Attack and Anomaly Detection in IoT Networks using Machine Learning Techniques: A Review (original) (raw)
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Attack and Anomaly Detection in IoT Networks using Machine Learning
International Journal of Computer Science and Mobile Computing, 2020
For quite a few years now the name Internet of Things (IoT) has been around. IoT is a technology capable of revolutionizing our way of life, in sectors ranging from transportation to health, from entertainment to our interactions with government. Even this great opportunity presents a number of critical obstacles. As we strive to develop policies, regulations, and governance that form this development without stifling creativity, the increase in the number of devices and the frequency of that increase presents problems to our security and freedom. This work attentions on the security aspect of IoT networks by examining the serviceability of machine learning algorithms in detecting anomalies that are contained within such network data. It discusses (Machine Learning (ML) algorithms which are used effectively in relatively similar situations and compares them using several parameters and methods. The following algorithms are implemented in this work: Random Forest (RF), Naive Bayes (NB), Support Vector Machine (SVM), and Decision tree Algorithm. The Random Forest algorithm obtained the best results, with an accuracy of 99.5 per cent.
Machine Learning Based Attack Detection in Internet of Things Network
Vol. 19 No. 8 AUGUST 2021 International Journal of Computer Science and Information Security (IJCSIS), 2021
In recent years, the Internet of Things (IoT) has grown up rapidly and tremendously. This growth has brought big and special problems. Two of the urgent topics of problems are security and privacy for IoT devices. Those devices are creating and gathering all data in their connections. For the security of IoT, detection of anomaly attacks is the first and crucial point for avoiding any interruption in the connection. Machine Learning algorithms have been rising and improving substantially year by year. Many classic tests can detect many attacks in the current time. However, those techniques are not enough for security since the types of attacks are changing and getting stronger frequently. In this study, we propose that how to detect a maximum number of attacks in IoT networks by applying machine learning techniques, especially K-Nearest Neighbors (KNN), Logistic Regression (LR), and Random Forest (RF) models. Dataset is presumably one of the most important starting points for the use of those techniques. UNSW-NB15 dataset which is publicly available has been used for this study. K-Nearest Neighbors algorithm shows 98.03% accuracy which is the best performance within the selected algorithms. Keywords- Internet of Things, Security, Attack detection, Machine Learning, Confusion matrix, Classification report
Attack and anomaly detection in IoT sensors in IoT sites using machine learning approaches
Internet of Things, 2019
Attack and anomaly detection in the Internet of Things (IoT) infrastructure is a rising concern in the domain of IoT. With the increased use of IoT infrastructure in every domain, threats and attacks in these infrastructures are also growing commensurately. Denial of Service, Data Type Probing, Malicious Control, Malicious Operation, Scan, Spying and Wrong Setup are such attacks and anomalies which can cause an IoT system failure. In this paper, performances of several machine learning models have been compared to predict attacks and anomalies on the IoT systems accurately. The machine learning (ML) algorithms that have been used here are Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Artificial Neural Network (ANN). The evaluation metrics used in the comparison of performance are accuracy, precision, recall, f1 score, and area under the Receiver Operating Characteristic Curve. The system obtained 99.4% test accuracy for Decision Tree, Random Forest, and ANN. Though these techniques have the same accuracy, other metrics prove that Random Forest performs comparatively better.
Attack and Anomaly Detection in IoT Networks Using Supervised Machine Learning Approaches
Rev. d'Intelligence Artif., 2021
Received: 7 December 2020 Accepted: 9 February 2021 IoT is characterized by communication between things (devices) that constantly share data, analyze, and make decisions while connected to the internet. This interconnected architecture is attracting cyber criminals to expose the IoT system to failure. Therefore, it becomes imperative to develop a system that can accurately and automatically detect anomalies and attacks occurring in IoT networks. Therefore, in this paper, an Intrsuion Detection System (IDS) based on extracted novel feature set synthesizing BoT-IoT dataset is developed that can swiftly, accurately and automatically differentiate benign and malicious traffic. Instead of using available feature reduction techniques like PCA that can change the core meaning of variables, a unique feature set consisting of only seven lightweight features is developed that is also IoT specific and attack traffic independent. Also, the results shown in the study demonstrates the effectiven...
Proceedings of the 15th International Conference on Availability, Reliability and Security, 2020
The insecure growth of Internet-of-Things (IoT) can threaten its promising benefits to our daily life activities. Weak designs, low computational capabilities, and faulty protocol implementations are just a few examples that explain why IoT devices are nowadays highly prone to cyber-attacks. In this survey paper, we review approaches addressing this problem. We focus on machine learningbased solutions as a representative trend in the related literature. We survey and classify Machine Learning (ML)-based techniques that are suitable for the construction of Intrusion Detection Systems (IDS) for IoT. We contribute with a detailed classification of each approach based on our own taxonomy. Open issues and research challenges are also discussed and provided.
