A Machine Learning-based Approach for Anomaly Detection in IoT Systems (original) (raw)
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Analysis of Machine Learning Techniques for Anomaly Detection in the Internet of Things
IEEE Latin American Conference on Computational Intelligence (LA-CCI), 2018
A major challenge faced in the Internet of Things (IoT) is discovering issues that can occur in it, such as anomalies in the network or within the IoT devices. The nature of IoT hinders the identification of issues because of the huge number of devices and amounts of data generated. The aim of this paper is to investigate machine learning for effectively identifying anomalies in an IoT environment. We evaluated several state-of-the-art techniques which can identify, in real-time, when anomalies have occurred, allowing users to make alterations to the IoT network to eliminate the anomalies. Our results offer practitioners a valuable reference about which techniques might be more appropriate for their usage scenarios.
Cornell University - arXiv, 2022
The Internet of Things (IoT) is a system that connects physical computing devices, sensors, software, and other technologies. Data can be collected, transferred, and exchanged with other devices over the network without requiring human interactions. One challenge the development of IoT faces is the existence of anomaly data in the network. Therefore, research on anomaly detection in the IoT environment has become popular and necessary in recent years. This survey provides an overview to understand the current progress of the different anomaly detection algorithms and how they can be applied in the context of the Internet of Things. In this survey, we categorize the widely used anomaly detection machine learning and deep learning techniques in IoT into three types: clustering-based, classification-based, and deep learningbased. For each category, we introduce some state-of-the-art anomaly detection methods and evaluate the advantages and limitations of each technique.
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.
Study of Anomaly Detection in IoT Sensors
The rapid proliferation of Internet of Things (IoT) technology has resulted in an exponential increase in the number of connected devices and sensors. These sensors play a crucial role in collecting and transmitting data, enabling various applications and services in diverse domains. However, the large-scale deployment of IoT sensors also introduces new challenges, particularly in the realm of anomaly detection. This research paper presents a comprehensive study of anomaly detection techniques specifically designed for IoT sensors. We delve into the different types of anomalies that can occur in IoT sensor data, including sudden changes, outliers, and malicious attacks. Moreover, we explore the unique characteristics and requirements of IoT sensor networks, such as resource constraints, heterogeneous data, and dynamic network topologies. To address these challenges, we provide an overview of state-of-the-art anomaly detection methods tailored to IoT sensor networks. These methods encompass both traditional statistical approaches and machine learning algorithms, considering their applicability and effectiveness in the IoT context. We discuss the strengths and limitations of each technique, highlighting their suitability for different anomaly detection scenarios. Furthermore, we analyze and compare the performance of these methods using real-world IoT sensor datasets, evaluating their accuracy, efficiency, and scalability. The findings of our study shed light on the strengths and limitations of existing techniques, enabling researchers and practitioners to make informed decisions when choosing an appropriate anomaly detection method for their IoT sensor networks. By enhancing the reliability and security of IoT sensor networks, the outcomes of this research contribute to the advancement of IoT technology and its widespread adoption in various domains, including smart cities, healthcare, transportation, and industrial automation.
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.
A Review of Machine Learning and Deep Learning Techniques for Anomaly Detection in IoT Data
Applied Sciences
Anomaly detection has gained considerable attention in the past couple of years. Emerging technologies, such as the Internet of Things (IoT), are known to be among the most critical sources of data streams that produce massive amounts of data continuously from numerous applications. Examining these collected data to detect suspicious events can reduce functional threats and avoid unseen issues that cause downtime in the applications. Due to the dynamic nature of the data stream characteristics, many unresolved problems persist. In the existing literature, methods have been designed and developed to evaluate certain anomalous behaviors in IoT data stream sources. However, there is a lack of comprehensive studies that discuss all the aspects of IoT data processing. Thus, this paper attempts to fill this gap by providing a complete image of various state-of-the-art techniques on the major problems and core challenges in IoT data. The nature of data, anomaly types, learning mode, window...
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.
Attack and Anomaly Detection in IoT Networks using Machine Learning Techniques: A Review
Asian Journal of Research in Computer Science, 2021
The Internet of Things (IoT) is one of today's most rapidly growing technologies. It is a technology that allows billions of smart devices or objects known as "Things" to collect different types of data about themselves and their surroundings using various sensors. They may then share it with the authorized parties for various purposes, including controlling and monitoring industrial services or increasing business services or functions. However, the Internet of Things currently faces more security threats than ever before. Machine Learning (ML) has observed a critical technological breakthrough, which has opened several new research avenues to solve current and future IoT challenges. However, Machine Learning is a powerful technology to identify threats and suspected activities in intelligent devices and networks. In this paper, various ML algorithms have been compared in terms of attack detection and anomaly detection, following a thorough literature review on Machine Learning methods and the significance of IoT security in the context of various types of potential attacks. Furthermore, possible ML-based IoT protection technologies have been introduced.
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.
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.