A Stratified IoT Deep Learning based Intrusion Detection System

Idrissi, Idriss; Azizi, Mostafa; Moussaoui, Omar

doi:10.1109/iraset52964.2022.9738045

Cited by 15 publications

(7 citation statements)

References 15 publications

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“…After generating this model based on the best-achieved ML algorithm (the base work in this paper), we intend to deploy it in an IoT device equipped with different sensors that may theoretically gather the same characteristics as the FIRMS dataset; such as a camera (see Figure 2). These IoT devices (ideally a camera or an IoT device equipped with a camera such as a drone, or buying and using satellite imagery [43]) can either make this prediction locally (meaning in the edge layer [44]) or can pass the data to the gateway, where a more powerful IoT device (in the fog layer) can make this prediction. This IoT should be an AI-enabled circuit (which is found in the market at low-cost) or can be on a device with higher processing power, preferably linked to a lightweight neural network hardware accelerator (like the Intel Neural Compute Stick 2, Google Coral edge TPU, or Nvidia jetson nano) [45].…”

Section: Proposed Methodsmentioning

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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<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: Proposed Methodsmentioning

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

View full text Add to dashboard Cite

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“…The network is first trained on a large dataset of images that contain the objects to detect [18]. The network learns to identify the features associated with each object and to associate a specific class with each of these features [19]. After the network has been trained, it can be applied to new images to detect the presence of the desired objects.…”

Section: Convolutional Neural Network Architecturesmentioning

confidence: 99%

Image and video-based crime prediction using object detection and deep learning

Boukabous¹,

Azizi²

2023

Bulletin EEI

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In recent years, the use of artificial intelligence (AI) for image and video-based crime detection has gained significant attention from law enforcement agencies and security experts. Indeed, deep learning (DL) models can learn complex patterns from data and help law enforcement agencies save time and resources by automatically identifying and tracking potential criminals. This contributes to make deep investigations and better steer their targets’ searches. Among others, handheld firearms and bladed weapons are the most frequent objects encountered at crime scenes. In this paper, we propose a DL-based surveillance system that can detect the presence of tracked objects, such as handheld firearms and bladed weapons, as well as may proceed to alert authorities regarding eventual threats before an incident occurs. After making a comparison of different DL-based object detection techniques, such as you only look once (YOLO), single shot multibox detector (SSD), or faster region-based convolutional neural networks (R-CNN), YOLO achieves the optimal balance of mean average precision (mAP) and inference speed for real-time prediction. Thus, we retain YOLOv5 for the implementation of our solution.

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“…Deep learning, inspired by the brain, allows a computer to deeply learn from data by creating relevant large neuronal network models [7].…”

Section: B Deep Learning (Dl)mentioning

confidence: 99%

Investigation of Combining Deep Learning Object Recognition with Drones for Forest Fire Detection and Monitoring

YANDOUZI¹,

GRARI²,

BERRAHAL³

et al. 2023

IJACSA

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Forest fires are a global environmental problem that can cause significant damage to natural resources and human lives. The increasing frequency and severity of forest fires have resulted in substantial losses of natural resources. To mitigate this, an effective fire detection and monitoring system is crucial. This work aims to explore and review the current advancement in the field of forest fire detection and monitoring using both drones or unmanned aerial vehicles (UAVs), and deep learning techniques. The utilization of drones fully equipped with specific sensors and cameras provides a cost-effective and efficient solution for real-time monitoring and early fire detection. In this paper, we conduct a comprehensive analysis of the latest developments in deep learning object detection, such as YOLO (You Only Look Once), R-CNN (Region-based Convolutional Neural Network), and their variants, with a focus on their potential application in the field of forest fire monitoring. The performed experiments show promising results in multiple metrics, making it a valuable tool for fire detection and monitoring.

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A Stratified IoT Deep Learning based Intrusion Detection System

Cited by 15 publications

References 15 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

Image and video-based crime prediction using object detection and deep learning

Investigation of Combining Deep Learning Object Recognition with Drones for Forest Fire Detection and Monitoring

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