Framework Of Internet Research Articles

CRSF: An Intrusion Detection Framework for Industrial Internet of Things Based on Pretrained CNN2D-RNN and SVM

CRSF: An Intrusion Detection Framework for Industrial Internet of Things Based on Pretrained CNN2D-RNN and SVM

Integrating Predictive Big Data Analytics with Behavioral Machine Learning Models for Proactive Threat Intelligence in Industrial IoT Cybersecurity

This paper introduces a comprehensive framework for industrial Internet of Things (IoT) cybersecurity, integrating multiple algorithms to enhance threat intelligence. The proposed framework encompasses five key algorithms, each addressing specific aspects of data preprocessing, time series analysis, predictive analytics, and behavioral machine learning. The Data Preprocessing and Integration algorithm refines raw IoT data through a meticulous 20-step process, ensuring high-quality input for subsequent analyses. The Time Series Analysis algorithm delves into temporal patterns, while the Random Forest algorithm focuses on predictive analytics for proactive threat detection. The LSTM Ensemble algorithm extends the analysis into behavioral machine learning, capturing temporal dependencies and detecting anomalies. The Weighted Average Ensemble combines outputs from predictive analytics and behavioral models, leveraging their correlation for enhanced threat intelligence. An ablation study dissects the individual contributions of each algorithmic component, shedding light on their specific impacts. The results highlight the significance of each step, guiding optimizations for improved performance. The proposed framework outperforms existing methods in various performance metrics, showcasing its potential as a robust solution for proactive threat intelligence in complex industrial environments. This framework stands at the forefront of industrial IoT cybersecurity, offering a holistic and adaptive approach to address evolving threats. The ablation study enhances the transparency and understanding of the framework, contributing to its continuous refinement and effectiveness in safeguarding critical industrial systems.

RS-RLNC: A Reinforcement Learning-Based Selective Random Linear Network Coding Framework for Tactile Internet

RS-RLNC: A Reinforcement Learning-Based Selective Random Linear Network Coding Framework for Tactile Internet

Trustworthy Federated Graph Learning Framework for Wireless Internet of Things

As computational power has increased rapidly in recent years, deep learning techniques have found widespread use in wireless internet of things (IoT) networks, where they have shown remarkable results. In order to make the most of the data contained in graphs and their surrounding contexts, graph intelligence has seen extensive use in a wide variety of tailored wireless applications. However, the sensitive nature of client data poses serious challenges to conventional customization approaches, which depend on centralized graph learning on globe graphs. In this work, we introduce federated graph learning, dubbed FGL, that is capable of producing accurate personalization while still protecting clients' anonymity. To train graph intelligence models jointly based on distributed graphs inferred from local data, we employ a trustworthy model updating technique. In order to make use of graph knowledge beyond the scope of dynamic interplay, we present a trustworthy graph extension mechanism for incorporating high-level knowledge while yet maintaining confidentiality. Six customization datasets were used to show that with excellent trustworthy protection, FGL achieves 2.0% to 5.0% lower errors than the state-of-the-art federated customization approaches. For ethical and insightful personalization, FGL offers a potential path forward for mining distributed graph data.

AID4I: An Intrusion Detection Framework for Industrial Internet of Things Using Automated Machine Learning

AID4I: An Intrusion Detection Framework for Industrial Internet of Things Using Automated Machine Learning

Toward a new semantic framework for internet of things-aware business process discovery

A fundamental obstacle to the automatic detection of business processes is the lack of modeling concepts that explicitly express Internet elements as components of a business process model. In order to present a framework for discovering business process models from sensor data, we studied and compared the work associated with process modeling in an intelligent environment. In this article, we presented a semantic framework built on our Extended BPMN for IoT ontology model.

