Babylonian Journal of Internet of Things
The Babylonian Journal of Internet of Things focuses on advancing IoT technologies. It offers a platform for sharing cutting-edge research and applications in sensor networks, architectures, data analytics, security, and various IoT fields. Committed to fostering innovation, it pushes the boundaries of IoT research and practical implementations
An Adaptive Data Security Frame Using Federated Learning and Blockchain for Privacy Protection in Smart Environments
The rapid expansion of smart environments, including smart cities, healthcare systems, and intelligent energy grids, has resulted in the generation of massive volumes of distributed and privacy-sensitive data. Conventional centralized security architectures are increasingly inadequate to guarantee confidentiality, integrity, and trust under adversarial and resource-constrained conditions. This paper proposes an Adaptive Federated Blockchain Security Framework (AFBSF) that integrates federated learning (FL) with a lightweight blockchain layer and a dynamic trust-driven cryptographic control mechanism. Federated learning enables collaborative model training without sharing raw data, while a Proof-of-Authority (PoA) blockchain provides tamper-resistant verification and transparent auditability of model updates. In addition, an adaptive trust model dynamically adjusts encryption strength and node participation according to behavioral reliability and data integrity, allowing real-time isolation of malicious or unreliable devices. Extensive experiments conducted on smart healthcare, energy, and transportation datasets demonstrate that the proposed framework outperforms conventional FL-based, blockchain-based, and existing hybrid approaches in terms of accuracy, privacy preservation, communication efficiency, and energy consumption. The results confirm that AFBSF achieves high learning performance with enhanced privacy protection, reduced attack success rate, and lower system overhead, making it a scalable and reliable security paradigm for next-generation decentralized IoT ecosystems.
Copyright (c) 2026 Najwan Abed Hasan https://creativecommons.org/licenses/by/4.0
Smart IoT Attack Detection Through AI-Optimized Routing Methods
The Internet of Things (IoT) model possesses much flexibility and mobility, hence the IoT system becomes more prone to security threats such as Distributed Denial-of-Service (DDoS) attacks due to its decentralized control, dynamic node moving, energy scarces and bandwidth limited. This development has demonstrated the capabilities of AI in providing improvements to performance of IoT network in terms of achieving faster rate, provide more throughput and achieve higher packet delivery ratio. Utilization of AI-based analytics technology with the use of adaptive methods can improve metrics such as End to End delay (E2E) and Average Received Packets (ARP), by improving response time and increasing intelligence in Intrusion Detection Systems in IoT based setups. In this paper, we use Feedforward Neural Networks (FFNN) and Convolutional Neural Networks (CNN) to detect malicious activities which can strengthen the capabilities of IDS in IoT routing. The suggested AI-optimized routing model outperforms existing models in detection accuracies, which are improved up to 82% and 85% with respective processing time of 18s and 17s. These findings demonstrate the potential to significantly improve security of IoT systems along with increasing overall network robustness and efficiency using this framework.
Copyright (c) 2025 Nabaa Ahmed Noori, Atheel Sabih Shaker, Rana Abdulrahman Lateef , Hiba Alzubaidi https://creativecommons.org/licenses/by/4.0
Investigating the Role of Deep Learning in Enhancing Communication Efficiency in 5G IoT Networks: A Comprehensive Survey
This analysis primarily examines the compatibility of IoT security and DL in offering attack prevention services for IoT devices. Consequently. The field of Internet of Things (IoT) is seeing significant growth. Consequently, there is an increasing amount of data being exchanged between cloud technologies and wireless networks to ensure smooth data flow among linked devices. Simultaneously, the Internet of Things (IoT) proves to be an exceedingly susceptible ecosystem that is susceptible to a wide range of threats. These entities has the capacity to cause extensive destruction to a country and also pose the most significant immediate economic threat. This survey essay aims to thoroughly examine the integration of 5G, IoT, and security. It is crucial to prioritise the requirement for security systems. The introduction discusses the infiltration of web vulnerabilities into devices, based on the provided information about attacks and threats. Subsequently, it demonstrates the crucial requirement for efficient security protocols. Consequently, it proceeds by examining the interdependence between DL and security, highlighting the security enhancements provided by DL to IoT. This study examines several endorsements of the DL-based technique for detecting many assaults across a diverse range of IoT environments, such as DSOS, DDoS, probing, user-to-root, remote-to-local, botnet, spoofing, and man-in-the-middle attacks, among others. In addition, the work provides a comprehensive description of deep learning techniques and the difficulties that arise when integrating deep learning-based security solutions in the context of the Internet of Things (IoT). Furthermore, the effectiveness of (DL) in enhancing IoT security has been confirmed by detailed case studies and real-life experiences. The poll also conducts a thorough analysis of security requirements in 5G IoT networks, emphasising the importance of understanding the vulnerabilities that may arise during times of crisis and chaos. This survey focuses on the comprehensive analysis of the delicate relationship between IoT, DL, and security. It provides numerous options for gaining a deeper knowledge and effectively managing the difficult security challenges in the IoT ecosystem.
