LocKedge: Low-Complexity Cyberattack Detection in IoT Edge Computing

Internet of Things and its applications are becoming commonplace with more devices, but always at risk of network security. It is therefore crucial for an IoT network design to identify attackers accurately, quickly and promptly. Many solutions have been proposed, mainly concerning secure IoT architectures and classification algorithms, but none of them have paid enough attention to reducing the complexity. Our proposal in this paper is an edge cloud architecture that fulfills the detection task right at the edge layer, near the source of the attacks for quick response, versatility, as well as reducing the workload of the cloud. We also propose a multi attack detection mechanism called LocKedge Low Complexity Cyberattack Detection in IoT Edge Computing, which has low complexity for deployment at the edge zone while still maintaining high accuracy. LocKedge is implemented in two manners: centralized and federated learning manners in order to verify the performance of the architecture from different perspectives. The performance of our proposed mechanism is compared with that of other machine learning and deep learning methods using the most updated BoT IoT data set. The results show that LocKedge outperforms other algorithms such as NN, CNN, RNN, KNN, SVM, KNN, RF and Decision Tree in terms of accuracy and NN in terms of complexity.

With the continuous development of the Internet of Things (IoT) and the needs of the times, the number of IoT edge devices is rapidly increasing, and to be able to provide users with more reliable information services and decision-making, this paper constructs an IoT system based on cloud-edge collaboration. Based on cloud-edge collaboration, data mining technology is used to categorize data, and regression models and branch neural networks are combined to further improve the IoT system based on cloud-edge collaboration. Finally, the particle swarm algorithm is used to optimize reinforcement learning, and the framework of an IoT system built on cloud-edge partnership is established. Through the data processing experiments on IoT edge computing, the data mining performance of the IoT edge computing-based data mining method is improved by 0.0295, 0.1870, and 0.2059 on the large datasets of Dataset4 and Dataset5, respectively. the average turnaround time is lower than 6.5 for different edge computing power levels. The system rewards are gradually improved from 5.3 to 8.2, and the number of packet loss of the algorithm is less than 5. The feasibility and effectiveness of the IoT data mining method and system based on cloud-edge collaboration are confirmed by this study, and a feasible method for data mining can be applied to IoT systems under cloud-edge collaboration.

With the proliferation of ubiquitous Internet of things (IoT) sensors and smart devices in a wide array of industries, involving transportation, agriculture, and healthcare, a huge amount of data is being generated, which requires ever-increasing calculations and services both at the cloud and at the edge of the network. Because cloud servers are specifically located a significant distance from IoT devices, time-sensitive Internet of things applications must be extended to the cloud architecture for the timely delivery of important services. Although IoT services can be allocated to the right edge nodes, ensuring low latency and efficient resource use is still challenging. To overcome these challenges, a novel resource allocation using clustering enabled optimization in IoT (RACE-IoT) has been proposed in this paper. This work introduces a novel Shannon–dragonfly (Sha-Dragon) optimization algorithm, which combines the dragonfly algorithm with the Shannon entropy for resource scheduling. The proposed RACE-IoT balances the load by reallocating the resources to maintain the same load ratio over all servers. The developed technique has been compared with the previous strategies involving PBSM, HRL-Edge-Cloud, and ERAM-EE in terms of specific parameters such as energy consumption, throughput, response time, end-to-end delay, resource utilization, makespan, and success rate. The outcomes demonstrate the efficiency of the developed RACE-IoT technique. The proposed technique achieves higher success rates of 35%, 17%, and 18% than existing techniques such as PBSM, HRL-Edge-Cloud, and ERAM-EE, respectively.

Enhanced Dynamic Deep Q-Network for Federated Learning scheduling policies on IoT devices using explanation-driven trust

The rapid expansion of 5G networks and edge computing has amplified security challenges in Internet of Things (IoT) environments, including unauthorized access, data tampering, and DDoS attacks. This paper introduces EdgeChainGuard, a hybrid blockchain-based authentication framework designed to secure 5G-enabled IoT systems through decentralized identity management, smart contract-based access control, and AI-driven anomaly detection. By combining permissioned and permissionless blockchain layers with Layer-2 scaling solutions and adaptive consensus mechanisms, the framework enhances both security and scalability while maintaining computational efficiency. Using synthetic datasets that simulate real-world adversarial behaviour, our evaluation shows an average authentication latency of 172.50 s and a 50% reduction in gas fees compared to traditional Ethereum-based implementations. The results demonstrate that EdgeChainGuard effectively enforces tamper-resistant authentication, reduces unauthorized access, and adapts to dynamic network conditions. Future research will focus on integrating zero-knowledge proofs (ZKPs) for privacy preservation, federated learning for decentralized AI retraining, and lightweight anomaly detection models to enable secure, low-latency authentication in resource-constrained IoT deployments.

