Intrusion Detection In Internet Of Things Research Articles

Deep Learning-Inspired IoT-IDS Mechanism for Edge Computing Environments.

The Internet of Things (IoT) technology has seen substantial research in Deep Learning (DL) techniques to detect cyberattacks. Critical Infrastructures (CIs) must be able to quickly detect cyberattacks close to edge devices in order to prevent service interruptions. DL approaches outperform shallow machine learning techniques in attack detection, giving them a viable alternative for use in intrusion detection. However, because of the massive amount of IoT data and the computational requirements for DL models, transmission overheads prevent the successful implementation of DL models closer to the devices. As they were not trained on pertinent IoT, current Intrusion Detection Systems (IDS) either use conventional techniques or are not intended for scattered edge-cloud deployment. A new edge-cloud-based IoT IDS is suggested to address these issues. It uses distributed processing to separate the dataset into subsets appropriate to different attack classes and performs attribute selection on time-series IoT data. Next, DL is used to train an attack detection Recurrent Neural Network, which consists of a Recurrent Neural Network (RNN) and Bidirectional Long Short-Term Memory (LSTM). The high-dimensional BoT-IoT dataset, which replicates massive amounts of genuine IoT attack traffic, is used to test the proposed model. Despite an 85 percent reduction in dataset size made achievable by attribute selection approaches, the attack detection capability was kept intact. The models built utilizing the smaller dataset demonstrated a higher recall rate (98.25%), F1-measure (99.12%), accuracy (99.56%), and precision (99.45%) with no loss in class discrimination performance compared to models trained on the entire attribute set. With the smaller attribute space, neither the RNN nor the Bi-LSTM models experienced underfitting or overfitting. The proposed DL-based IoT intrusion detection solution has the capability to scale efficiently in the face of large volumes of IoT data, thus making it an ideal candidate for edge-cloud deployment.

Bi-Level Intrusion Detection in IoT Networks Using Ensemble Method and A-GRU-RNN Classifier

For minimizing security vulnerabilities and attacks, which affect Internet of Things (IoT) applications’ performance, it is essential to design an efficient authentication protocol owing to the rapid deployment of IoT. This paper proposes a bi-level Intrusion Detection (ID) in IoT using an ensemble and Arctan-based Gated Recurrent Unit-Recurrent Neural Network (A-GRU-RNN) classifier. This work mainly concentrates on both major and minor attacks. Allowing the trusted nodes to join the network is the other motive of this work. Here, the nodes are registered to the network and then initialized. Then, to verify the trust levels of the nodes, test packet transmission is performed. By employing the Deterministic Initialization Method-centric K-Means algorithm (DIM-K-Means) algorithm, the trusted nodes are formed into the cluster. Next, the respective CH is selected by Multiplex-Valued Encoding Sea Lion Optimization (MVE-SLO). Afterwards, by employing Multi-Point Relays-Optimized Link State Routing (MPR-OLSR), routing is taken place. The steps, namely preprocessing, attribute extraction, attribute reduction, and classification are taken to detect the confidentiality of the sensed data. The classification phase significantly determines whether the data is attacked or not. If the data is attacked, then it detects the attack type. Here, the publicly available dataset is used. As per the experimental outcome, the proposed method withstands high-security levels when analogized to the prevailing methodologies.

IoT Intrusion Detection System Based on Machine Learning

With the rapid development of the Internet of Things (IoT), the number of IoT devices is increasing dramatically, making it increasingly important to identify intrusions on these devices. Researchers are using machine learning techniques to design effective intrusion detection systems. In this study, we propose a novel intrusion detection system that efficiently detects network anomalous traffic. To reduce the feature dimensions of the data, we employ the binary grey wolf optimizer (BGWO) heuristic algorithm and recursive feature elimination (RFE) to select the most relevant feature subset for the target variable. The synthetic minority oversampling technique (SMOTE) is used to oversample the minority class and mitigate the impact of data imbalance on the classification results. The preprocessed data are then classified using XGBoost, and the hyperparameters of the model are optimized using Bayesian optimization with tree-structured Parzen estimator (BO-TPE) to achieve the highest detection performance. To validate the effectiveness of the proposed method, we conduct binary and multiclass experiments on five commonly used IoT datasets. The results show that our proposed method outperforms state-of-the-art methods in four out of the five datasets. It is noteworthy that our proposed method achieves perfect accuracy, precision, recall, and an F1 score of 1.0 on the BoT-Iot and WUSTL-IIOT-2021 datasets, further validating the effectiveness of our approach.