Copyright (c) 2025 Zainab Ali Abbood, Haider D. Albonda, Nabaa Ahmed Noori https://creativecommons.org/licenses/by/4.0
Enhancing Digital Infrastructure Security in Education: Securing Access and the Moodle Platform
This study is to analyse the implementation of a Learning Management System (LMS), and its improvement the (LMS) at the Islamic University of Lebanon, with a particular focus on improving the security of the (LMS) as regards of keeping sensitive data and maintaining a secured reliable e-learning environment. The paper discusses best practices, and what both the technical and organizational security measures that must be adhered to in order to secure either of these platforms, should be.Technically, the research provides cloud hosting services for hosting Moodle platform, such as Microsoft Azure, which provides advanced features in security, including safeguarding against cyber threats, advanced data encryption and so on. Additionally, It showed that encrypting data-in-transit, as it moves from the source to the destination, with secure protocols helps to mitigate the risk of data breaches or interception by unauthorized third parties.The two-factor authentication is provided as the primary user account protection mechanism, according to the research recommendation from a user perspective. The guide also suggests making passwords that are a combination of upper and lower case letters and symbols, as well as not including any personal information within the password. As well as that, he also promotes application (mobile phones or email) authentication to improve the level of security of the login solution. In this study, we want to present a systematic and practical guide to enhance the security of e-learning platform in academic settings, integrating cloud-based technologies with right security mechanisms at the level of devices and the level of users.
Copyright (c) 2025 Husham Abd AL-Kareem, Hadeel M Saleh https://creativecommons.org/licenses/by/4.0
Enhancing Cyber security in Smart Cities: Challenges and Solutions
Leveraging the advanced technologies such as Internet of Things (IoT), Big Data, Artificial Intelligence (AI) and so on, smart cities are a new type of urban model to improve people's quality of life and resource allocation. By all accounts, smart cities — no matter how shiny and hyper connected they may be are on a cyber-odyssey. A lot of threats to data integrity and availability of critical infrastructure are originated from this end like privacy leakage so on. As research objective, this paper mainly intended to investigate the underlying cyber challenges in smart cities comprising vulnerabilities of IoT devices, data privacy issues and no legal codes for governing IT/communications infrastructure. It further suggests generalist solutions, such as the utilization of hybrid security hardware smart secrecy algorithms and machine learning controlled systems for video surveillance/spy camera’s or the use of block chain processes used more commonly across all fields. The study provides valuable advice for policy makers, entrepreneurs and practitioners to facilitate the development of smart cities for a safer environment and people as well as plants and animals.
Copyright (c) 2025 Wisam K. Jummar , Osama Khamees Ali , Hadeel M. Saleh https://creativecommons.org/licenses/by/4.0
GAM: Introducing a Novel Algorithm for Advanced Ultralightweight Cryptography for IoT devices
This paper introduces the Generic Algorithmic Security (GAM) algorithm, a groundbreaking advancement in ultralightweight cryptography tailored for the quantum computing era. As quantum computing chal-lenges traditional cryptographic methods, GAM emerges as a robust and adaptive solution. It employs innovative features such as Self-Healing Cryptographic Keys (SHCK) and Energy-Aware Dynamic En-cryption Scaling (EDES), ensuring resilience and optimising encryption based on real-time energy and computational constraints. Our study reveals that GAM outperforms existing algorithms like SIMON and SPECK in speed, energy efficiency, and update flexibility. Designed for resource-constrained environments like IoT devices and wearable technology, GAM offers quantum-proof security while maintaining opera-tional efficiency. We delve into its technical architecture, detailing its encryption and decryption processes, and conduct a thorough security analysis showcasing GAM's exceptional adaptability to current and future cyber threats. The results demonstrate GAM’s superior performance, achieving a 20% increase in encryp-tion speed and a 15% improvement in energy efficiency compared to SIMON and SPECK. Additionally, GAM’s update flexibility scored 30% higher, indicating its proactive adaptability to evolving threats. These findings highlight GAM’s potential as a future-proof solution, providing robust protection and operational efficiency in diverse digital environments. GAM represents a significant leap forward in cryptography, offering a future-proof solution to safeguard digital information against the challenges posed by quantum computing, underscoring the importance of innovative cryptographic solutions in securing tomorrow's digital landscape.
Copyright (c) 2025 Ghada Al-Kateb https://creativecommons.org/licenses/by/4.0
Integrating AI-Driven Deep Learning for Energy-Efficient Smart Buildings in Internet of Thing-based Industry 4.0
The integration of Industry 4.0 technologies has paved the way for rapid advancements in smart, energy-efficient buildings. This research focuses on optimizing energy consumption in IoT-enabled infrastructures through the application of data-driven modeling techniques. A comparative analysis is conducted using several machine learning and deep learning models, including Random Forest (RF), Gradient Boosting (GB), Deep Neural Networks (DNN), and Artificial Neural Networks (ANN). These models are trained and validated using real-world datasets, with appropriate preprocessing methods applied to enhance data quality. Evaluation metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Scatter Index (SI) are used to measure performance. The findings suggest that RF and GB models strike a practical balance between accuracy and computational efficiency, while DNN delivers the highest predictive accuracy but demands significantly more processing power.
