Intrusion Detection for IoT Devices based on RF Fingerprinting using Deep Learning

Bassey, Joshua; Adesina, Damilola; Li, Xiangfang; Qian, Lijun; Aved, Alexander; Kroecker, Timothy

doi:10.1109/fmec.2019.8795319

Cited by 47 publications

(22 citation statements)

References 11 publications

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“…Their model had a high detection rate for specific attack categories but a low detection rate for others. Table I [43] Autoencoder, SVM UNSW-NB15 Binary Acc=97.00 [44] MLP, CNN, DNN UNSW-NB15 Binary Acc=99.24 [45] CNN UNSW-NB15 -------------Acc=89.02 [46] CNN UNSW-NB15 -------------Acc=96 [47] Stacked Autoencoders Personal Dataset -------------Acc=95.00 [48] LSTM, GRU Personal Dataset Multiclass Acc =96.08 [49] Feed Forward DNN Personal Dataset Binary Recall =97 [50] CNN Personal Dataset - ------------Acc=98.80 [51] MLP Personal Dataset - -----------…”

Section: Related Workmentioning

confidence: 99%

Design and Development of a Deep Learning-Based Model for Anomaly Detection in IoT Networks

2021

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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 anomalybased 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.

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Section: Related Workmentioning

confidence: 99%

Design and Development of a Deep Learning-Based Model for Anomaly Detection in IoT Networks

2021

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“…Deep learning algorithms use many deeper hidden layers to surpass classical ANN methods. [46,47]. A convolutional neural network is one of the most popular deep neural network algorithms, and it is named convolution by using mathematical linear operation between matrices.…”

Section: Correlation Analysismentioning

confidence: 99%

Intrusion Detection System to Advance Internet of Things Infrastructure-Based Deep Learning Algorithms

Alkahtani

Aldhyani

2021

Complexity

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Smart grids, advanced information technology, have become the favored intrusion targets due to the Internet of Things (IoT) using sensor devices to collect data from a smart grid environment. These data are sent to the cloud, which is a huge network of super servers that provides different services to different smart infrastructures, such as smart homes and smart buildings. These can provide a large space for attackers to launch destructive cyberattacks. The novelty of this proposed research is the development of a robust framework system for detecting intrusions based on the IoT environment. An IoTID20 dataset attack was employed to develop the proposed system; it is a newly generated dataset from the IoT infrastructure. In this framework, three advanced deep learning algorithms were applied to classify the intrusion: a convolution neural network (CNN), a long short-term memory (LSTM), and a hybrid convolution neural network with the long short-term memory (CNN-LSTM) model. The complexity of the network dataset was dimensionality reduced, and to improve the proposed system, the particle swarm optimization method (PSO) was used to select relevant features from the network dataset. The obtained features were processed using deep learning algorithms. The experimental results showed that the proposed systems achieved accuracy as follows: CNN = 96.60%, LSTM = 99.82%, and CNN-LSTM = 98.80%. The proposed framework attained the desired performance on a new variable dataset, and the system will be implemented in our university IoT environment. The results of comparative predictions between the proposed framework and existing systems showed that the proposed system more efficiently and effectively enhanced the security of the IoT environment from attacks. The experimental results confirmed that the proposed framework based on deep learning algorithms for an intrusion detection system can effectively detect real-world attacks and is capable of enhancing the security of the IoT environment.

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“…Bassey et al [133] applied deep learning in an IDS model to nd unauthorized devices in IoT using radiofrequency ngerprinting(RF), which are hard to impersonate and are collected from six identical ZigBee devices. A CNN is used to nd appropriate features from the RF traces, and de-correlation is performed on these features.…”

Section: Cnn-based Schemesmentioning

confidence: 99%

A Survey of Deep Learning Techniques for Misuse-Based Intrusion Detection Systems 

Lánský

Mohammadi

Mohammed

et al. 2021

Preprint

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The ever-increasing complication and severity of the computer networks' security attacks have inspired security researchers to apply various machine learning methods to protect the organizations' data and reputation. Deep learning is one of the exciting techniques that recently have been widely used by intrusion detection systems (IDS) to secure computer networks and hosts' performance. This survey article focuses on the signature-based IDS using deep learning techniques and puts forward an in-depth survey and classification of these schemes. For this purpose, it first presents the essential background concepts about IDS architecture and various deep learning techniques. It then classifies these schemes according to the type of deep learning methods applied in each of them. It describes how deep learning networks are utilized in the misuse detection process to recognize intrusions accurately. Finally, a complete analysis of the investigated IDS frameworks is provided, and concluding remarks and future directions are highlighted.

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Intrusion Detection for IoT Devices based on RF Fingerprinting using Deep Learning

Cited by 47 publications

References 11 publications

Design and Development of a Deep Learning-Based Model for Anomaly Detection in IoT Networks

Design and Development of a Deep Learning-Based Model for Anomaly Detection in IoT Networks

Intrusion Detection System to Advance Internet of Things Infrastructure-Based Deep Learning Algorithms

A Survey of Deep Learning Techniques for Misuse-Based Intrusion Detection Systems

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Intrusion Detection for IoT Devices based on RF Fingerprinting using Deep Learning

Cited by 47 publications

References 11 publications

Design and Development of a Deep Learning-Based Model for Anomaly Detection in IoT Networks

Design and Development of a Deep Learning-Based Model for Anomaly Detection in IoT Networks

Intrusion Detection System to Advance Internet of Things Infrastructure-Based Deep Learning Algorithms

A Survey of Deep Learning Techniques for Misuse-Based Intrusion Detection Systems&nbsp;

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A Survey of Deep Learning Techniques for Misuse-Based Intrusion Detection Systems