ABSTRACT In this project, we propose a new comprehensive realistic cyber security dataset of IoT and IIoT applications, called Edge-IIoTset, which can be used by machine learning-based intrusion detection systems in two different modes, namely, centrali
The ubiquitous smart meters are expected to be a central feature of future smart grids because they enable the collection of massive amounts of fine-grained consumption data to support demand-side flexibility. However, current smart meters are not smart
datasets/mohamedamineferrag/edgeiiotset-cyber-security-dataset-of-iot-iiot About Dataset No description available Usability info License MIT Expected update frequency Not specified Tags Earth and NatureTabularDeep LearningAdvancedMobile and WirelessDNN ...
ferrag/edgeiiotset-cyber-security-dataset-of-iot-iiot -f "Edge-IIoTset dataset/Selected dataset for ML and DL/DNN-EdgeIIoT-dataset.csv" !unzip DNN-EdgeIIoT-dataset.csv.zip !rm DNN-EdgeIIoT-dataset.csv.zipStep 2: Reading the Datasets' CSV file to a Pandas DataFrame: import pandas...
I hope I’ve managed to shed some light on what Edge AI is and what it means for the future of IoT and even humankind. With SBCs and microcontrollers now joining the fray, there’s no better time to explore machine learning applications and build some Edge AI projects for yourself!
I’m going to use the common objects in context or COCO open source dataset at COCO.org. This gives me access to thousands of tagged images of various different objects, like bicycles, dogs, baseball bats, and pizza to name a few. I’m going to select bowls and click search. As I...
Edge computing is an emerging computing paradigm that has attracted a great deal of attention in recent years (Chen and Ran2019; Mendez et al.2022). The proliferation of the Internet of Things (IoT), 5G, and other cutting-edge technologies has caused a rapid increase in data generation and...
Assume there are m VMs, \(VM=\left\{ vm_1, vm_2, vm_3,\ldots , vm_m\right\}\) that are used to execute the n tasks \(T=\left\{ T_1, T_3, T_3,\dots , T_n\right\}\), where each task parameter \(T_i\) is described by a set of attributes $$\begin{aligned} ...
The optimizer in the IoT platform uses these trained parameters to optimize the global model (GM). The cost function used by the optimizer (GM) function in Algorithm 2 is shown in Eq. (3). Here, for m different edge locations, y(i) is the training dataset collected from edge location ...
where, \({n}_{k}\) is the data volume of the \(k\) th device, \({f}_{i}(w,{x}_{i},{y}_{i})\) is the loss function of the model with the parameter \(w\) on the instance \(({x}_{i},{y}_{i})\) in the \(k\) th device-local dataset. The optimization proc...