A New Task Scheduling Framework for Internet of Things based on Agile VNFs On-demand Service Model and Deep Reinforcement Learning Method

Recent innovations in the Internet of Things (IoT) have given rise to IoT applications that require quick response times and low latency. Fog computing has proven to be an effective platform for handling IoT applications. It is a significant challenge to deploy fog computing resources effectively because of the heterogeneity of IoT tasks and their delay sensitivity. To take advantage of idle resources in IoT devices, this paper presents an edge computing concept that offloads edge tasks to nearby IoT devices. The IoT-assisted edge computing should meet two conditions, edge services should exploit the computing resources of IoT devices effectively and edge tasks offloaded to IoT devices do not interfere with local IoT tasks. Two main phases are included in the proposed method: virtualization of edge nodes, and task scheduling based on deep reinforcement learning. The first phase offers a layered edge framework. In the second phase, we applied deep reinforcement learning (DRL) to schedule tasks taking into account the diversity of tasks and the heterogeneity of available resources. According to simulation results, our proposed task scheduling method achieves higher levels of task satisfaction and success than existing methods.

Device Agent Assisted Blockchain Leveraged Framework for Internet of Things

Blockchain (BC) is a burgeoning technology that has emerged as a promising solution to peer-to-peer communication security and privacy challenges. As a revolutionary technology, blockchain has drawn the attention of academics and researchers. Cryptocurrencies have already effectively utilized BC technology. Many researchers have sought to implement this technique in different sectors, including the Internet of Things. To store and manage IoT data, we present in this paper a lightweight BC-based architecture with a modified raft algorithm-based consensus protocol. We designed a Device Agent that executes a novel registration procedure to connect IoT devices to the blockchain. We implemented the framework on Docker using the Go programming language. We have simulated the framework on a Linux environment hosted in the cloud. We have conducted a detailed performance analysis using a variety of measures. The results demonstrate that our suggested solution is suitable for facilitating the management of IoT data with increased security and privacy. In terms of throughput and block generation time, the results indicate that our solution might be 40% to 45% faster than the existing blockchain.

Intelligent Trust based Security Framework for Internet of Things

Trust models have recently been proposed for Internet of Things (IoT) applications as a significant system of protection against external threats. This approach to IoT risk management is viable, trustworthy, and secure. At present, the trust security mechanism for immersion applications has not been specified for IoT systems. Several unfamiliar participants or machines share their resources through distributed systems to carry out a job or provide a service. One can have access to tools, network routes, connections, power processing, and storage space. This puts users of the IoT at much greater risk of, for example, anonymity, data leakage, and other safety violations. Trust measurement for new nodes has become crucial for unknown peer threats to be mitigated. Trust must be evaluated in the application sense using acceptable metrics based on the functional properties of nodes. The multifaceted confidence parameterization cannot be clarified explicitly by current stable models. In most current models, loss of confidence is inadequately modeled. Esteem ratings are frequently mis-weighted when previous confidence is taken into account, increasing the impact of harmful recommendations. 
 In this manuscript, a systematic method called Relationship History along with cumulative trust value (Distributed confidence management scheme model) has been proposed to evaluate interactive peers trust worthiness in a specific context. It includes estimating confidence decline, gathering & weighing trust parameters and calculating the cumulative trust value between nodes. Trust standards can rely on practical contextual resources, determining if a service provider is trustworthy or not and does it deliver effective service? The simulation results suggest that the proposed model outperforms other similar models in terms of security, routing and efficiency and further assesses its performance based on derived utility and trust precision, convergence, and longevity.

LSSDNF: A Lightweight Secure Software Defined Network Framework for Future Internet in 5G–6G

As information technology advances quickly, so does the 5G–6G network management system, which is moving toward greater integration, decentralization, diversity, and intelligence. As flexibility is a crucial criterion for 5G–6G network architecture, we use the Software Defined Network (SDN) paradigm to make the programmability more flexible. Due to their ability to replace the current TCP/IP architecture with one that separates the control plane and data plane, software-defined networks have gained much popularity. However, they are susceptible to routing attacks. Therefore, this work proposes Lightweight Security Framework that combines blockchain technology with Software-Defined Networking (LSSDNF) to address this problem. The proposed framework adds the routing data that the controller withheld to the multichain blockchain. Here, a mininet network simulator is used to model the proposed framework. The data transfer rate or network throughput, bandwidth variation, and jitter have all been used to assess the performance of single-controller-SDN networks and multi-controller-SDN networks. The results demonstrate that the proposed framework performs better than the conventional single-controller-SDN architecture in terms of throughput, bandwidth fluctuation, and jitter.

