Machine learning approaches have been discovered to be useful tools in cybersecurity strategies, including main fraud detection and discovering harmful activities.
Machine learning may also be utilized in a wide range of cybersecurity use cases, such as the identification of malicious pdf files, malware domain detection, intrusion detection, imitation attack detection, and more.
These AI processes include data sets of quartiles that are first determined using the free quartile calculator and then used to run backend programs.
The top datasets for your next cybersecurity project are given here below.
Table of Contents
Set of Malicious URLs:
About: The Malicious URLs dataset contains 3.2 million features and 2.4 million URLs. There are two types of datasets available:
- Matlab
- SVM-light
The file URL. mat in Matlab format provides the list of column indices for the data matrices that are real-valued features and are called Feature Types. The Feature Types is a text file set of feature indices that represent real-valued features in SVM-light format.
This text file is based on set quartiles as calculated for the data set through a lower quartile calculator.
What Role Does Cybersecurity Play?
The research done helps to explain the significance of cybersecurity. According to the survey, ransomware has increased by 26%, email-based spoofing has been observed by 88% of businesses, and impersonation fraud has increased by 67% of enterprises.
Utilizing public Wi-Fi increases the attack area for your device or data. 54% of internet users use public Wi-Fi, and 73% of people are aware that it is unsafe, even if it is password-protected. These facts demonstrate the urgent necessity for cybersecurity.
ISOT Cloud Intrusion Detection (ISOT CID) Dataset:
About: The ISOT Cloud IDS (ISOT CID) dataset comprises over 8Tb of data that is gathered in a genuine cloud environment. It includes system logs, performance statistics (such as CPU utilization), and system calls in addition to network traffic at the VM and hypervisor levels.
A compilation of varied data, including information from guest hosts, hypervisors, and networks, is included in the ISOT-CID. The dataset includes information from several sources and different forms, such as memory dumps, resource (such as CPU) utilization logs, system call records, computer logs, and traffic from the network.
The basic building block of ISOT is based on quartile calculations that are instantly done by using the quartile calculator. After the calculations are done, the procedure is continued.
Behavioral Biometric Datasets:
The mouse dynamics dataset, mouse gesture dynamics dataset, combined mouse/keystroke dynamics/site actions dataset, and mobile keystroke dynamics OTP dataset are the four types of datasets that make up the ISOT Behavioral Biometric dataset.
The mouse dynamics data for 48 users that were gathered over several months make up the ISOT mouse dynamics dataset. The Mouse Gesture Dynamics dataset includes real gesture data created by 41 people and forged data created against 25 distinct people.
First Quartile Calculator:
The online tool has provided a straightaway path to the calculations that are considered necessary for the programming. What would you do when you have to deal with a large data set of values?
No doubt a frame will come that will make you frustrated. This is where the quartile calculator comes into play. It divides the data set into first and third upper quartiles so that you may not feel difficulty in developing a code step by step.
EMBER:
The EMBER dataset, which is a collection of characteristics from PE files, is used by academics as a benchmark dataset. It is a publicly available dataset for developing machine learning models that statically identify malware Windows portable executable files. From 1.1M binary files, the dataset has the following features:
- 300K harmful,
- 300K benign, and
- 300K unlabeled training samples
Wrapping It Up:
In the following article, we had a brief overview of the different cybersecurity data sets and the role of the quartile calculator in programming and efforts against cyberattacks.