Copyright (c) 2025 Mohamed C. Ghanem , Said Salloum https://creativecommons.org/licenses/by/4.0
Artificial Intelligence of Things: A Review
The integration of Artificial Intelligence (AI) and the Internet of Things (IoT), referred to as the Artificial Intelligence of Things (AIoT), has rapidly evolved into a transformative technological paradigm influencing sectors from smart cities to healthcare, agriculture, and beyond. This review explores the convergence of AI and IoT, highlighting the benefits, challenges, and socio-technical implications of their fusion. While AIoT systems promise operational efficiency, sustainability, and enhanced decision-making capabilities, they also pose pressing ethical concerns, especially around data privacy, security, and the societal impact of automation. Through a structured review of thirty recent scholarly contributions, this paper identifies major research themes, gaps in the literature, and opportunities for interdisciplinary innovation. The study emphasizes the need for balanced development—ensuring that technological advancements are accompanied by robust policy frameworks and a nuanced understanding of their social consequences. Ultimately, the paper argues for a more holistic and human-centered approach to AIoT innovation to ensure its responsible and equitable integration into society..
Artificial Intelligence of Things (AIoT)
Copyright (c) 2025 Khder Alakkari , Bushra Ali https://creativecommons.org/licenses/by/4.0
Interoperability in Internet of Things: Taxonomies and Open Challenges
This research investigates the existing taxonomies of interoperability within the Internet of Things (IoT), focusing on the critical gaps and challenges that impede seamless integration across various IoT systems, particularly in the healthcare sector. It addresses the key issue of disparate standards and protocols that complicate interoperability, utilizing a comprehensive analysis of qualitative and quantitative data, including case studies, technical specifications, and user experiences. The research identifies significant barriers such as data silos, inconsistent protocol adoption, and the lack of standardized frameworks, highlighting a need for cohesive solutions that foster interoperability. The findings reveal that enhancing interoperability can lead to improved patient outcomes, greater operational efficiency, and reduced costs, thereby underscoring the importance of standardized communication between devices and systems in healthcare. The implications of this study extend beyond technical advancements, suggesting that addressing interoperability challenges could catalyze a paradigm shift towards more integrated, patient-centered healthcare delivery models. By proposing actionable solutions, this research not only contributes to the academic discourse on IoT interoperability but also offers practical insights for stakeholders aiming to optimize the use of IoT technology in health-related applications.
Copyright (c) 2025 Toufik MZILI, Mourad Mzili, Saif Islam Bouderba, Ahmed Abatal, Widi Aribowo, Adarsh Kumar Arya https://creativecommons.org/licenses/by/4.0
An Extensive Examination on IOT and Industrial IOT: Attacks, Security, Detection Methods, Blockchain Solutions and Challenges
The Internet of Things (IoT) has consistently expanded in both the quantity of devices implemented and the variety of applications in which these devices are used. They exhibit significant variations in terms of their dimensions, processing capabilities, storage capacity, and energy consumption. The IoT has facilitated the ongoing digitization of society in several ways during the last decade. The IoT is an extensive network of interconnected smart gadgets that exchange data via web channels. The security aspect of IoT is of utmost importance due to its fast proliferation as a new technological paradigm, since it may include safety-critical operations and the digital storage of confidential information. Regrettably, the foremost obstacle that arises when using IoT technology is security. Consequently, enhancing the security of IoT devices is now the primary focus for manufacturers and researchers. There is a significant amount of literature that covers various challenges related to the topic and offers viable solutions. However, the majority of current research lacks a comprehensive viewpoint on assaults inside the IoT. Therefore, the purpose of this survey is to create a framework that will assist researchers in classifying attacks in a taxonomy based on different factors, including attack domains, threat types, execution methods, software surfaces, IoT protocols, device properties, adversary locations, and levels of information damage caused by the attacks.
Copyright (c) 2025 Karthik Kumar Vaigandla https://creativecommons.org/licenses/by/4.0
Developing IoT Performance in Healthcare Through the Integration of Machine Learning and Software-Defined Networking (SDN)
In this era of rapid growth of the Internet of Things (IoT) in healthcare, the networking solution must be robust and efficient for smooth data transfer and the network. It is very well established that Deep Learning (DL) is a subset of Machine Learning (ML) and has shown great promise in enhancing network performance through intelligent data analysis and making smart decisions. The wide deployment of Wireless sensor networks and Mobile ad hoc networks (MANET) is why they are used in healthcare applications, as they offer low-cost deployment and real-time data exchange. Nevertheless, WSNs suffer limitations in energy availability due to their inherent constraints. However, the energy consumption in a WSN routing is a significant problem because of resource limitations. Integrating ML in a Software Defined Networking (SDN) framework shows the possibility of network resource optimization and a sustainable network architecture. Typically, conventional routing annoys most about shortest path algorithms, where nodes are used unevenly, and critical nodes die early from battery depletion. This work proposes a new DRL-based algorithm for maximizing traffic distribution and maintaining energy consumption equilibrium across the nodes in WSNs to improve their lifetime. The model proposes dynamically changing routing paths to preserve the nodes’ loading with a limited power supply system to persist the network lifespan and thus improve efficiency. However, the approach adds some hops to the average packet transmissions per hop. Still, it delays the first node’s energy depletion long enough to provide sustained network operability in IoT-driven healthcare environments. The experimental results validate the effectiveness of the proposed method in furthering the network functionality and reliability for data transmission, thereby making it a viable solution for the next generation of IoT healthcare networks.