Today’s Machine Learning (ML) in a blend with Internet of Things (IoT)-based cloud applications plays a significant role in our everyday life. As indicated by Gartner’s recent study, there are around 25 billion devices and a gadget interfacing with IoT including wearables and automated vehicles to smart homes and smart cities applications. All such connected (smart) devices generate immense data that needs to be examined and analysed, to ensure that they continually learn from the available datasets and better themselves without any manual interference. This is where the prerequisite for machine learning comes into being. Several ML algorithms and techniques are introduced in a short time to easily evaluate big data measurements, increasing the IoT’s productivity. Similarly, special ML techniques, such as decision trees, clustering and neural and Bayesian networks, allow devices and gadgets to discern trends from various sources in different kinds of datasets and take appropriate decisions based on their analysis.It would really be difficult for smart devices to make smart decisions gradually without including and enforcing ML. The IoT helps to interconnect various hardware devices, such as houses, cars, electronic gadgets and other devices that are integrated with actuators, sensors and software, so that data can be collected and shared. As various organizations understand the progressive capability of the IoT, they have begun finding various obstructions they have to deliver to use it productively. Numerous organizations and businesses use ML to exploit the IoT’s latent capacity. This chapter evaluates the different methods of machine learning that deal with the challenges posed in the handling of IoT data. Note that this big data is generated through the communication of Internet of Things/smart devices, and this data stored at cloud. The taxonomy of machine learning algorithms is described in this chapter, explaining how different techniques are applied to data generated using IoT devices. It will also address the future problems of machine learning for IoT data analytics.KeywordsInternet of Things (IoT)-based cloud applicationsMachine learning techniquesArtificial intelligenceClassificationSmart era

This paper examines the enhancement of security measures for the Internet of Things (IoT) systems through the application of Machine Learning (ML) techniques. As the number of IoT devices continues to rise, ensuring their security has become increasingly critical, given that conventional methods frequently struggle to identify advanced threats. This study explores the implementation of several ML algorithms, including Random Forest (RF), Decision Trees (DT), Support Vector Machines (SVM), and Convolutional Neural Networks (CNN), to identify anomalies and intrusions within IoT networks. By conducting a comprehensive review of existing research and experiments, it highlights the effectiveness of ML in enhancing IoT security, with high detection rates for various threats, including botnet attacks, Denial-of-Service (DoS) and Distributed Denial-of-Service (DDoS) incidents, and intrusion attempts. DoS/DDoS attacks and many types of botnets are the most devastating attacks that have been spreading for a long time, and they are still branching out in new ways against IoT networks. They can damage IoT services and prevent these services from being used by legitimate users. Therefore, securing IoT networks becomes a significant concern. The proposed model is used to increasingly monitor network traffic for any deviations from standard patterns IoT networks. This paper also stresses the necessity of utilising suitable datasets and feature selection techniques to enhance the efficacy of ML models. To train our model, we have utilized a dataset called the IoT23 dataset, which is one of the most recent datasets that has many IoT scenarios and anomalous activities. Further-more, we utilised two types of feature selection algorithms, the Correlation-based Feature Selection (CFS) algorithm and the Genetic Algorithm (GA), and then we compared the results of these algorithms when training our model. The best performances were obtained with DT and RF classifiers when they were trained with features selected by CFS However, for training and testing times metrics, DT performance was superior across both feature selection methods.

The paradigm of computing has completely altered as a result of the development of communication and Information technology. The Internet of Things (IoT) is a type of communication environment made up of web-enabled devices that can communicate, gather, assess, and send data across the network without the need for human participation. The IoT idea is widely established in numerous fields of application, and it has also been used in the medical industry. The Internet of Medical Things (IoMT) refers to the combination of IoT with medical technology. Even though IoMT applications have facilitated real time monitoring in the healthcare sector, it suffers from several security and privacy attacks. The presence of such security and privacy attacks can cause the alteration or disclosure of sensitive data or sometimes authorized users cannot access the data also. Hence, it is crucial to secure the IoMT environment from such malicious attacks. Therefore, this study aims in assessing harmful traffic in IoMT environment. In this research, XGBoost classification algorithm is proposed to classify malicious traffic in an IoMT environment. Further, the study has also implemented state-of-art machine learning algorithms such as DT (Decision Tree), RF (Random Forest), LR (Logistic Regression), SVM (Support Vector Machine), NB (Naive Bayes), MLP (Multilayer Perceptron), SGD (Stochastic Gradient Descent) to test the efficacy of the proposed model. The experimental outcomes indicate that the proposed XGBoost algorithm outperformed other conventional machine learning approaches in terms of predicting malicious traffic in an IoMT environment, with a 100-percentage accuracy rate. Furthermore, the XGBoost algorithm exhibits better performance in terms of precision, Fl-score, recall, and AUC as compared with traditional approaches.