A lightweight IoT intrusion detection model based on improved BERT-of-Theseus

The proliferation of Internet of Things (IoT) technology has resulted in an increase in security vulnerabilities associated with the interconnectivity of IoT devices. As a result, there is a need for intrusion detection mechanisms that can effectively detect attacks on IoT security vulnerabilities. However, due to the resource constraints of IoT deployment devices, intrusion detection schemes must be customized to meet the specific demands of the IoT environment. In this study, we propose a knowledge-distillation-based IoT intrusion detection model named BT-TPF, which is capable of detecting network attacks encountered by IoT devices in an IoT environment with limited computing resources. The proposed BT-TPF model leverages a Siamese network for feature dimensionality reduction of complex high-dimensional network traffic data. Additionally, it employs a large-scale Vision Transformer as a teacher model to guide a small-scale Poolformer model during training, before deploying the trained Poolformer model as a classifier to detect network intrusion traffic. Through knowledge distillation, the final small model obtained in this paper only requires a minimum of 788 parameters, reducing the number of parameters by approximately 90% compared to the large model before knowledge distillation, while maintaining high detection accuracy. Experimental results show that the BT-TPF model achieves over 99% accuracy on both the CIC-IDS2017 and TON_IoT datasets. Furthermore, it exhibits significant advantages compared to traditional Deep Learning methods and recent state-of-the-art models, as evidenced by various evaluation metrics.

Enhanced Lion Optimization Algorithm and deep belief network for intrusion detection with SDN enabled IoT networks

A severe problem that regularly affects cloud systems are intrusions. Ignore how the expansion of Internet of Things (IoT) devices will result in enormous intrusions. To distinguish intrusions from authorized network activity, detection is a crucial procedure. An Enhanced Lion Optimization Algorithm (ELOA) is utilized in this research, IoT intrusion detection system. Intrusions are classified using the Deep Belief Network (DBN) and an SDN controller technique. The proposed ELOA-based Intrusion Detection System uses the optimal weight in DBN to train the neurons to categorize the data in a network as normal and attacked during the training phase. In the testing step that follows training, data from nodes are examined, and by contrasting the training results, they are categorized as normal and attacked data. By using the proposed ELOA and DBN algorithms, our intrusion detection system can successfully identify intrusions. Based on the creation of blacklists for detecting IoT intrusions, the (SDN) Software Defined Networking controller can effectively prohibit harmful devices. In order to demonstrate that the proposed ELOA finds network intrusions more successfully, its performance is compared to that of other existing techniques. The node sizes of the algorithms are run and evaluated for 1000, 2000, 3000, 4000, and 5000 respectively. At highest node 5000, the Proposed ELOA and DPN have precision, recall, f-score and accuracy becomes as 97.8, 96.22, 97.5 and 98.67 respectively.

An intelligent IoT intrusion detection system using HeInit-WGAN and SSO-BNMCNN based multivariate feature analysis

Devices connected to the Internet of Things (IoT) are growing in popularity across multiple industries. IoT links intelligent homes, buildings, businesses, and communities, and also links computers and mobile devices. Cybersecurity in IoT networks remains challenging, although many intrusion detection services for assault avoidance systems have been developed. Because of enormous growth in computing resources, attack surfaces, communications infrastructure, and attack rates, IoT services encounter substantial security issues. Innovative intrusion-detection systems ensuring data security, availability, and integrity have been developed to mitigate security problems; these systems are recommended for preventing common internal and external assaults and managing IoT environments. To clearly understand any impending attacks, the data are first preprocessed. Missing values are categorized with the concordance connection coefficient, and preprocessing is applied before handling of the missing values. The unevenly distributed and overlapping data are then handled through maximum likelihood estimation-based robust scalar (MLE-RS) scaling followed by random oversampling-based cluster-initialized divisive clustering (ROS-CInitDC). This study conceptualizes a Squirrel search optimization-based Bengio Nesterov momentum convolutional neural network (SSO-BNMCNN)-based approach, which indicates the relationships among information by considering causation. It keeps an optimal processing time and correctness to get data understanding. The selected features are then tested against the categorization of a He initialization-based Wasserstein gradient penalty loss generative adversarial network (HeInit-WGAN). The HeInit-WGAN technique identifies attacks more reliably and accurately than existing methods, reducing false alarms. The proposed strategy had an F-measure of 0.91, and scores of 0.96 for precision, 0.91 for awareness, and 0.98 for explicitness. Moreover, it decreased the false acceptance rate, achieving a 0.02 false positive rate and 0.09 false negative rate.

