion, it is necessary to underline that AI can be useful in improving the results of learning processes. But it is crucial that the researchers are trained well and are put in a position to doubt the outcome produced by the AI. Thus, the purpose of the paper is to discuss how AI is being used in academia at the moment and what could be done to expand its use in the future.

groups

Alaa A. Qaffas mail

link https://doi.org/10.54216/FPA.160209

Volume & Issue

Vol. Volume 16 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2024

Speaker Identification in Crowd Speech Audio using Convolutional Neural Networks

Crowd speaker identification is the most advanced technology in the field of audio identification and personal user experience which researchers have extensively focused on, but still, science hasn’t been able to achieve high results in crowed identification. This work aims to design and implement a novel crowd speech identification method that can identify speakers in a multi speaker environment, (two, three, four and five speakers). This work will be implemented through two phases. The training phase is the Convolutional Neural Network (CNN) training and testing phase. Through this phase, the training will be implemented on data generated via the Combinatorial Cartesian Product approach. This approach uses two primary processes, the Computation of the Cartesian product process and combinatorial selection process. The second phase is the prediction phase. The aim of this phase is to check the CNN trained in the first phase, through testing it on new crowed audios than the data that the CNN was trained on in the first phase, these new crowded audios exist in the Ghadeer-Speech-Crowd-Corpus (GSCC) dataset, which is a new database designed through this work. Compared to the state-of-the-art speaker identification in multi speaker environment approaches, the results are impressive, with a recognition rate of 99.5% for audio with three speakers, 98.5% for music with four speakers, and 96.4% for audio with five speakers.

groups

Ghadeer Qasim Ali mail -

Husam Ali Abdulmohsin mail

link https://doi.org/10.54216/FPA.160208

Volume & Issue

Vol. Volume 16 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2024

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

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%.

groups

Bashar Ahmed Khalaf mail -

Siti Hajar Othman mail -

Shukor Abd Razak mail -

Alexandros Konios mail

link https://doi.org/10.54216/JCIM.140222

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2024

Enhanced Credit Card Fraud Detection Using Deep Learning Techniques

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.

groups

Ola Imran Obaid mail -

Ali Yakoob Al-Sultan mail

link https://doi.org/10.54216/JCIM.140223

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2024

Credit Card Fraud Detection Model Based on Correlation Feature Selection

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.

groups

Ahmad Salim mail -

Salah N. Mjeat mail -

Daniah Abul Qahar Shakir mail -

Mohammed Awad Alfwair mail

link https://doi.org/10.54216/JCIM.140224

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2024

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

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%.

groups

Faqeda Hassen Kareem mail -

Mohammed Abdullah Naser mail

link https://doi.org/10.54216/JCIM.140225

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2024

An Efficient Algorithm for Stock Market Prediction Using Attention Mechanism

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.

groups

Zena Kreem Minsoor mail -

Ali Yakoob Al-Sultan mail

link https://doi.org/10.54216/JCIM.140226

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2024

Innovative Approaches to Bank Security in India: Leveraging IoT, Blockchain, and Decentralized Systems against Loan Scams

This research paper explores the significant impacts of multiple loan fraud on Indian banks and financial institutions, emphasizing the resulting bad debts and financial losses. The issue is exacerbated in the real estate sector, where influential developers exploit system vulnerabilities to secure multiple loans using the same collateral. Consumers also face challenges in accessing credit due to these fraudulent practices. The study underscores the need for enhanced regulatory measures and internal controls within financial institutions. Additionally, it introduces IoTBlockFin, a decentralized system that integrates block chain and IoT technologies to securely assess customer reliability and mitigate fraud. IoTBlockFin's Advanced Proof of Work (APOW) mechanism, combined with IoT data for real-time monitoring, offers superior security, latency, and cost-effectiveness compared to centralized systems, as demonstrated by experimental results.

groups

Akhtar Hasan Jamal Khan mail -

Syed Afzal Ahmad mail

link https://doi.org/10.54216/JISIoT.130221

Volume & Issue

Vol. Volume 13 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2024

An Ensemble Boosting Algorithm based Intrusion Detection System for Smart Internet of Things Environment

An influx of smart spaces that are now connected to the IoT network has increased new forms of cyber threats; thus, a need for more effective IDS to deal with these complex cyber threats. Traditional security measures cannot solve the modern problem of protecting IoT devices as they are a complex and homogeneously distributed network. Advancements and development of Artificial intelligent (AI) and machine learning technologies have provided new hope to make more reliable IDS. Our study presents Particle Swarm Optimization integrated Light-Weight Gradient Boosting Machine, abbreviated as LGBM-PSO in which, the PSO algorithm is applied for hyper parameters optimization in the model training. Based on the ensemble methodology, a new model for network intrusion detection is proposed in this study to improve the accuracy of the technique proposed. As for the current study project, the “DS2OS” dataset was employed to execute the suggested task. All of the data obtained from the traces of the smart devices placed in a smart home environment are incorporated in this dataset. The IDS model comprises several stages, one of which comprises data preprocessing that entails data cleaning, normalization, and encoding of network traffic data. Feature selection and dimensionality reduction are used which leads to the optimization of the dataset in this case. The core of the model comprises four classifiers: The compared models are Decision Tree (DT), LGBM-PSO, Light Gradient Boost Machine (LGBM), and Extreme Gradient Boost (XGB). Each of these classifiers can be combined with a majority voting ensemble method to increase the reliability of the predictions. The suggested model's accuracy that is LGBM-PSO is the highest with a value of 99.89%. The corresponding figures for the training data are 99.79%. Stand on the testing data proving the efficiency and stability of the algorithm. The use of the ensemble approach is superior especially when using a complex model like LGBM-PSO in the field of intrusion detection. As a result, high accuracy, optimized time, and effective threat identification ensure that it is a useful tool in strengthening security in the different applications.

groups

Rami Baazeem mail

link https://doi.org/10.54216/JISIoT.130222

Volume & Issue

Vol. Volume 13 / Iss. Issue 2

Details open_in_new