Copyright (c) 2025 Murtadha Shujairi https://creativecommons.org/licenses/by/4.0
Integrated A Robust Intelligent System For Secure Network
An increasing number of assaults on network security that are related to the Internet are caused in malicious software. Firewalls, intrusion detection systems (IDS), and proxy servers that check for viruses are examples of safety technology that have been used by organizations that value safety. Not all high-priced safety features are easy to use. Having only one safety mechanism in place is insufficient to protect users' private information on privacy networks due to the fact that multiple safety schemes exhibit distinct traits, particularly hostile and DoS attacks. Integrating intrusion detection subsystems with different approaches to network security protection is detailed in this article. The subsystem employs AI techniques to detect unusual actions taken by known attackers and threats using the patterns recorded in the system's memory. Conversely, in order to prevent spyware documents from accessing the network, the proposed Intelligent Network Security System which contents intelligent web filter ,intelligent networks intrusion detection systems ,intelligent firewalls ;could detect the malicious URL and websites.
Intelligent networks-intrusion detection
Copyright (c) 2025 Tamara Saad Mohamed , Saad Mohammed khalifah https://creativecommons.org/licenses/by/4.0
Leveraging the Internet of Things, Remote Sensing, and Artificial Intelligence for Sustainable Forest Management
Sustainable forest management is vital for addressing climate change, biodiversity loss, and deforestation. Human-induced stresses on forest ecosystems demand innovative approaches to ensure long-term health and productivity. This study explores how cutting-edge technologies, including the Internet of Things (IoT), remote sensing, and artificial intelligence (AI), enhance sustainable forest management practices. Researchers reviewed 196 studies published between 2021 and 2024 from IEEE Xplore Digital Library, MDPI, Taylor & Francis, ScienceDirect, Frontiers, Springer, SAGE, Hindawi, Nature, Wiley Online Library, and Google Scholar. The findings highlight IoT devices like drones, enabling real-time data collection on temperature, humidity, soil moisture, and tree growth, facilitating continuous forest monitoring. Remote sensing technologies, utilizing satellite imagery and aerial surveys, deliver high-resolution data for large-scale forest assessments, including forest cover changes, biomass estimation, and early detection of illegal logging. When integrated with AI, these tools enhance predictive modeling, data analysis, and decision-making, leading to more effective forest management strategies. The study also identifies challenges such as data security concerns, bandwidth limitations, interoperability issues, and high costs. Despite these barriers, IoT, remote sensing, and AI present transformative potential for improving forest resilience, carbon sequestration, and biodiversity conservation. These technologies are crucial in preserving forest ecosystems and mitigating climate change impacts by advancing real-time monitoring, optimizing resource allocation, and enabling data-driven decisions.
Copyright (c) 2025 Guma Ali , Maad M. Mijwil , Ioannis Adamopoulos , Jenan Ayad https://creativecommons.org/licenses/by/4.0
An Author-Centric Framework Error Minimization in Scholarly Recommender System (Acfemsr)
In the current landscape of computational innovations, the internet hosts a vast repository of publications sourced from various channels. Consequently, researchers encounter unprecedented challenges in identifying publications relevant to their research interests. Navigating through the multitude of options provided by search engines is not only impractical but also prone to selection errors – termed Recommendation Error (RE). These experiences underscore the need for novel research avenues. Previous studies have proposed numerous recommendation frameworks exclusively for scientific paper recommendations. However, many of these approaches have been plagued by RE, thereby compromising the integrity of recommendation systems. Recommendation Errors typically arise from underutilization of key features in the recommendation process. To mitigate Recommendation Errors, this study leverages publicly available metadata features, including Title, Abstract, Author(s), and keywords. Feature vectors for each candidate paper (CP) and the paper of interest (POI) are computed using CountVectorizer, and cosine similarity formula is employed to identify similar papers suitable for recommendations. The effectiveness of the proposed framework is evaluated using Error evaluation metrics such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). Finally, the proposed Framework is compared against two previous Baseline approaches. The evaluation results demonstrate that the proposed approach exhibits lower Recommendation Errors compared to its baseline counterparts. Additionally, this research highlights the Author(s) feature as the most influential among the four features utilized by the Proposed Framework.
