It is important for network operators to carry out traffic and application characterization to gain insights into the activity of their networks. Several studies proposed methods that extract features from network traffic to characterize it, or to classify the application that produced it, based on a ?man in the middle? network interception point that can analyze the entire network traffic of an organization.
The network attacks are increasing both in frequency and intensity with the rapid growth of internet of things (IoT) devices. Recently, denial of service (DoS) and distributed denial of service (DDoS) attacks are reported as the most frequent attacks in IoT networks. The traditional security solutions like firewalls, intrusion detection systems, etc., are unable to detect the complex DoS and DDoS attacks since most of them filter the normal and attack traffic based upon the static predefined rules.
Any work using this dataset should cite this paper as follows:
Nirmalya Thakur and Chia Y. Han, "Country-Specific Interests towards Fall Detection from 2004–2021: An Open Access Dataset and Research Questions", Journal of Data, Volume 6, Issue 8, pp. 1-21, 2021.
Abstract
The Internet of things (IoT) has emerged as a topic of intense interest among the research and industrial community as it has had a revolutionary impact on human life. The rapid growth of IoT technology has revolutionized human life by inaugurating the concept of smart devices, smart healthcare, smart industry, smart city, smart grid, among others. IoT devices’ security has become a serious concern nowadays, especially for the healthcare domain, where recent attacks exposed damaging IoT security vulnerabilities. Traditional network security solutions are well established.
In this paper, the daily price data of constituents of the S&P 500 index, which dates from 2005 to 2021, are selected to train and test by the RNN model.
Using Wi-Fi IEEE 802.11 standard, radio frequency waves are mainly used for communication on various devices such as mobile phones, laptops, and smart televisions. Apart from communication applications, the recent research in wireless technology has turned Wi-Fi into other exploration possibilities such as human activity recognition (HAR). HAR is a field of study that aims to predict motion and movement made by a person or even several people.
The given Dataset is record of different group people either healthy subjects or subclinical cardiovascular disease(CVD) with history coronary heart disease or hypertension for superficial body features, original photoplethysmography imaging(iPPG) signal and characteristics.
The main purpose of the dataset is to understand the relationship between CVD and high-dimensional ippg characteristics.
Industrial Internet of Things (IIoTs) are high-value cyber targets due to the nature of the devices and connectivity protocols they deploy. They are easy to compromise and, as they are connected on a large scale with high-value data content, the compromise of any single device can extend to the whole system and disrupt critical functions. There are various security solutions that detect and mitigate intrusions.
The IEEE 24 bus system is modelled in Digsilent’s Power factory, and normal operating data points are generated under quasi dynamic simulation and N-2 contingencies. An optimization problem to generate false data injection attack vectors are then modelled using MATLAB. This data contains normal, contingency and attack data points for the necessary variables in a power network. The attack data has 5 parts, with each part generated by a different type of attack.
Brain-Computer Interface (BCI) has become an established technology to interconnect a human brain and an external device. One of the most popular protocols for BCI is based on the extraction of the so-called P300 wave from EEG recordings. P300 wave is an event-related potential with a latency of 300 ms after the onset of a rare stimulus. In this paper, we used deep learning architectures, namely convolutional neural networks (CNNs), to improve P300-based BCIs.
