Detection of attacks in IoT sensors networks using machine learning algorithm (original) (raw)

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 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.

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 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.

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...

Using Machine Learning to Build a Classification Model for IoT Networks to Detect Attack Signatures

2020

Internet of things (IoT) has led to several security threats and challenges within society. Regardless of the benefits that it has brought with it to the society, IoT could compromise the security and privacy of individuals and companies at various levels. Denial of Service (DoS) and Distributed DoS (DDoS) attacks, among others, are the most common attack types that face the IoT networks. To counter such attacks, companies should implement an efficient classification/detection model, which is not an easy task. This paper proposes a classification model to examine the effectiveness of several machine-learning algorithms, namely, Random Forest (RF), k-Nearest Neighbors (KNN), and Naïve Bayes. The machine learning algorithms are used to detect attacks on the UNSW-NB15 benchmark dataset. The UNSW-NB15 contains normal network traffic and malicious traffic instants. The experimental results reveal that RF and KNN classifiers give the best performance with an accuracy of 100% (without nois...

Performance Evaluation of Machine Learning Classifiers on Internet of Things Security Dataset

International Journal of Control and Automation

In the recent years a huge growth has been observed in the area of Internet of Things (IoT). IoT is used as a term where things can communicate with each other without human intervention. Security is one of the key concerns when devices are communicating through Internet. There are many systems that have been made to counter these security issues. In this paper, we have evaluated Machine Learning algorithms on newly created synthetic dataset to counter the IoT security issues. This dataset contains the traces of different types of attacks in IoT. The different classification algorithms are evaluated based on different parameters i.e. accuracy, precision, recall and f-measure. After evaluation we have observed that support vector machine shows better performance on this dataset and also shows 94% accuracy, 0.95 precision, 0.94 recall and 0.94 Fmeasure.

Analysis of Machine Learning Classification Techniques for Iot Attack Vectors

2022

Internet of Things (IoT) revolution has challenged IoT security architects to great extent by exploiting the entire layered IoT architecture as attack surface for different cyber-attacks. Rather it has become easier to execute attacks due to non-standardized security architectures of IoT technologies. This study reviews the possibilities of attack surfaces available in IoT ecosystem and techniques used for early detection of malware or attacks. There are a number of attacks in which an IoT device is used as an attack surface for attacking some other system resource including attack vectors such as backdoor, password attacks, cross site scripting, ransomware, DDos, SQL injection, scanning, spying which can infect the IoT system as well as other paired devices through it. This work studies the possible attack types through IoT ecosystem and exploiting machine learning techniques in detection of attacks well in time. A set of machine learning algorithm from each family of machine learning is evaluated for one of the open-source data sets and their performance is compared for seven different IoT device types and eight types of attacks on each of the devices. The performance metrics used for evaluation of algorithms are recall, precision, F-score and accuracy. The study also presents issues related to the variation in performance of machine learning algorithms based on the composition of attributes of different types.

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.

Detections of IoT Attacks via Machine Learning-Based Approaches with Cooja

EAI Endorsed Transactions on Internet of Things

Once hardware becomes "intelligent", it is vulnerable to threats. Therefore, IoT ecosystems are susceptible to a variety of attacks and are considered challenging due to heterogeneity and dynamic ecosystem. In this study, we proposed a method for detecting IoT attacks that are based on ML-based approaches that release the final decision to detect IoT attacks. However, we have implemented three attacks as a sample in the IoT via Contiki OS to generate a real dataset of IoT-based features containing a mix of data from malicious nodes and normal nodes in the IoT network to be utilized in the ML-based models. As a result, the multiclass random decision forest ML-based model achieved 98.9% overall accuracy in detecting IoT attacks for the real novel dataset compared to the decision tree jungle, decision forest tree regression, and boosted decision tree regression, which achieved 87.7%, 93.2%, and 87.1%, respectively. Thus, the decision tree-based approach efficiently manipulate...