Usually, IoT devices are designed to perform specific tasks, while robots have to adapt to unpredictable situations. Artificial intelligence and machine learning help these robots cope with the emerging unexpected conditions. The Internet of Robotic Things is an evolving concept that brings together all-encompassing sensors and devices with robotic and autonomous systems. Both IoT devices and robots rely on sensors to understand the surrounding environment, to process data quickly, and to decide how to respond. Nevertheless, while most IoT systems can handle only well-defined tasks, robots can handle expected situations as well. The Internet of Robotic Things is a better Internet of Things (IoT) solution due to its ability to bridge the gap between IT and real operations. The IoRT may face several challenges in power consumption and network bandwidth limitation due to the growth in the information dissemination over the Robotic network. The main objective of this paper is to introduce an overview of the concepts and challenges in the Internet of Robotic Things based on Fog Computing technique. Further, a framework of Fog based IoRT is introduced.

The Internet of Things (IoT) gives a strong structure for connecting things to the internet to facilitate Machine to Machine (M2M) communication and data transmission through basic network protocols such as TCP/IP. IoT is growing at a fast pace, and billions of devices are now associated, with the amount expected to reach trillions in the coming years. Many fields, including the army, farming, manufacturing, healthcare, robotics, and biotechnology, are adopting IoT for advanced solutions as technology advances. This paper offers a detailed view of the current IoT paradigm, specifically proposed for robots, namely the Internet of Robotic Things (IoRT). IoRT is a collection of various developments such as Cloud Computing, Artificial Intelligence (AI), Machine Learning, and the (IoT). This paper also goes over architecture, which would be essential in the design of Multi-Role Robotic Systems for IoRT. Furthermore, includes systems underlying IoRT, as well as IoRT implementations. The paper provides the foundation for researchers to imagine the idea of IoRT and to look beyond the frame while designing and implementing IoRT-based robotic systems in real-world implementations.

The real-time communication requirement of the Internet of Things (IoT) applications promotes the convergence of IoT and Mobile Edge Computing (MEC). The MEC paradigm greatly shortens the IoT service delay by leveraging cloudlets (edge servers) of MEC in the proximity of IoT devices. Considering limited computing and storage resources in an MEC network, it is challenging to provide efficient IoT-enabled service provisioning in such a network. In this article, we study the service home identification problem of service provisioning for multi-source IoT applications in an MEC network, by identifying a service home (cloudlet) of each multi-source IoT application for its data processing, querying and storage. Each multi-source IoT application consists of multiple sources located at different geographical locations and each source uploads its data stream via a gateway (its nearby access point) to the MEC network and the uploaded data then is aggregated with the stream data of the other sources of the IoT application at the service home. We here focus on two novel service home identification problems: the service operational cost minimization problem with the aim to minimize the total service operational cost by admitting as many multi-source IoT applications as possible, and the online throughput maximization problem with the aim to maximize the number of multi-source IoT application requests admitted. We first show that both the problems are NP-hard. We then formulate an Integer Linear Programming (ILP) solution to the service operational cost minimization problem, and propose a randomized algorithm with high probability and a deterministic approximation algorithm respectively, at moderate resource capacity violations. We third develop an efficient heuristic algorithm for the problem without any resource violation. Furthermore, we deal with the online throughput maximization problem under an assumption that multi-source IoT application requests arrive one by one without the knowledge of future arrivals, for which we formulate an Integer Linear Programming (ILP) solution to its offline version, followed by devising an online algorithm with competitive ratio. We finally evaluate the performance of the proposed algorithms through experimental simulations. Simulation results demonstrate that the proposed algorithms are promising, and outperform their comparison counterparts.