Strengthening IoT Intrusion Detection through the HOPNET Model

The rapid growth of Internet of Things (IoT) applications has raised concerns about the security of IoT communication systems, particularly due to a surge in malicious attacks leading to network disruptions and system failures. This study introduces a novel solution, the Hyper-Parameter Optimized Progressive Neural Network (HOPNET) model, designed to effectively detect intrusions in IoT communication networks. Validation using the Nsl-Kdd dataset involves meticulous data preprocessing for error rectification and feature extraction across diverse attack categories. Implemented on the Java platform, the HOPNET model undergoes comprehensive evaluation through comparative analysis with established intrusion detection methods. Results demonstrate the superiority of the HOPNET model, with improved attack prediction scores and significantly reduced processing times, highlighting the importance of advanced intrusion detection methods for enhancing IoT communication security. The HOPNET model contributes by establishing robust defense against evolving cyber threats, ensuring a safer IoT ecosystem, and paving the way for proactive security measures as the IoT landscape continues to evolve.

SIDS: A federated learning approach for intrusion detection in IoT using Social Internet of Things

The Internet of Things (IoT) ecosystem needs Intrusion Detection Systems (IDS) to mitigate cyberattacks and exploit security vulnerabilities. Over the past years, utilizing machine learning in IDSs has gained a lot of attention. However, in many current works, the training data from different locations should be collected in a central server to be used in the learning process. This data-sharing procedure increases concerns regarding data privacy and decreases the data holders’ motivation to participate in the learning process. The use of distributed learning models has been considered a solution to overcome concerns related to privacy. However, these distributed learning models are vulnerable in the presence of untrusted nodes that can participate in the learning process and deteriorate performance. In this paper, we propose SIDS (Social Intrusion Detection System), a trust-oriented federated learning approach for intrusion detection in IoT that utilizes the Social Internet of Things (SIoT). The proposed approach leverages the social relationships among the objects in a system to provide a privacy-preserving collaborative mechanism for detecting intrusions in IoT environments. The experimental results show the proposed solution outperforms the learning models on individual servers while providing a privacy-preserving and trustable environment for collaboration.

A stacking ensemble of deep learning models for IoT intrusion detection

The number of Internet of Things (IoT) devices has increased considerably in the past few years, which resulted in an exponential growth of cyber attacks on IoT infrastructure. As a consequence, the prompt detection of attacks in IoT environments through the use of Intrusion Detection Systems (IDS) has become essential. This article proposes a novel approach to intrusion detection in IoT based on a stacking ensemble of deep learning (DL) models. This approach is named Deep Integrated Stacking for the IoT (DIS-IoT) and it combines four different DL models into a fully connected DL layer, creating a standalone ensemble model. DIS-IoT is evaluated on three open-source datasets, namely ToN_IoT, CICIDS2017 and SWaT, in binary and multi-class classification and compared results with other standard DL methods. Experiments demonstrate that DIS-IoT is capable of a high-level accuracy with a very low False Positive rate (FPR) in all datasets. Results were also compared against other state-of-the-art works available in the literature, which used similar methods on the same ToN_IoT dataset. DIS-IoT achieves comparable performance with others in binary classification and outperforms them in multi-class classification.

Feature selection using bio-inspired optimization for IoT intrusion detection and prevention system

Nowadays smart Internet of Things (IoT) devices are used briskly, and these devices communicate with each other via wireless medium. However, this increase in IoT devices has resulted in a rise of security issues associated with the IoT system. Therefore, an intrusion detection and prevention system (IDPS) is used to locate and report any malicious activity. The IDPS's feature selection (FS) task is necessary to improve the data quality and decrease the data used for classifying intrusive traffic. Therefore, this paper proposes a novel FS method that hybridizes improved salp swarm algorithm and harris hawk optimization algorithm. The XGBoost classifier is used for classifying reduced network traffic. Proposed system demonstrates high accuracy and low computation time, surpassing other related approaches used for the IDPS feature selection task.