Copyright (c) 2024 A.H. Zaharaddeen, Nurra Mukhtar https://creativecommons.org/licenses/by/4.0
Integrating IoT Technologies with Open Educational Resources (OER) for Enhanced Learning Experiences During the COVID-19 Pandemic
Consequently, this research focuses on the blended application of IoT with OER within college students learning in the context of the COVID-19 outbreak. As more education is moving to the online setting, this study examines if there is an improvement in learning achieved through the integration of open educational resources with Internet of Things-based educational appliances. Two groups of students were surveyed: those studying traditionally in face-to-face classes before the pandemic and those studying online during the pandemic. In the 2019 survey, out of the 912 students who responded, while in the 2021 survey, 1018 students responded. They found that an overwhelming number of students across the two groups felt that OER particularly the open online courses and the open-access textbooks were more advantageous during the pandemic. The current study revealed that distance learners benefited most from OER as supplementary sources of material as opposed to traditional learners where OER was deemed relevant for examinations and tests. Each group, however, mooted some concerns about the nature and range of OER that is available in the market. The findings show that IoT adaptation in combination with OER can solve the challenges students face in different learning conditions while increasing education access and quality during crises.
Copyright (c) 2023 Yasser Taha Alzubaidi , Safa Hussein Oleiwi https://creativecommons.org/licenses/by/4.0
Construction a virtual central network for all Iraq's fire station
Fire stations are crucial in Iraq for protecting human life, property, and vital economic and religious infrastructure. In this paper, a design for a centralized virtual network that brings together all fire stations in Iraq is proposed. The network consists of 18 stations, with each station representing a governorate in Iraq. When a fire occurs in a certain area, citizens can request the service by entering their personal information and the location of the event, which is sent to the centralized network that transfers it to the appropriate station in the area. If the station is not available in the area, the request is sent to the nearest other station to fulfil the request as quickly as possible. This virtual communication network allows for better coordination and simplifies data sharing and communication between different stations. The results showed that the centralized network design contributes to fast response times, rapid response to requests and rapid deployment of fire engines to the incident site. Fire stations can also increase overall efficiency by improving coordination, allocating resources more effectively and monitoring situations in real time. Thanks to the centralization of data, all stations have access to the same set of information, enhancing decision-making, especially in the case of large-scale incidents. Also, the network design is characterized by a high level of security.
Copyright (c) 2024 Wasnaa Hadi Ghasab, Ahmed Adnan Hadi , Alaa.G.K. Alshami , Ahmed Dheyaa Radhi, Rusul Mansoor Al-Amri, Ayad Hameed Mousa https://creativecommons.org/licenses/by/4.0
Revolutionizing IoT Security in the 5G Era with the Rise of AI-Powered Cybersecurity Solutions
This study involves a detailed analysis of how AI can be employed in improving IoT security. However, with the surge of IoT, these systems which require device data interchange through cloud and wireless connections have become vulnerable to various types of cyber threats. Storms, hacking attempts and similar contingencies create high risks, financial losses and even the impact on people. This survey investigates IoT, and 5G Technology along with Cybersecurity in terms of new emerging issues and challenges and states the necessity of strong security measures. From the case of the IoT environment, the paper explores the following cyberattacks; DDoS, probing, U2R, R2L, botnet, spoofing, and MITM attacks. It assesses Artificial Intelligence-based approaches such as ML and DL that enhance IoT security by identifying, responding, and blocking these threats. Further, this survey discusses the issues related to deploying AI-based security solutions in the IoT context, and the particular focus is made on aspects such as scalability, real-time reconfigurability, and privacy concerns. Real-life cases as well as research findings are provided to support the readiness of AI in protecting IoT environments. This paper also discusses the 5GIoT network security requirements and concerns identifying new methodologies for preventing new security threats in this advanced network. This survey provides a complex picture of the interconnection between IoT, AI, and cybersecurity to help researchers and practitioners design novel complex security solutions for the newest IoT systems.
Copyright (c) 2023 Juan H. Namdar, Janan Farag Yonan https://creativecommons.org/licenses/by/4.0
Wheelchair Control Using Finger Gestures IoT-Based Arduino
This paper presents the development of a wheelchair control system using Arduino and wireless finger gestures. The system incorporates various components such as Arduino Uno and Arduino Nano boards, ultrasonic and IR sensors, an LN298 motor driver module, motors, an IR flame/fire sensor, a buzzer, batteries, and HC12 wireless modules. The flex sensor is utilized to detect finger movements and gestures, while the ultrasonic and IR sensors provide obstacle detection capabilities. The Arduino boards process the sensor data and interpret the finger gestures, mapping them to specific commands for wheelchair control. The LN298 motor driver module interfaces with the motors to enable the execution of the desired motions based on the recognized gestures. IR flame/fire sensor monitors fire, and the buzzer provides audible feedback. The HC12 wireless modules facilitate wireless communication between the Arduino boards. The paper aims to create an intuitive and efficient control system for a wheelchair, allowing users to navigate their surroundings using wireless finger gestures while incorporating obstacle detection and air quality monitoring features.
