Enhancing Security of Host-based Intrusion Detection Systems for the Internet of Things (original) (raw)
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A Comparative Study of Machine Learning Algorithms for Intrusion Detection in IoT Networks
ria_37.03_05, 2023
The pervasive threat of cyberattacks jeopardizes the security and privacy of the Internet of Things (IoT) landscape, spanning devices to networks. To counter these attacks, research has been directed towards the development of effective and appropriate countermeasures. Intrusion Detection Systems (IDSs), particularly those leveraging Machine Learning (ML) techniques for expedited attack detection, are currently recognized as some of the most potent solutions for preserving the integrity of the IoT environment. This study was conducted with the objective of evaluating the efficacy of supervised Machine Learning techniques, specifically, Random Forest (RF), Decision Trees (DT), and XGBoost classifiers, in detecting attacks within the IoT network. Chi-square (Chi2) and Mutual Information served as the employed Feature Selection Techniques. The research utilized two recent datasets for model evaluation. In pursuit of an optimal solution and high IDS model accuracy, a comparison of different techniques was undertaken across each stage of the ML workflow. The performance of the algorithms was assessed using the Edge-IIoT and BoTNeTIoT datasets, and the results from the two were compared. The impact of each workflow step on the model's accuracy was also examined. According to the performance metrics, the best results were achieved with the Mutual Information and XGBoost combination, outperforming both the Random Forest and Decision Tree classifiers. This study thus contributes to the ongoing efforts to strengthen IoT security through enhanced intrusion detection techniques.
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.
An Experimental Analysis of Attack Classification Using Machine Learning in IoT Networks
Sensors
In recent years, there has been a massive increase in the amount of Internet of Things (IoT) devices as well as the data generated by such devices. The participating devices in IoT networks can be problematic due to their resource-constrained nature, and integrating security on these devices is often overlooked. This has resulted in attackers having an increased incentive to target IoT devices. As the number of attacks possible on a network increases, it becomes more difficult for traditional intrusion detection systems (IDS) to cope with these attacks efficiently. In this paper, we highlight several machine learning (ML) methods such as k-nearest neighbour (KNN), support vector machine (SVM), decision tree (DT), naive Bayes (NB), random forest (RF), artificial neural network (ANN), and logistic regression (LR) that can be used in IDS. In this work, ML algorithms are compared for both binary and multi-class classification on Bot-IoT dataset. Based on several parameters such as accur...
2015
The Internet of Things (IoT) is a worldwide network including all identifiable heterogeneous objects around us such as smartphones, laptops or smart sensors that can connect to the Internet by using a wide range of technologies. IoT is able to provide accessibility to the Internet for all physical objects since it is a hybrid network of the Internet and diverse networks with heterogeneous nodes. Generally, due to the insecure nature of the Internet as well as Wireless Sensor Networks, which are the main components of IoT, implementing security mechanisms in IoT seems necessary. To deal with intrusions that may occur in IoT, a novel multi-faceted intrusion detection system is proposed in this thesis which can detect both cyber-attacks and insider-attacks of IoT. This study proposes an offline misused-based technique based on a modified supervised Optimum-Path Forest (OPF) to detect the external (cyber) attacks of IoT. In this technique, k-Means algorithm, along with the social network analysis (i.e., centrality and prestige), is employed to overcome the problem of the scalability of the input large dataset and prune the training dataset with the aim of identifying the most informative samples. The proposed method uses a high-dimensional NSL-KDD dataset as the simulated traffic of the Internet; hence, this thesis presents a novel hybrid method based on Binary Gravitational Search Algorithm and Mutual Information to reduce the dimensions of the original input dataset. The experimental results show the superior performance of the proposed approach in detecting and identifying the types of cyber-attacks. In addition, this study provides a hybrid of anomaly-based and specification-based real-time intrusion detection system to determine routing attacks in 6LoWPAN (the main effort to make the concept of real IoT) based on the unsupervised OPF. The proposed method, which is an efficient security technique not only can detect internal (insider) attacks of IoT but also can determine the malicious nodes as the cause of the IoT's insider-attacks. In addition, the presented model is developed based on the MapReduce architecture in order to obtain the ability of distributed detection.
2021
A significant increase in the number of interconnected devices and data communication through wireless networks has given rise to various threats, risks and security concerns. Internet of Things (IoT) applications is deployed in almost every field of daily life, including sensitive environments. The edge computing paradigm has complemented IoT applications by moving the computational processing near the data sources. Among various security models, Machine Learning (ML) based intrusion detection is the most conceivable defense mechanism to combat the anomalous behavior in edge-enabled IoT networks. The ML algorithms are used to classify the network traffic into normal and malicious attacks. Intrusion detection is one of the challenging issues in the area of network security. The research community has proposed many intrusion detection systems. However, the challenges involved in selecting suitable algorithm(s) to provide security in edge-enabled IoT networks exist. In this paper, a c...
2019
Devices and objects connected together on the internet form a network of interconnected devices referred to as Internet of Things (IoT). A typical IoT network comprises of heterogeneous devices all connecting seamlessly enabling interoperability, scalability and deployment of applications. There has been a significant increase in IoT objects, which has in turn augmented IoT-based attacks. The nature of IoT networks require intelligent methods of detection and prevention. Machine-Learning Algorithms have previously been implemented to detect and prevent cyber-based attacks. The outcome of this research is to assess and propose more effective and efficient Machine-Learning Algorithms. This paper underscores current Machine Learning Algorithm techniques in Network Intrusion Malware detection, anomaly detection and privacy detection. Evaluations and experiments were conducted on the Algorithms. Supervised Machine-Learning Algorithms relied on NSL-KDD dataset consisting of 41 features and KDD99 Dataset'. Statistical and comparative analyses were also conducted to evaluate results. Privacy detection was evaluated by simulation. In conclusion, the research established that the enhancement and combination of algorithms achieved accuracy, precision and efficiency. Neural Networks in Algorithms displayed significant limitations but could offer efficient and effective algorithms due to their predictive capabilities. This paper finds it critical for further and more research in the area of fuzzy logic in machine learning algorithms with respect to IoT based attacks.
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.
International Journal of Electrical and Computer Engineering (IJECE), 2024
Within an internet of things (IoT) environment, the fundamental purpose of various devices is to gather the abundant amount of data that is being generated and then transmit this data to the predetermined server over the internet. IoT connects billions of objects and the internet to communicate without human intervention. But network security and privacy issues are increasing very fast, in today's world. Because of the prevalence of technological advancement in regular activities, internet security has evolved into a necessary requirement. Because technology is integrated into every aspect of contemporary life, cyberattacks on the internet of things represent a bigger danger than attacks against traditional networks. Researchers have found that combining machine learning techniques into an intrusion detection system (IDS) is an efficient way to get beyond the limitations of conventional IDSs in an IoT context. This research presents a comprehensive literature assessment and develops an intrusion detection system that makes use of machine learning techniques to address security problems in an IoT environment. Along with a comprehensive look at the state of the art in terms of intrusion detection systems for IoT-enabled environments, this study also examines the attributes of approaches, common datasets, and existing methods utilized to construct such systems.
UHD Journal of Science and Technology
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...
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