Deep Learning Towards Intrusion Detection System (IDS): Applications, Challenges and Opportunities

With the growth of numerous technological areas, including sensors, embedded computing, broadband Internet access, wireless communications, distributed services, automatic identification, and tracking, the potential for integrating smart objects into our daily activities through the Internet has increased. The Internet of Things (IoT) is the confluence of the Internet and intelligent objects that can converse and cooperate with one another. IoT is a brand-new example that unifies Cyberspace with actual physical objects from various areas, including, business processes, human health, home automation, and environmental monitoring. It intensifies the use of Internet-connected strategies in our regular lives, carrying with it several advantages as well as security challenges. Intrusion Detection Systems (IDS) have been a crucial device for the defence of systems and material schemes for more than 20 years. However, applying traditional IDS techniques was challenging due to the IoT’s inimitable features, like resource-constrained devices and particular protocol stacks and standards. As a result, this survey will focus on various Deep Learning (DL)-based intrusion detection techniques. This study makes use of 50 research papers that focused on different techniques, and a review of studies that used those techniques was given. This research enables categorizing the methods employed for intrusion detection in IoT based on Convolutional Neural Network (CNN)-based methods, Deep Neural Network (DNN)-based methods, Optimization-based methods, and so on. Moreover, the categorization of approaches, published year, the dataset used, tools used, and the performance metrics are measured for intrusion detection in IoT. On the basis of the software used for implementation, performance achievement, and other factors, a thorough analysis was conducted. The conclusion identifies the research gaps and issues in a way that makes it clear why should create an efficient method for enabling efficient enhancement.

Federated Learning for IoT Intrusion Detection

The number of Internet of Things (IoT) devices has increased considerably in the past few years, resulting in a large growth of cyber attacks on IoT infrastructure. As part of a defense in depth approach to cybersecurity, intrusion detection systems (IDSs) have acquired a key role in attempting to detect malicious activities efficiently. Most modern approaches to IDS in IoT are based on machine learning (ML) techniques. The majority of these are centralized, which implies the sharing of data from source devices to a central server for classification. This presents potentially crucial issues related to privacy of user data as well as challenges in data transfers due to their volumes. In this article, we evaluate the use of federated learning (FL) as a method to implement intrusion detection in IoT environments. FL is an alternative, distributed method to centralized ML models, which has seen a surge of interest in IoT intrusion detection recently. In our implementation, we evaluate FL using a shallow artificial neural network (ANN) as the shared model and federated averaging (FedAvg) as the aggregation algorithm. The experiments are completed on the ToN_IoT and CICIDS2017 datasets in binary and multiclass classification. Classification is performed by the distributed devices using their own data. No sharing of data occurs among participants, maintaining data privacy. When compared against a centralized approach, results have shown that a collaborative FL IDS can be an efficient alternative, in terms of accuracy, precision, recall and F1-score, making it a viable option as an IoT IDS. Additionally, with these results as baseline, we have evaluated alternative aggregation algorithms, namely FedAvgM, FedAdam and FedAdagrad, in the same setting by using the Flower FL framework. The results from the evaluation show that, in our scenario, FedAvg and FedAvgM tend to perform better compared to the two adaptive algorithms, FedAdam and FedAdagrad.

Addressing Internet of Things security by enhanced sine cosine metaheuristics tuned hybrid machine learning model and results interpretation based on SHAP approach.

An ever increasing number of electronic devices integrated into the Internet of Things (IoT) generates vast amounts of data, which gets transported via network and stored for further analysis. However, besides the undisputed advantages of this technology, it also brings risks of unauthorized access and data compromise, situations where machine learning (ML) and artificial intelligence (AI) can help with detection of potential threats, intrusions and automation of the diagnostic process. The effectiveness of the applied algorithms largely depends on the previously performed optimization, i.e., predetermined values of hyperparameters and training conducted to achieve the desired result. Therefore, to address very important issue of IoT security, this article proposes an AI framework based on the simple convolutional neural network (CNN) and extreme machine learning machine (ELM) tuned by modified sine cosine algorithm (SCA). Not withstanding that many methods for addressing security issues have been developed, there is always a possibility for further improvements and proposed research tried to fill in this gap. The introduced framework was evaluated on two ToN IoT intrusion detection datasets, that consist of the network traffic data generated in Windows 7 and Windows 10 environments. The analysis of the results suggests that the proposed model achieved superior level of classification performance for the observed datasets. Additionally, besides conducting rigid statistical tests, best derived model is interpreted by SHapley Additive exPlanations (SHAP) analysis and results findings can be used by security experts to further enhance security of IoT systems.