Copyright (c) 2024 Rana Ali Salim https://creativecommons.org/licenses/by/4.0
Enhancing IoT Security to Leveraging ML for DDoS Attack Prevention in Distributed Network Routing
DDoS attacks have become much more frequent especially with the situating of IoT technologies. These attacks are based on compromising weaknesses within the IoT network and focuses on communication links used as paths to flood systems with large volumes of traffic and thereby cause system breakdowns. DDoS attacks lean on centralized control mechanisms and less available bandwidth of IoT infrastructures and the clandestine mobility of the nodes. Traditional measures of security like password and user accounts protection are typically insufficient to counteract such threats. This paper focuses on the system architectures of DDoS attack detection solutions in the IoT networks to evaluate their ability to counter the attack. More specifically, it focuses on the function of machine learning systems, which are designed to analyze previous attacks, in order to stop new attacks. The comparisons with three Machine Learning algorithms—Support Vector Machines, Random Forest, Decision Trees are done with relation to their capability to classify invasion attempts within distributed IoT network routing system. It also assesses the methods for optimising these models such as determination of hyperplane, the clustering of data point as well as the tree structure. Common performance parameters such as confusion matrix, F1 measure, and AUC-ROC are used to ensure the efficiency, effectiveness and to handle the cases with intricate unbalanced datasets to enhance the IoT network Security.
Intrusion Detection Systems(IDS)Distributed Network Routing
Copyright (c) 2023 Abdulazeez Alsajri, Amani Steiti , Hasan Ahmed Salman https://creativecommons.org/licenses/by/4.0
IoT of Healthcare Innovation Solutions for Predictable Virus Detection
The evolution of health care systems from old models to more personalized frameworks facilitates easier diagnosis, treatment, and monitoring of patients, benefiting numerous individuals. Individuals receive remote treatment and care, which is essential for some during the global crisis exemplified by Predictable Virus Detection. This manuscript, A medical platform features an architecture reliant on middleware and database support for individuals with Coronavirus, primarily serving three user categories. The administrator is categorized into two user groups: doctors and patients. The doctor has an app that questions through the patient so he knows the patient that is being visited and extracts the health identity from him, and he questions the patient for sending him an OTP in the event that the patient does not have a mobile screen or an Internet connection. Alternatively, if QR inquires whether his laptop is intelligent and connected to the Internet. The individual will gain access to the system subsequent to the doctor's examination. The patient will conduct a self-examination using his equipment and present the results to his doctor. The physician is able to provide a prescription each time he transmits new readings. If the prescription is accurate, retain it and escalate the dosage; do not incorporate an additional dose. Physicians will utilize the prescription interface to transmit the prescription for cloud authentication and acquire an encrypted QR code, which will subsequently be provided to the medication receiver. The patient is afforded the opportunity to examine medicine information through the recipient's application. The entitlement to access QR protected cloud data is perpetual. The pharmacy can dispense the medication just as prescribed until the QR code's expiration date. The initiative aims to enhance access to healthcare facilities for patients and physicians, while adhering to GDPR regulations.
Copyright (c) 2024 Sara Salman Qasim https://creativecommons.org/licenses/by/4.0
Integrating computer vision, web systems and embedded systems to develop an intelligent monitoring system for violating vehicles
This study aims to design and develop a smart system based on artificial intelligence and deep learning techniques to monitor and track violating vehicles, especially those driving in the opposite direction or illegally on the roads. The study relies on the use of the YOLOv8 algorithm, which has proven to be highly efficient compared to other algorithms such as RCNN, to determine the location of the target vehicle in real time. The proposed system seeks to integrate computer vision, web development, and embedded systems technologies into one integrated and efficient system, which includes connecting to a database to update the location in real time, in addition to an alarm system using Arduino technology to send notifications about the monitored condition. By implementing the system, the researchers hope to reduce traffic violations and reduce accidents related to driving.
Copyright (c) 2023 Waseem Ghafori Yass , Mohammad Faris https://creativecommons.org/licenses/by/4.0
Monitoring Pollution in Smart Cities Based on Arduino and IoT
This study offerings an innovative solution for checking pollution in smart cities using the Internet of Things (IoT) and Arduino-based sensors. As urban areas face increasing air pollution levels, the need for effective monitoring systems becomes necessary. This paper focuses on creating a scalable, secure IoT architecture to collect, analyze, and manage air quality data in real-time. The system utilizes MQTT for secure data transmission, and machine learning algorithms for predictive analysis. A model is developed using Arduino sensors to measure carbon dioxide, butane gas, temperature, and humidity. The outcomes indicate the system’s ability to effectively monitor pollution, notify users through mobile applications, and provide actionable insights for pollution management. Future work will improve the system’s abilities, including expanded scalability and additional environmental parameters.
Copyright (c) 2024 Sura Ali Hobi, Davood Akbari Bangar https://creativecommons.org/licenses/by/4.0
RNN-Based Framework for IoT Healthcare Security for Improving Anomaly Detection and System Integrity
The rapid rise of the Internet of Things greatly benefited the healthcare sector by opening up new ways of monitoring patients and ingeniously collecting data on disease management. However, this increased connectivity and health system data interchange introduce many critical security vulnerabilities that are more likely to discredit highly sensitive patient information along with system integrity. The paper investigates only one critical IoT health care security issue, for which a new security framework based on RNNs was used to investigate enhancements in the threat detection and response. This approach modelled network traffic and device behavior sequentially for anomaly and potential breach detection using RNNs. Hence, we introduce the RNN-based model combined with an inclusive security architecture, including data encryption, mechanisms of authentication, and monitoring tools in real time. Experimental results prove that our RNN-based framework significantly improves malicious activity detection and reduces false positives compared to traditional security solutions. The proposed model would provide a strong, scalable, and adaptable security solution tailored to the IoT healthcare environment dynamics. These findings could indicate how RNNs can enhance security in IoTs and provide new ways in which better and more secure healthcare systems can be developed.
