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

Idrissi, Idriss; Azizi, Mostafa; Moussaoui, Omar

doi:10.11591/ijeecs.v25.i2.pp1140-1150

Cited by 20 publications

(8 citation statements)

References 22 publications

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“…It is considered a black box making it hard for the security officer to know the root causes of the security attacks or anomalies. Note that deep learning can be seen as a generative approach with the emergence of generative adversarial networks [8]. Several works based on machine learning for security intrusion in IoT were proposed in the literature.…”

Section: Intrusion and Outlier Detectionmentioning

confidence: 99%

An Integrated Multilayered Framework for IoT Security Intrusion Decisions

Sallay¹

2023

Intelligent Automation &Amp; Soft Computing

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Security breaches can seriously harm the Internet of Things (IoT) and Industrial IoT (IIoT) environments. The damage can exceed financial and material losses to threaten human lives. Overcoming these security risks is challenging given IoT ubiquity, complexity, and restricted resources. Security intrusion management is a cornerstone in fortifying the defensive security process. This paper presents an integrated multilayered framework facilitating the orchestration of the security intrusion management process and developing security decision support systems. The proposed framework incorporates four layers with four dedicated processing phases. This paper focuses mainly on the analytical layer. We present the architecture and models for predictive intrusion analytics for reactive and proactive defense strategies. We differentiate between the device and network levels to master the complexity of IoT infrastructure. Benefiting from the singularity of IIoT devices traffic, we approach the reactive security intrusion prediction through outlier detection models mean. We thoroughly experiment with ten outlier detection models on the IIoT wustl realistic dataset. The obtained results show the adequacy of the approach with an area under the curve (AUC) results surpassing 98% for several models with a good level of precision and time efficiency. Furthermore, we investigate the use of survival analysis semi-parametric predictive models to forecast the security intrusion before its occurrence for the proactive security strategy. The experiments show encouraging results with a concordance index (c-Index) reaching 89% and an integrated brier score (IBS) of 0.02. By integrating outlier intrusion detection and survival forecasting, the framework provides a valuable means to monitor the security intrusions in IoT.

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Section: Intrusion and Outlier Detectionmentioning

confidence: 99%

An Integrated Multilayered Framework for IoT Security Intrusion Decisions

Sallay¹

2023

Intelligent Automation &Amp; Soft Computing

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“…A common challenge in deep learning is obtaining datasets that are sufficiently large and diverse in nature for the task at hand [26], [27]. The dataset used for training our models is comprised of a large number of images captured of wildfires in different locations around the world, as well as images of forest landscapes with no fire.…”

Section: A Datasetmentioning

confidence: 99%

Forest Fires Detection using Deep Transfer Learning

YANDOUZI¹,

GRARI²,

IDRISSI³

et al. 2022

IJACSA

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Forests are vital ecosystems composed of various plant and animal species that have evolved over years to coexist. Such ecosystems are often threatened by wildfires that can start either naturally, as a result of lightning strikes, or unintentionally caused by humans. In general, human-caused fires are more severe and expensive to fight because they are frequently located in inaccessible areas. Wildfires can spread quickly and become extremely dangerous, causing damage to homes and facilities, as well as killing people and animals. Early discovery of wildfires is vital to protect lives, property, and resources. Reinforced imaging technologies can play a key role to detect wildfires earlier. By applying deep learning (DL) over a dataset of images (collected using drones, planes, and satellites), we target to automate the forest fire detection. In this paper, we focus on building a DL model specifically to detect wildfires using transfer learning techniques from the best pretrained DL computer vision architectures available nowadays, such as VGG16,

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“…DL applications utilize an artificial neural network (ANN) to achieve this. A neural network inspired by the human brain's biological neural network is used to create an ANN that is much more competent than traditional machine learning models at learning [28].…”

Section: Deep Learning (Dl)mentioning

confidence: 99%

Review on maintenance of photovoltaic systems based on deep learning and internet of things

Hammoudi¹,

Idrissi²,

Boukabous³

et al. 2022

IJEECS

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Many solar plants have been installed globally, and they must be continuously protected and supervised to ensure their safety and reliability. Photovoltaic plants are susceptible to many defects and failures, and fault detection technology is used to protect and isolate them. Despite numerous inter-national standards, invisible photovoltaic defects continue to cause major is-sues. As a result, smart technologies like AI (Artificial Intelligence) and IoT are being developed for remote sensing, problem detection, and diagnosis of photovoltaic systems. Solar plants generate not only green electricity but also a lot of data, such as power output. With AI, a clear picture of electricity yields should be possible. The output of entire solar parks could be monitored and analyzed. The AI could also detect malfunctions within a solar park, according to the research. This would speed up and simplify maintenance work. Deep learning (DL) and IoT applications for photovoltaic plants are discussed. The most advanced techniques, such as DL, are discussed in terms of precision and accuracy. Incorporating DL and IoT approaches for fault detection and diagnosis into simple hardware, such as low-cost chips, maybe cost-effective and technically feasible for photovoltaic facilities located in remote locations.

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

Cited by 20 publications

References 22 publications

An Integrated Multilayered Framework for IoT Security Intrusion Decisions

An Integrated Multilayered Framework for IoT Security Intrusion Decisions

Forest Fires Detection using Deep Transfer Learning

Review on maintenance of photovoltaic systems based on deep learning and internet of things

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