Enhancing intrusion detection in IoT systems: A hybrid metaheuristics-deep learning approach with ensemble of recurrent neural networks

Significant benefits have been brought to various industries by IoT (Internet of Things), but new security challenges have also been introduced due to the sheer volume and complexity of IoT systems. The protection of IoT systems from attacks and the assurance of their security posture are ensured by intrusion detection systems (IDSs). Recently, machine learning (ML) strategies have been broadly adopted for IDSs in IoT systems. However, there is still room for improvement, particularly in addressing the physical and functional diversity of IoT systems. A hybrid metaheuristics-deep learning approach is proposed in this paper for enhancing intrusion detection in IoT systems. An advanced metaheuristics algorithm with an ensemble of recurrent neural networks (RNNs) can be utilized to enhance the intrusion detection in IoT. Different types of attacks in IoT systems are identified by employing LSTM and GRU models, which constitute the RNNs. Feature selections are performed using Harris hawk optimization and fractional derivative mutation, as employed in this paper. To assess the proposed approach, publicly available datasets were utilized, and the empirical analysis demonstrated that the proposed approach works well than the other related methods in terms of accuracy and efficiency. Overall, the proposed work presents a promising solution for enhancing intrusion detection in IoT systems, and it has the potential to serve as a foundation for future research in this field.

EBF: an enhanced Bloom Filter for intrusion detection in IoT

Intrusion Detection is essential to identify malicious incidents and continuously alert many users of the Internet of Things (IoT). The constant monitoring of events generated from many devices connected to the IoT and the extensive analysis of every event based on predefined security policies consumes enormous resources. Accordingly, performance enhancement is a crucial concern of Intrusion Detection in IoT and other massive Big Data Applications to ensure a secure environment. Like many Big Data Applications, the Intrusion Detection system of the IoT needs to employ the fast membership filter, Bloom Filter, to quickly identify possible attacks. Bloom Filter is an admiringly fast and space-efficient data structure that quickly handles elements of extensive datasets in small memory space. However, the trade-off between the query performance, the number of hash functions, memory space, and false positive probability remains an issue of Bloom Filter. Thus, this article presents an enhanced Bloom Filter (eBF) that remarkably improves memory efficiency and introduces new techniques to accelerate the filtering of malicious URLs. We experimentally show the efficacy of eBF using a real Intrusion Detection dataset. The experimental result shows that the proposed filter is remarkably memory efficient, faster, and more accurate than the state-of-the-art filters. eBF requires 15.6x, 13x, and 8x less memory compared with Standard Bloom Filter, Cuckoo filter, and robustBF, respectively. Therefore, this new system significantly enhances the performance of Intrusion Detection of IoT that concurrently monitors several billion events crosschecking with the defined security policies.

Securing a Smart Home with a Transformer-Based IoT Intrusion Detection System

Machine learning (ML)-based Network Intrusion Detection Systems (NIDSs) can classify each network’s flow behavior as benign or malicious by detecting heterogeneous features, including both categorical and numerical features. However, the present ML-based NIDSs are deemed insufficient in terms of their ability to generalize, particularly in changing network environments such as the Internet of Things (IoT)-based smart home. Although IoT devices add so much to home comforts, they also introduce potential risks and vulnerabilities. Recently, many NIDS studies on other IoT scenarios, such as the Internet of Vehicles (IoV) and smart cities, focus on utilizing the telemetry data of IoT devices for IoT intrusion detection. Because when IoT devices are under attack, their abnormal telemetry data values can reflect the anomaly state of those devices. Those telemetry data-based IoT NIDS methods detect intrusion events from a different view, focusing on the attack impact, from the traditional network traffic-based NIDS, which focuses on analyzing attack behavior. The telemetry data-based NIDS is more suitable for IoT devices without built-in security mechanisms. Considering the smart home IoT scenario, which has a smaller scope and a limited number of IoT devices compared to other IoT scenarios, both NIDS views can work independently. This motivated us to propose a novel ML-based NIDS to combine the network traffic-based and telemetry data-based NIDS together. In this paper, we propose a Transformer-based IoT NIDS method to learn the behaviors and effects of attacks from different types of data that are generated in the heterogeneous IoT environment. The proposed method utilizes a self-attention mechanism to learn contextual embeddings for input network features. Based on the contextual embeddings, our method can solve the feature set challenge, including both continuous and categorical features. Our method is the first to utilize both network traffic data and IoT sensors’ telemetry data at the same time for intrusion detection. Experiments reveal the effectiveness of our method on a realistic network traffic intrusion detection dataset named ToN_IoT, with an accuracy of 97.95% for binary classification and 95.78% for multiple classifications on pure network data. With the extra IoT information, the performance of our method has been improved to 98.39% and 97.06%, respectively. A comparative study with existing works shows that our method can achieve state-of-the-art performance on the ToN_IoT dataset.