Lead Separation and Combination: A Novel Unsupervised 12-Lead ECG Feature Learning Framework for Internet of Medical Things

The development of healthcare industry, especially Internet of Medical Things (IoMT), has generated considerable unlabeled electrocardiogram (ECG) signals. This article proposes a new unsupervised feature learning method for these unlabeled 12-lead ECGs, a type of 12-channel 1-D time series. Based on contrastive predictive coding (CPC), it considers the characteristics of 12-lead ECGs and develops novel lead-separation CPC (LSCPC) and lead-combination CPC (LCCPC). Specifically, LSCPC captures intralead features for each lead, while LCCPC combines all the leads and explores interlead relationships. Furthermore, a fusion model of LSCPC and LCCPC generates final representations. The Physikalisch-Technische Bundesanstalt (PTB)-XL database that contains 21837 12-lead records is used for unsupervised feature learning. Using learned features, linear classifiers are trained to accomplish the downstream tasks. 448 ECG records from 148 myocardial infarction (MI) and 52 healthy control subjects of the PTB database are used for MI detection. 6877 records from the CPSC-2018 database are used for atrial fibrillation (AF) detection, including 918 normal records, 1098 AF records, and 4861 other records. Using fivefold cross-validation, our model achieves 90.38% and 73.27% accuracy in MI and AF detection, respectively. Compared with existing models, it improves the performances by at least 2.32% for MI detection and 3.99% for AF detection. The model has been deployed on a lightweight embedded system (800-MHz ARM processor, 1-GB RAM). The maximum latency is only 465.34 ms, which can satisfy the real-time constraints. Overall, all the results have demonstrated the potential of our method for real-world healthcare, and lightweight IoMT applications.

A Novel Resource Oriented DMA Framework for Internet of Medical Things Devices in 5G Network

The Internet of Medical Things (IoMT) mobile devices such as ambulance, medical done, and emergency mobile medical equipment face severe signal distortions due to interference, end-to-end packet loss, handoff delays, and lower throughputs during mobility. Network mobility basic support protocol (NBSP) has been proposed using the IP-based Wi-Fi solution to solve these issues. However, the weak signal, extra signaling overhead, and higherdelays were identified during handover due to patients' excessive requisites, resulting in radio link failure. Therefore, this article proposes a novel resource-efficient flow-enabled distributed mobility anchoring (FDMA) framework enhancing the functionalities of the centralized network entities and mobility entities.The performance of the proposed FDMA framework is evaluated and compared with the standard NBSP and proxy NEMO (PNEMO) scheme in terms of the variable number of cell residence time and mobile routers, where the proposed framework outperformed NBSP and PNEMO schemes for IoMT Mobile devices in 5G network.

P2SF-IoV: A Privacy-Preservation-Based Secured Framework for Internet of Vehicles

With the development of Internet of Vehicles (IoV), the integration of Internet of Things (IoT) and manual vehicles becomes inevitable in Intelligent Transportation Systems (ITS). In ITS, the IoVs communicate wirelessly with other IoVs, Road Side Unit (RSU) and Cloud Server using an open channel Internet. The openness of above participating entities and their communication technologies brings challenges such as security vulnerabilities, data privacy, transparency, verifiability, scalability, and data integrity among participating entities. To address these challenges, we present a Privacy-Preserving based Secured Framework for Internet of Vehicles (P2SF-IoV). P2SF-IoV integrates blockchain and deep learning technique to overcome aforementioned challenges, and works on two modules. First, a blockchain module is developed to securely transmit the data between IoV-RSU-Cloud. Second, a deep learning module is designed that uses the data from blockchain module to detect intrusion and its performance is assessed using two network datasets IoT-Botnet and ToN-IoT. In contrast with other peer privacy-preserving intrusion detection strategies, the P2SF-IoV approach is compared, and the experimental results reveal that in both blockchain and non-blockchain based solutions, the proposed P2SF-IoV framework outperforms.