Internet of Things (IoT) have opened the door to a world of unlimited possibilities for implementations in varied sectors in society, but it also has many challenges. One of those challenges is security and privacy. IoT devices are more susceptible to security threats and attacks. Due to constraints of the IoT devices such as area, power, memory, etc., there is a lack of security solutions that are compatible with IoT devices and applications, which is leading this world of securely connected things to the “internet of insecure things.” A promising solution to this problem is going beyond the standard or classical techniques to implementing the security solutions in the hardware of the IoT device. The integration of emerging technologies in IoT networks, such as machine learning, blockchain, fog/edge/cloud computing, and quantum computing have added more vulnerable points in the network. This paper introduces a comprehensive study on IoT security threats and solutions. Additionally, this survey outlines how emerging technologies such as machine learning and blockchain are integrated in IoT, challenges resulted from this integration, and potential solutions to these challenges. The paper utilizes the 4-layer IoT architecture as a reference to identify security issues with corresponding solutions.

The internet of things is one of today’s most revolutionary technologies. Because of its pervasiveness, increasing network connection capacity, and diversity of linked items, the internet of things (IoT) is adaptable and versatile. The most common problem impeding IoT growth is insufficient security measures. The threat of data breaches is always there since smart gadgets gather and transmit sensitive information that, if disclosed, might have severe consequences. In this article, to identify and classify IoT network attacks, we have analyzed six machine learning and deep learning approaches: Decision Tree, Random Forest, AdaBoost, XGBoost, ANN and MLP. Accuracy, Precision, Recall, F1-Score, Confusion Matrix are some of the metrics we have used to evaluate our models. We have achieved fairly impressive results (above 96%) in binary classification for all the techniques. When all of the classifiers were analyzed, Decision Tree and Random Forest outperformed all others (above 99%) for both binary and multiclass classification. Adaboost and ANN, on the other hand, perform badly for multiclass classification. We have also applied Undersampling, Oversampling and SMOTE techniques on a dataset to reduce data skewness and to evaluate multiple ML and DL algorithms. The feasibility of the techniques suggested in this work is demonstrated on the IoT/IIoT dataset of TON_IoT datasets, which incorporate data obtained from Telemetry datasets of IoT and IIoT sensors.

The Internet of Things (IoT) has transformed various sectors by enabling connectivity and automation through smart devices. However, the expansion of IoT networks has introduced new security challenges, particularly concerning the detection and prevention of cyber threats. This study explores the application of machine learning (ML) models for enhancing network security in IoT-enabled smart systems. The primary objective was to evaluate the performance of different ML algorithms, including Convolutional Neural Networks (CNN), Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), K-Means, and Autoencoder, in detecting and mitigating security threats in IoT environments. The study also aimed to identify the most effective model in terms of accuracy, precision, recall, and F1-score. The research methodology involved training and testing these models on publicly available IoT security datasets, followed by performance evaluation based on the key metrics. Statistical analysis, including One-Way ANOVA and post-hoc testing, was used to assess the significance of the performance differences across the models. The results revealed that CNN outperformed all other models, achieving the highest accuracy (96.7%), precision (95.8%), recall (97.2%), and F1-score (96.5%). Random Forest also demonstrated strong performance with an accuracy of 95.6% and a balanced evaluation across all metrics. In contrast, traditional models like SVM and DT showed comparatively lower performance, while Autoencoder struggled with classification tasks, particularly in real-time threat detection. Statistical analysis confirmed the significant differences in model performance. In conclusion, the study demonstrates the effectiveness of deep learning and ensemble models, particularly CNN and Random Forest, in enhancing IoT network security. The research highlights the importance of selecting the appropriate ML model based on the specific requirements of the IoT environment and computational constraints, and suggests that hybrid models and model optimization strategies may offer promising solutions for resource-constrained IoT systems.

The proliferation of Internet of Things (IoT) devices in personal and household environments has led to a significant increase in security vulnerabilities. These devices, due to their limited computational resources, often struggle to support conventional security solutions, making them prime targets for cyberattacks. This paper explores the potential of lightweight machine learning (ML) models to enhance the security of personal IoT devices. By leveraging the power of AI, lightweight models can offer real-time threat detection and anomaly identification without compromising the device's performance or requiring extensive computational resources. The paper investigates various ML techniques that are well-suited for IoT environments, such as decision trees, k-nearest neighbours (KNN), and support vector machines (SVM), focusing on their ability to detect intrusions, unauthorized access, and other malicious activities while maintaining efficiency. Additionally, the study highlights the trade-offs between model complexity, accuracy, and resource consumption, offering practical insights for deploying ML solutions in resource-constrained IoT systems. Key challenges, including data privacy, model generalization, and the adaptability of models to diverse IoT ecosystems, are addressed. Finally, the paper discusses future directions for the integration of more advanced lightweight models, such as federated learning and edge computing, which could further enhance security capabilities while ensuring minimal impact on IoT device performance. Through this review, the paper advocates for the adoption of lightweight ML models as a feasible and scalable solution to securing personal IoT devices in an increasingly connected world.