Attack and Anomaly Detection in IoT Networks Using Supervised Machine Learning Approaches

Tyagi, Himani; Kumar, Rajendra

doi:10.18280/ria.350102

Cited by 29 publications

(21 citation statements)

References 20 publications

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“…Popoola et al [ 21 ] proposed Stacked RNN (SRNN), which involves cascading multiple layers of RNN. Tyagi and Kumar [ 37 ] recommended the RF model because it performed better than the k-Nearest Neighbour (kNN), Logistic Regression (LR), SVM, Multi-Layer Perceptron (MLP) and Decision Tree (DT) models. Lo et al [ 38 ] proposed the Edge-based Graph Sample and Aggregate (E-GraphSAGE) model and it outperformed the Extreme Gradient Boosting (XGBoost) and DT models.…”

Section: Review Of Related Workmentioning

confidence: 99%

SMOTE-DRNN: A Deep Learning Algorithm for Botnet Detection in the Internet-of-Things Networks

Popoola

Adebisi

Ande

et al. 2021

Sensors

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Nowadays, hackers take illegal advantage of distributed resources in a network of computing devices (i.e., botnet) to launch cyberattacks against the Internet of Things (IoT). Recently, diverse Machine Learning (ML) and Deep Learning (DL) methods were proposed to detect botnet attacks in IoT networks. However, highly imbalanced network traffic data in the training set often degrade the classification performance of state-of-the-art ML and DL models, especially in classes with relatively few samples. In this paper, we propose an efficient DL-based botnet attack detection algorithm that can handle highly imbalanced network traffic data. Specifically, Synthetic Minority Oversampling Technique (SMOTE) generates additional minority samples to achieve class balance, while Deep Recurrent Neural Network (DRNN) learns hierarchical feature representations from the balanced network traffic data to perform discriminative classification. We develop DRNN and SMOTE-DRNN models with the Bot-IoT dataset, and the simulation results show that high-class imbalance in the training data adversely affects the precision, recall, F1 score, area under the receiver operating characteristic curve (AUC), geometric mean (GM) and Matthews correlation coefficient (MCC) of the DRNN model. On the other hand, the SMOTE-DRNN model achieved better classification performance with 99.50% precision, 99.75% recall, 99.62% F1 score, 99.87% AUC, 99.74% GM and 99.62% MCC. Additionally, the SMOTE-DRNN model outperformed state-of-the-art ML and DL models.

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Section: Review Of Related Workmentioning

confidence: 99%

SMOTE-DRNN: A Deep Learning Algorithm for Botnet Detection in the Internet-of-Things Networks

Popoola

Adebisi

Ande

et al. 2021

Sensors

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“…Tyagi et al [ 15 ] developed an IDS based on extracted novel feature sets synthesizing the BoT-IoT dataset that can accurately and automatically distinguish benign and malicious traffic in real-time. An IoT-specific lightweight feature set consisting of seven lightweight features was developed instead of using existing feature reduction techniques such as principal component analysis (PCA), which can change the core meaning of variables.…”

Section: Existing Workmentioning

confidence: 99%

Anomaly Detection IDS for Detecting DoS Attacks in IoT Networks Based on Machine Learning Algorithms

Altulaihan,

Almaiah,

Aljughaiman

2024

Sensors

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Widespread and ever-increasing cybersecurity attacks against Internet of Things (IoT) systems are causing a wide range of problems for individuals and organizations. The IoT is self-configuring and open, making it vulnerable to insider and outsider attacks. In the IoT, devices are designed to self-configure, enabling them to connect to networks autonomously without extensive manual configuration. By using various protocols, technologies, and automated processes, self-configuring IoT devices are able to seamlessly connect to networks, discover services, and adapt their configurations without requiring manual intervention or setup. Users’ security and privacy may be compromised by attackers seeking to obtain access to their personal information, create monetary losses, and spy on them. A Denial of Service (DoS) attack is one of the most devastating attacks against IoT systems because it prevents legitimate users from accessing services. A cyberattack of this type can significantly damage IoT services and smart environment applications in an IoT network. As a result, securing IoT systems has become an increasingly significant concern. Therefore, in this study, we propose an IDS defense mechanism to improve the security of IoT networks against DoS attacks using anomaly detection and machine learning (ML). Anomaly detection is used in the proposed IDS to continuously monitor network traffic for deviations from normal profiles. For that purpose, we used four types of supervised classifier algorithms, namely, Decision Tree (DT), Random Forest (RF), K Nearest Neighbor (kNN), and Support Vector Machine (SVM). In addition, we utilized two types of feature selection algorithms, the Correlation-based Feature Selection (CFS) algorithm and the Genetic Algorithm (GA) and compared their performances. We also utilized the IoTID20 dataset, one of the most recent for detecting anomalous activity in IoT networks, to train our model. The best performances were obtained with DT and RF classifiers when they were trained with features selected by GA. However, other metrics, such as training and testing times, showed that DT was superior.

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“…Because of their exceptional ability to anticipate assaults and mitigate them, machine learning and deep learning [67] techniques been widely employed in a number of real-world applications [68][69][70][71][72][73][74][75][76]. The current machine learning methods are low-cost and computationally cheap, and they support the growth of Big Data.…”

Section: And DL Algorithms In Detecting and Mitigating The Attacks In...mentioning

confidence: 99%

A Novel IoT Architecture, Assessment of Threats and Their Classification with Machine Learning Solutions

Debnath,

Karmakar

et al. 2023

Journal on Internet of Things

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The Internet of Things (IoT) will significantly impact our social and economic lives in the near future. Many Internet of Things (IoT) applications aim to automate multiple tasks so inactive physical objects can behave independently of others. IoT devices, however, are also vulnerable, mostly because they lack the essential built-in security to thwart attackers. It is essential to perform the necessary adjustments in the structure of the IoT systems in order to create an end-to-end secure IoT environment. As a result, the IoT designs that are now in use do not completely support all of the advancements that have been made to include sophisticated features in IoT, such as Cloud computing, machine learning techniques, and lightweight encryption techniques. This paper presents a detailed analysis of the security requirements, attack surfaces, and security solutions available for IoT networks and suggests an innovative IoT architecture. The Seven-Layer Architecture in IoT provides decent attack detection accuracy. According to the level of risk they pose, the security threats in each of these layers have been properly categorized, and the essential evaluation criteria have been developed to evaluate the various threats. Also, Machine Learning algorithms like Random Forest and Support Vector Machines, etc., and Deep Learning algorithms like Artificial Neural Networks, Q Learning models, etc., are implemented to overcome the most damaging threats posing security breaches to the different IoT architecture layers.

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Attack and Anomaly Detection in IoT Networks Using Supervised Machine Learning Approaches

Cited by 29 publications

References 20 publications

SMOTE-DRNN: A Deep Learning Algorithm for Botnet Detection in the Internet-of-Things Networks

SMOTE-DRNN: A Deep Learning Algorithm for Botnet Detection in the Internet-of-Things Networks

Anomaly Detection IDS for Detecting DoS Attacks in IoT Networks Based on Machine Learning Algorithms

A Novel IoT Architecture, Assessment of Threats and Their Classification with Machine Learning Solutions

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