A forensic investigation framework for Internet of Things monitoring

With low-energy wireless sensing platforms enabling communications between the physical world and numerous Internet based digital devices, Internet connected physical objects have blurred the lines that traditionally separated a physical scene from the digital scene. Whilst sophisticated new Internet enabled physical objects are rapidly introduced for various IoT applications, the increasing variety and similarities of the connected Things to ordinary physical objects has significant implications for the digital forensic process of identification. The often-constrained Things, however, communicate utilizing new and improved technologies that provide for robust wireless communications where other traditional wireless technologies are less suitable. This brings the opportunity to gather valuable insights from the radio frequency signals of a physical environment. This research introduces a novel concept of harnessing radio frequency signals to discover, determine and locate the wireless sensing deployments of a digital scene. A model of the system for IoT monitoring combines the processes of collecting communications between connected Things, building the logical topologies and predicting the locations of disparate Things. The design of this solution is suited to benefit both forensic investigators and law enforcement agencies.

Sharding-Hashgraph: A High-Performance Blockchain-Based Framework for Industrial Internet of Things With Hashgraph Mechanism

In recent years, with the development and widespread use of blockchain, many projects have introduced blockchain technology to solve the increasingly serious security problems of the Industrial Internet of Things (IIoT). However, due to the conflict between the operational performance and security of the blockchain system, the conflict between transparency and privacy, and the compatibility issues with a large number of IIoT devices running together, the mainstream blockchain system cannot be applied to IIoT scenarios. In order to solve these problems, in this article, we propose an IIoT distributed data system based on blockchain technology. We provide a novel system architecture for different IIoT devices to deploy high-performance blockchain systems in many scenarios, such as smart factory networks. To improve the performance of the blockchain network, we adopt the sharding hashgraph consensus mechanism and introduce a node evaluation mechanism based on the state of the node, which is applied to divide a large number of nodes into many shards dynamically. We abstract the node sharding problem as a joint optimization problem and use deep reinforcement learning to solve it. Finally, we compared with asynchronous Byzantine consensus algorithms, such as HoneybadgerBFT and BEAT, which validated the performance of this system architecture.

Identification of Casting Product Surface Quality Using Alex net and Le-net CNN Models

Casting is a manufacturing process in which a fluid product is normally poured into a mold, which contains a hollow cavity of the preferred form, and afterwards allowed to solidify. A Casting defect is an undesirable abnormality in a metal casting procedure. There are lots of types of issues in casting like blow openings, pinholes, burr, shrinkage issues, product flaws. Defects are an undesirable thing in the casting Industry. In this job, we extracted different casting items features and then applied convolutional neural network-based models for the discovery of the casted item is great or otherwise. So, it observed that neural networks can record the colours as well as textures of casting particularly to respective, which looks like human decision-making. This design is to deploy the Django internet framework. We try out different surfaces as input to convolutional neural networks for the efficient classification of the surface defect.

Towards Design and Development of Security Assessment Framework for Internet of Medical Things

The majority of medical equipment in use today does not have built-in security features. As a result, whether linked to a hospital system or the cloud, these devices’ built-in weaknesses make them vulnerable to a variety of cyberattacks. In hospitals and clinics, hackers can breach equipment, manipulate data, and disrupt facilities, putting patients’ health as well as their lives in jeopardy. A professional can manage cybersecurity threats by lowering the attack surface of the system. Security analysis, whether as a means to detect possible vulnerabilities that can be exploited by attackers or as a means to prevent cyberattacks, plays an important role in risk mitigation. In addition, throughout the pre-market and post-market phases, security checks are required. This study presents a paradigm for incorporating security check concepts into medical device design and development and healthcare big data security. The security of devices and healthcare data is tested by the integrated fuzzy AHP-TOPSIS method. After the security check of devices, with the parameters security-checked for data, the algorithm is designed and implemented. As a result, the appropriate customized security controls are prompted in order to impede the attack.