Copyright (c) 2024 S. Rajaprakash, C. Bagath Basha , M. Nithya , K. Karthik , Nitisha Aggarwal , S. Kayathri https://creativecommons.org/licenses/by/4.0
Evaluating the Effectiveness of Machine Learning-Based Intrusion Detection in Multi-Cloud Environments
In the dynamic landscape of cloud computing, multi-cloud environments have emerged as the prevalent ones due to offering a host of advantages including redundancy, flexibility, and increased security. These multi-cloud environments can, however, be vulnerable to stealthy cyber-attacks, thereby making it a challenging job to find efficient intrusion detection systems (IDS). The efficiency of the Machine Learning-based Intrusion Detection System in multi-cloud environments has been evaluated using a 1-D Convolutional Neural Network to classify network traffic based on the UNSW-NB15 dataset. The dataset included a wide variety of network features, and it was decided that it could be used directly after converting the categorical variables into their numerical forms and scaling them with MinMaxScaler. This was used for training and testing the 1D-CNN model, which had attained an accuracy of 84.02% during training and 82.79% on validation. Further, the performance of the model is elicited with metrics that include precision at 80.91%, recall at 82.79%, and an F1-score at 81.28%, showing its capability in identifying network intrusions effectively. The small difference between the accuracy of training and validation shows that overfitting was minimal; thus, the model will generalize well when applied to unseen data. This research underlines the possibility of using deep learning techniques, and particularly CNNs, for intrusion detection in complex multi-cloud infrastructures. Probably future work would be optimizing the model through hyperparameter tuning, using additional sources of data, and even exploring much more sophisticated architectures that will further improve the accuracy of intrusion detection.
Copyright (c) 2024 Hussein Jawad Kadhim AL Masoodi https://creativecommons.org/licenses/by/4.0
CNN- based intrusion detection software for network operating system environment
Cybersecurity represents an important challenge specific to digital technology in the modern world, and is of vital importance for reducing or even preventing the impact of cybercrime. The Linux operating system is designed as open-source software that includes some features of software tools intended for network security and cybersecurity systems, such as intruder detection and penetration testing. With these tools in Linux, we need a special system to constantly detect intrusions into connected network devices. This research presents a method for detecting intrusion attacks based on analyzing the natural behavior of the system by building a special convolutional network to achieve this goal. The classification and detection results of the proposed convolutional neural network were compared with the regular machine learning method (SVM), with feature selection by correlation for both methods. Same datasets were used to train and test each of CNN and SVM. Some metrics were determined to evaluate the performance of classification and prediction models for a specific type of regular attacks, DoS and BOT attacks, where both SVM and CNN obtained an accuracy of 85.58% and 95.59%, respectively.
Copyright (c) 2024 Sundos A. Hameed Alazawi , Huda Abdulaali Abdulbaqi https://creativecommons.org/licenses/by/4.0
Metaverse Revolution in Higher Education: The Rise of Metaversities and Immersive Learning Environments
This study examines the Metaverse's impact on higher education and finance, introducing the concept of "Metaversities" and virtual financial services. We conducted a randomized controlled trial with 400 undergraduate students over two semesters, comparing an integrated metaverse-enhanced learning approach to traditional digital methods. Results show significant improvements in the metaverse group: 15% higher exam scores, 41% more time spent learning, 19.5% better knowledge retention, and 110% more peer interactions. The study also explores virtual financial solutions offered by banks, potentially democratizing access to financial services. Our results demonstrate significant improvements in academic performance, engagement, knowledge retention, collaboration, and student satisfaction among participants in the metaverse-enhanced learning group. The experimental group showed a 15% increase in average final exam scores, spent 41% more time on learning activities, and demonstrated 19.5% higher knowledge retention one month after course completion compared to the control group. Collaboration metrics also improved substantially, with the experimental group engaging in 110% more peer interactions and producing higher quality group projects. However, challenges such as practical application, legislation needs, cybersecurity, and digital divide issues are identified. While the Metaverse shows promise in expanding access to quality education and financial services, careful management is crucial to prevent exacerbating inequalities. This research contributes empirical evidence on the Metaverse's effectiveness in higher learning and insights into its financial sector implications. Our findings suggest that the Metaverse offers unprecedented opportunities for innovation in education and finance, but stakeholders must address critical challenges to ensure equitable and beneficial implementation.