Research on Industrial IoT Security Based on Deep Learning

<p>With the rapid development of "Internet +" and the construction of a new generation of information infrastructure, the intrusion behaviors against the Industrial Internet of Things are increasingly common. How to ensure the security of the industrial Internet of Things is one of the current research hotspots. The modern technology trend has the hottest technologies of the Internet of Things (IoT). The application of IoT on the other hand improves work efficiency and brings convenience to people’s life; on the other hand, it makes the network face increasingly serious security threat problems and attacks the network by unscrupulous elements occur from time to time. Machine learning-based intrusion detection techniques involve a large number of mathematical formula operations, while with the development of neural networks, the excellent autonomous feature learning capability of deep learning is recognized. An intrusion detection system plays an important role in preventing security threats and protecting them from attacks. The current research on industrial IoT security technology focuses on authentication technology, encryption technology, access control technology, and intrusion detection technology. In this paper, we analyze deep learning and industrial IoT intrusion detection and use the powerful data processing capability and feature learning capability of deep learning to conduct an in-depth study on industrial IoT intrusion detection methods based on deep learning. This paper achieves a 96.32% detection rate on industrial control dataset, which can better adapt to the needs of industrial IoT intrusion detection.</p> <p> </p>

EIDM: deep learning model for IoT intrusion detection systems

Internet of Things (IoT) is a disruptive technology for the future decades. Due to its pervasive growth, it is susceptible to cyber-attacks, and hence the significance of Intrusion Detection Systems (IDSs) for IoT is pertinent. The viability of machine learning has encouraged analysts to apply learning techniques to intelligently discover and recognize cyber attacks and unusual behavior among the IoTs. This paper proposes an enhanced anomaly-based Intrusion Detection Deep learning Multi-class classification model (EIDM) that can classify 15 traffic behaviors including 14 attack types with the accuracy of 95% contained in the CICIDS2017 dataset. Four state-of-the-art deep learning models are also customized to classify six classes of network traffic behavior. An extensive comparative study in terms of classification accuracy and efficiency metrics is conducted between EIDM and several state-of-the-art deep learning-based IDSs showing that EIDM has achieved accurate detection results.

Towards adversarial realism and robust learning for IoT intrusion detection and classification

The internet of things (IoT) faces tremendous security challenges. Machine learning models can be used to tackle the growing number of cyber-attack variations targeting IoT systems, but the increasing threat posed by adversarial attacks restates the need for reliable defense strategies. This work describes the types of constraints required for a realistic adversarial cyber-attack example and proposes a methodology for a trustworthy adversarial robustness analysis with a realistic adversarial evasion attack vector. The proposed methodology was used to evaluate three supervised algorithms, random forest (RF), extreme gradient boosting (XGB), and light gradient boosting machine (LGBM), and one unsupervised algorithm, isolation forest (IFOR). Constrained adversarial examples were generated with the adaptative perturbation pattern method (A2PM), and evasion attacks were performed against models created with regular and adversarial training. Even though RF was the least affected in binary classification, XGB consistently achieved the highest accuracy in multi-class classification. The obtained results evidence the inherent susceptibility of tree-based algorithms and ensembles to adversarial evasion attacks and demonstrate the benefits of adversarial training and a security-by-design approach for a more robust IoT network intrusion detection and cyber-attack classification.

A Review on IoT Intrusion Detection Systems Using Supervised Machine Learning: Techniques, Datasets, and Algorithms

Physical objects that may communicate with one another are referred to “things” throughout the Internet of Things (IoT) concept. It introduces a variety of services and activities that are both available, trustworthy and essential for human life. The IoT necessitates multifaceted security measures that prioritize communication protected by confidentiality, integrity and authentication services; data inside sensor nodes are encrypted and the network is secured against interruptions and attacks. As a result, the issue of communication security in an IoT network needs to be solved. Even though the IoT network is protected by encryption and authentication, cyber-attacks are still possible. Consequently, it’s crucial to have an intrusion detection system (IDS) technology. In this paper, common and potential security threats to the IoT environment are explored. Then, based on evaluating and contrasting recent studies in the field of IoT intrusion detection, a review regarding the IoT IDSs is offered with regard to the methodologies, datasets and machine learning (ML) algorithms. In This study, the strengths and limitations of recent IoT intrusion detection techniques are determined, recent datasets collected from real or simulated IoT environment are explored, high-performing ML methods are discovered, and the gap in recent studies is identified.