Secure and Reliable Transfer Learning Framework for 6G-Enabled Internet of Vehicles

In the coming 6G era, Internet of Vehicles (IoV) has been evolving towards 6G-enabled IoV with super-high data rate, seamless networking coverage, and ubiquitous intelligence by Artificial Intelligence (AI). Transfer Learning (TL) has great potential to empower promising 6G-enabled IoV, such as smart driving assistance, with its outstanding features including enhancing the quality and quantity of training data, speeding up learning processes, and reducing computing demands. Although TL had been widely adopted in wireless applications (e.g., spectrum management and caching), its reliability and security in 6G-enabled IoV were still not well investigated. For instance, malicious vehicles in source domains may transfer and share untrustworthy models (i.e., knowledge) about connection availability to target domains, thus adversely affecting the performance of learning processes. Therefore, it is important to select and also incentivize trustworthy vehicles to participate in TL. In this article, we first introduce the integration of TL and 6G-enabled IoV and provide TL applications for 6G-enabled IoV. We then design a secure and reliable transfer learning framework by using reputation to evaluate the reliability of pre-trained models and utilizing the consortium blockchain to achieve secure and efficient decentralized reputation management. Moreover, a deep learning-based auction scheme for the TL model market is designed to motivate high-reputation vehicles to participate in model sharing. Finally, the simulation results demonstrate that the proposed framework is secure and reliable with well-designed incentives for TL in 6G-enabled IoV.

Uncertainty-Guided Optimal Scheduling of Automated Energy Internet

This article tries to propose a novel uncertainty-guided day-ahead scheduling framework for the microgrid energy Internet incorporating the renewable energy sources, battery storage, and electric vehicles. The proposed model makes the use of the prediction interval concept to capture the uncertainty effects in the bandwidth and create an optimal interval for the output cost function. To this end, a deep learning approach based on the generative adversarial models (GAMs) is designed to not only have a prediction interval with high confidence level but also make the least bandwidth with the maximum possible information. Moreover, an evolving approach based on bat algorithm is proposed to help to train the GAM optimally. Finally, the model performance and quality are assessed using the IEEE standard test system.

A drone-based data management and optimization using metaheuristic algorithms and blockchain smart contracts in a secure fog environment

The advent of fog computing as an extension of cloud-enabling technology has moved from the hub of the internet framework to the unmanned aerial vehicle (UAV)-enabled control and management of drone-based data. The key objectives are to improve the computation and processing of drone-based resource constraints and deliver the data to the outsourced computational node to schedule, process, manage, optimize, and preserve it since they are located close to each other through a wireless sensor network. This collaborative technological approach to robustness is the standard of drone system design and implementation, reducing the computational power, resource consumption, and latency for the application that needs a fast response. However, recent research into fog-enabled drone-based data management and optimization poses a serious challenge in terms of privacy, security, and preservation. On the other hand, Blockchain Hyperledger Technology, which is mostly used in Bitcoin Cryptocurrencies, has been applied in a large number of distributed applications due to the ledger integrity, transparency, trustworthiness, provenance, reliability, availability, protection, and security-related distributed features that are provided. Therefore, the distributed application of drone control has received more attention because of its ability to handle collaborative procedures to capture, schedule, process, optimize, manage, and preserve drone-based data using fog nodes and blockchain hyperledger technology. In this paper, we proposed a collaborative approach using blockchain hyperledger fabric and a metaheuristic-enabled genetic algorithm for fog node management called B-Drone. This approach handles drone-based data collection, scheduling, optimizing, processing, managing, and preservation in a secure manner in the fog node. The protection of each transaction between drone and fog node before exchange is private with the utilization of the hash-encryption (SHA-256) algorithm. The blockchain smart contracts are implemented and deployed to auto-manage all the connectivity and communication protocols between drones and fog nodes in the designed private permissioned network. The simulations of the proposed collaborative approach reduce the computing cost down to 12.03% and increase the performance up to 73.11%, thus decreasing the cost of drone-ledger preservation down to 30.13% and the robust usage of the network up to 54.29% as compared to other state-of-the-art methods .