Copyright (c) 2023 Pushan Kumar Dutta, Denis Sekiwu, Omega John Unogwu, Ankar Tersoo Catherine, Amr Hosny Helal https://creativecommons.org/licenses/by/4.0
Encoding IoT for Patient Monitoring and Smart Healthcare: Connected Healthcare System Fostering Health 6.0
Internet of Things (IoT) has revolutionized the way healthcare and patient monitoring was done until now. The Next Generation era that this evolved to is called Health 6.0 which is secured by IoT devices, this smart play in healthcare landscape gives the swat-the-clock monitoring inkling fostering detection controls for them to turn antibiotics at razor-edge level precision results enhancing efficiency and effectiveness box between disease endowment/therapy alternatives. The obvious examples is that IoT devices like wearables or medical equipment will be able to send health data in real time back to healthcare providers so they can leverage the additional information for better diagnosis, treatment as well as care management. This ecosystem provides the plumbing and tools to flow data between different healthcare systems so physicians can quickly access all of the information they need at point-of-care. IoT can also be used in patient monitoring, so that healthcare providers have data-supported decisions on care processes and treatment strategies; by predicting the health problems before reaching critical levels. This paper deeply dives in the futuristic vision of Smart Hospitals and their potential to reshape the healthcare 6.0 landscape.
Copyright (c) 2023 Pushan Kumar Dutta, Bhupinder Singh , Sahil Lal, Manmeet Kaur Arora, Anjali Raghav, George Benneh Mensah, Hussein Alkattan https://creativecommons.org/licenses/by/4.0
An Energy Consumption Monitoring and Control System in Buildings using Internet of Things
In this paper a system to monitor and control the electricity consumption by means of Internet of Things (IoT) technology in buildings is presented. Wi-Fi smart plugs act as sensors to provide real-time power consumption data on a per device basis. This information is passed to a cloud-based platform via MQTT for further analysis. This mobile app is used to access the building's energy usage data in both real-time and historical, as well allowing control over connected devices from anywhere. The architecture of the system, i.e. hardware components, software infrastructure as well data flow are explained in details. The results show that the IoT-based smart plugs are providing accurate power consumption data with a low deviation. By providing electricity consumers with a greater level of detail on how and when they consume their power, this system can enable them to consciously exercise control over their energy consumption; which may result in reductions in cost as well as levels of inefficiency from the amount used.
Copyright (c) 2023 Pushan Kumar Dutta, Sayed M. El-kenawy , Guma Ali, Klodian Dhoska https://creativecommons.org/licenses/by/4.0
Analyzing and Comparing Global Sustainability Standards: LEED, BREEAM, and PBRS in Green Building arch article topic
Since the year 2000, a heightened environmental awareness has led to the emergence of global trends in forefront countries, prompting the need for standardized practices in the environmental building industry. This paper explores various experiments on buildings, demonstrating trends through examinations and tests conducted by governmental or private organizations. Notable among these trends are the standards set by global evaluation systems such as BREEAM, LEED, PBRS, and SSBS. The paper highlights the recent adoptaion of Arab standards in Abu Dhabi, positioning the emirate as a pioneer in sustainable development. The research focuses on the city of Kirkuk, where building regulations lack consideration for environmental standards. The study aims to identify applicable international standards, emphasizing the importance of residential standards in comparison to other criteria. The World Green Building Council's universal standards for existing buildings are discussed, with the recognition that these standards may need customization to align with the local environment and architectural characteristics of Kirkuk. The paper delves into the methodology used, employing a theoretical framework, analytical methodology, and a deductive approach to formulate recommendations tailored to meet local standards. The Abu Dhabi experience with the Pearl Rating System is explored, outlining its three evaluation stages and the specific criteria for building assessment. A comparison between sustainability standards reveals disparities in evaluation criteria, emphasizing the challenge in establishing global standards. The study calls for a transparent and globally applicable evaluation basis, considering discrepancies among diverse countries and their respective tools. The paper concludes by underlining the importance of establishing local standards, serving as a benchmark for quality and operational efficiency within the real estate market. It also emphasizes the crucial role of existing residential buildings in proposed solutions for sustainable development, highlighting their significance in local markets.
Copyright (c) 2024 Noah Mohammed Saleh , Ali Mohammed Saleh, , Raed A. Hasan , Jafar Keighobadi, Omer Khalil Ahmed, Zeyad K. Hamad https://creativecommons.org/licenses/by/4.0
A Real-Time Intrusion Detection System for DoS/DDoS Attack Classification in IoT Networks Using KNN-Neural Network Hybrid Technique
As more devices are connected to the Internet through the Internet of Things (IoT), there are huge security challenges. One of the major problems is Distributed Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks. These attacks floods networks with useless traffic, disrupting IoT services. There is a need for better security measures to handle this. Intrusion Detection Systems (IDS) is used to find suspicious activities, but many of them can't keep up with new types of attacks in real time. This study focuses on creating an efficient real time hybrid framework that uses the Kth Nearest Neighbor (KNN) algorithm and dense neural networks. This proposed framework aims to identify and categorize DoS/DDoS attacks in real time through the utilization of a simulation model and MQTT-IoT-IDS2020 dataset and compared with existing frameworks, our proposed framework excels in accuracy, precision, and recall.
Copyright (c) 2024 Aisha Ibrahim Gide , Abubakar Aminu Mu’azu https://creativecommons.org/licenses/by/4.0