Machine Learning Methods for Anomaly Detection in IoT Networks, with Illustrations
Machine Learning for Networking
IoT devices have been the target of 100 million attacks in the first half of 2019 [1]. According to [2], there will be more than 64 billion Internet of Things (IoT) devices by 2025. It is thus crucial to secure IoT networks and devices, which include significant devices like medical kit or autonomous car. The problem is complicated by the wide range of possible attacks and their evolution, by the limited computing resources and storage resources available on devices. We begin by introducing the context and a survey of Intrusion Detection System (IDS) for IoT networks with a state of the art. So as to test and compare solutions, we consider available public datasets and select the CIDDS-001 Dataset. We implement and test several machine learning algorithms and show that it is relatively easy to obtain reproducible results [20] at the state-of-the-art. Finally, we discuss embedding such algorithms in the IoT context and point-out the possible interest of very simple rules.
Detection of attacks in IoT sensors networks using machine learning algorithm
Assault and peculiar location on the Internet of Things (IoT) framework is an increasing worry in the IoT region. By the expanded IoT foundation utilization in every area, assaults, and dangers in these frameworks are likewise developing proportionately. Malicious control, Spying, Forswearing of Service, Scan, Data Type Probing, Wrong setup, and malicious operation are such assaults and irregularities that may source an IOT framework disappointment. This project proposes a few Machine learning (ML) module that is contrasted with foresee assault and abnormalities on the IoT frameworks precisely. The ML algorithms that have been utilized here are Artificial Neural Network (ANN), Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT). The assessment measurements utilized in the examination of presentation are f1 score, exactness, area, recollect, and precision under the ROC Curve. Even though these strategies have similar accuracy, different measurements demonstrate that RF executes relatively preferable.
Attack and Anomaly Detection in IoT Sites Using Machine Learning Techniques
International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022
A growing problem in the IoT space is the attack and anomaly detection in the infrastructure of the Internet of Things (IoT). Every domain is using IoT infrastructure more and more, and with that use comes a surge in risks and attacks against those infrastructures. Such attacks and anomalies that can lead to an IoT system failure include Denial of Service, Data Type Probing, Malicious Control, Malicious Operation, Scan, Spying, and Wrong Setup. Logistic Regression (LR), Decision Tree (DT) and Random Forest (RF) are the machine learning (ML) methods that have been employed in this. Accuracy, precision, recall, f1 score, and area under the receiver operating characteristic curve are the evaluation measures used in performance comparison. For Decision Tree and Random Forest, the system received test accuracy results of 99.4 %. Despite the same accuracy of these algorithms, other criteria show that Random Forest performs significantly better.
Internet of Things (IoT) Intrusion Detection by Machine Learning (ML): A Review
Asia-Pacific Journal of Information Technology and Multimedia, 2023
One of today's fastest-growing technologies is the Internet of Things (IoT). It is a technology that lets billions of smart devices or objects known as "Things" collect different kinds of data about themselves and their surroundings utilizing different sensors. For example, it could be used to keep an eye on and regulate industrial services, or it could be used to improve corporate operations. But the IoT currently faces more security threats than ever before. This review paper discusses the many sorts of cybersecurity attacks that may be used against IoT devices. Also, K-Nearest Neighbour (KNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Naive Bayes (NB), and Artificial Neural Network (ANN) are examples of Machine Learning (ML) approaches that can be employed in IDS. The goal of this study is to show the results of analyzing various classification algorithms in terms of confusion matrix, accuracy, precision, specificity, sensitivity, and f-score to Develop an Intrusion Detection System (IDS) model.
Internet of Things: A survey on machine learning-based intrusion detection approaches
Computer Networks, 2019
In the world scenario, concerns with security and privacy regarding computer networks are always increasing. Computer security has become a necessity due to the proliferation of information technologies in everyday life. The increase in the number of Internet accesses and the emergence of new technologies, such as the Internet of Things (IoT paradigm, are accompanied by new and modern attempts to invade computer systems and networks. Companies are increasingly investing in studies to optimize the detection of these attacks. Institutions are selecting intelligent techniques to test and verify by comparing the best rates of accuracy. This research, therefore, focuses on rigorous state-of-the-art literature on Machine Learning Techniques applied in Internet-of-Things and Intrusion Detection for computer network security. The work aims, therefore, recent and in-depth research of relevant works that deal with several intelligent techniques and their applied intrusion detection architectures in computer networks with emphasis on the Internet of Things and machine learning. More than 95 works on the subject were surveyed, spanning across different themes related to security issues in IoT environments.