Design and Development of a Deep Learning-Based Model for Anomaly Detection in IoT Networks
Imtiaz Ullah,Qusay H. Mahmoud +1 more
TLDR
In this article, a novel anomaly-based IDS (Intrusion Detection System) using machine learning techniques to detect and classify attacks in IoT networks is proposed, where a convolutional neural network model is used to create a multiclass classification model.Abstract:
The growing development of IoT (Internet of Things) devices creates a large attack surface for cybercriminals to conduct potentially more destructive cyberattacks; as a result, the security industry has seen an exponential increase in cyber-attacks. Many of these attacks have effectively accomplished their malicious goals because intruders conduct cyber-attacks using novel and innovative techniques. An anomaly-based IDS (Intrusion Detection System) uses machine learning techniques to detect and classify attacks in IoT networks. In the presence of unpredictable network technologies and various intrusion methods, traditional machine learning techniques appear inefficient. In many research areas, deep learning methods have shown their ability to identify anomalies accurately. Convolutional neural networks are an excellent alternative for anomaly detection and classification due to their ability to automatically categorize main characteristics in input data and their effectiveness in performing faster computations. In this paper, we design and develop a novel anomaly-based intrusion detection model for IoT networks. First, a convolutional neural network model is used to create a multiclass classification model. The proposed model is then implemented using convolutional neural networks in 1D, 2D, and 3D. The proposed convolutional neural network model is validated using the BoT-IoT, IoT Network Intrusion, MQTT-IoT-IDS2020, and IoT-23 intrusion detection datasets. Transfer learning is used to implement binary and multiclass classification using a convolutional neural network multiclass pre-trained model. Our proposed binary and multiclass classification models have achieved high accuracy, precision, recall, and F1 score compared to existing deep learning implementations.read more
Citations
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Journal ArticleDOI
Cyber Threats Detection in Smart Environments Using SDN-Enabled DNN-LSTM Hybrid Framework
TL;DR: In this paper , the authors proposed a deep learning-driven intrusion detection system (DNNLSTM) for the Internet of Things (IoT), which is capable to encounter a tremendous class of common as well as less frequently occurring cyber threats.
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Towards a Hybrid Deep Learning Model for Anomalous Activities Detection in Internet of Things Networks
TL;DR: This paper proposes and implements a model for anomaly-based intrusion detection in IoT networks that uses a convolutional neural network (CNN) and gated recurrent unit (GRU) to detect and classify binary and multiclass IoT network data.
Journal ArticleDOI
Cyber Threats Detection in Smart Environments using SDN-enabled DNN-LSTM Hybrid Framework
Mohammad Al Razib,Danish Javeed,Muhammad Taimoor Khan,Reem Alkanhel,Mohammed Saleh Ali Muthanna +4 more
TL;DR: This scientific study proposes Deep Learning (DL) driven Software Defined Networking (SDN) enabled Intrusion Detection System (IDS) to combat emerging cyber threats in IoT.
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Using Embedded Feature Selection and CNN for Classification on CCD-INID-V1-A New IoT Dataset.
TL;DR: In this article, the authors proposed a hybrid intrusion detection system (IDS) based on an embedded model and a convolutional neural network (CNN) for attack detection and classification.
Proceedings ArticleDOI
Machine and Deep Learning Approaches for IoT Attack Classification
Alfredo Nascita,Francesco Cerasuolo,Davide Di Monda,Jonas Thern Aberia Garcia,Antonio Montieri,Antonio Pescape +5 more
TL;DR: This work employs state-of-art traffic classifiers based on deep learning and assess their effectiveness in accomplishing IoT attack classification, aiming to recognize different attack classes and distinguish them from benign network traffic.
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