Accelerating the update of a DL-based IDS for IoT using deep transfer learning

Idrissi, Idriss; Azizi, Mostafa; Moussaoui, Omar

doi:10.11591/ijeecs.v23.i2.pp1059-1067

Cited by 34 publications

(25 citation statements)

References 18 publications

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“…Machine learning (ML) is a subfield of artificial intelligence (AI) that relies on using data and algorithms to mimic the way people learn and improve accuracy over time [18]. ML is a key element of the rapidly expanding area of data science.…”

Section: Machine Learningmentioning

confidence: 99%

Early wildfire detection using machine learning model deployed in the fog/edge layers of IoT

Grari¹,

Idrissi²,

Boukabous³

et al. 2022

IJEECS

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<span lang="EN-US">The impact of wildfires, even following the fire's extinguishment, continues to affect harmfully public health and prosperity. Wildfires are becoming increasingly frequent and severe, and make the world's biodiversity in a growing serious danger. The fires are responsible for negative economic consequences for individuals, corporations, and authorities. Researchers are developing new approaches for detecting and monitoring wildfires, that make use of advances in computer vision, machine learning, and remote sensing technologies. IoT sensors help to improve the efficiency of detecting active forest fires. In this paper, we propose a novel approach for predicting wildfires, based on machine learning. It uses a regression model that we train over NASA's fire information for resource management system (FIRMS) dataset to predict fire radiant power in megawatts. The analysis of the obtained simulation results (more than 99% in the R2 metric) shows that the ensemble learning model is an effective method for predicting wildfires using an IoT device equipped with several sensors that could potentially collect the same data as the FIRMS dataset, such as smart cameras or drones.</span>

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Section: Machine Learningmentioning

confidence: 99%

Early wildfire detection using machine learning model deployed in the fog/edge layers of IoT

Grari¹,

Idrissi²,

Boukabous³

et al. 2022

IJEECS

Self Cite

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“…There exists a multitude of IDS systems. According to the different taxonomies of IDS systems, we distinguish the network intrusion detection system (NIDS) [19], an IDS capable of analyzing incoming network traffic, and the host intrusion detection system (HIDS), an IDS capable of monitoring sensitive operating system files, and Hybrid IDS solution that combines the two solutions to ease the weakness of the other two categories [20].…”

Section: Intrusion Detection System (Ids)mentioning

confidence: 99%

An unsupervised generative adversarial network based-host intrusion detection system for internet of things devices

Idrissi¹,

Azizi²,

Moussaoui³

2022

IJEECS

Self Cite

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Machine learning (ML) and deep learning (DL) have achieved amazing progress in diverse disciplines. One of the most efficient approaches is unsupervised learning (UL), a sort of algorithms for analyzing and clustering unlabeled data; it allows identifying hidden patterns or performing data clustering over provided data without the need for human involvement. There is no prior knowledge of actual abnormalities when using UL methods in anomaly detection (AD); hence, a DL-intrusion detection system (IDS)- based on AD depends intensely on their assumption about the distribution of anomalies. In this paper, we propose a novel unsupervised AD Host-IDS for internet of things (IoT) based on adversarial training architecture using the generative adversarial network (GAN). Our proposed IDS, called “EdgeIDS”, targets mostly IoT devices because of their limited functionality; IoT devices send and receive only specific data, not like traditional devices, such as servers or computers that exchange a wide range of data. We benchmarked our proposed “EdgeIDS” on the message queuing telemetry transport (MQTTset) dataset with five attack types, and our obtained results are promising, up to 0.99 in the ROC-AUC metric, and to just 0.035 in the ROC-EER metric. Our proposed technique could be a solution for detecting cyber abnormalities in the IoT.

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“…Idrissi et al [ 23 ] also propose the usage of transfer learning to overcome the limitation of traditional DL-based IDS on the detection of novel attacks in IoT environments with few labeled data. Their solution retrains a fine-tuned pretrained model where most of the layers are fixed and just the last ones are trained using a CNN.…”

Section: Related Workmentioning

confidence: 99%

Transfer-Learning-Based Intrusion Detection Framework in IoT Networks

Rodríguez

Valls

Otero

et al. 2022

Sensors

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Cyberattacks in the Internet of Things (IoT) are growing exponentially, especially zero-day attacks mostly driven by security weaknesses on IoT networks. Traditional intrusion detection systems (IDSs) adopted machine learning (ML), especially deep Learning (DL), to improve the detection of cyberattacks. DL-based IDSs require balanced datasets with large amounts of labeled data; however, there is a lack of such large collections in IoT networks. This paper proposes an efficient intrusion detection framework based on transfer learning (TL), knowledge transfer, and model refinement, for the effective detection of zero-day attacks. The framework is tailored to 5G IoT scenarios with unbalanced and scarce labeled datasets. The TL model is based on convolutional neural networks (CNNs). The framework was evaluated to detect a wide range of zero-day attacks. To this end, three specialized datasets were created. Experimental results show that the proposed TL-based framework achieves high accuracy and low false prediction rate (FPR). The proposed solution has better detection rates for the different families of known and zero-day attacks than any previous DL-based IDS. These results demonstrate that TL is effective in the detection of cyberattacks in IoT environments.

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Accelerating the update of a DL-based IDS for IoT using deep transfer learning

Cited by 34 publications

References 18 publications

Early wildfire detection using machine learning model deployed in the fog/edge layers of IoT

Early wildfire detection using machine learning model deployed in the fog/edge layers of IoT

An unsupervised generative adversarial network based-host intrusion detection system for internet of things devices

Transfer-Learning-Based Intrusion Detection Framework in IoT Networks

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