The data contains BTC Blockchain ledger of 100M, from February 3, 2009 to April 25, 2011. It also includes analysis results of the ledger: 10 characteristic indicators and 2 money laundering models.

This work intend to identify characteristics in network traffic that are able to distinguish the normal network behavior from denial of service attacks. One way to classify anomalous traffic is the data analysis of the packets header. This dataset contains labeled examples of normal traffic (23.088 instances), TCP Flood attacks (14.988 instances), UDP Flood (6.894 instances), HTTP Flood (347 instances) and HTTP Slow (183 instances) distributed in 73 numeric variables.

This dataset is benchmark dataset we use in our research for Intrusion Detection System.

Spectroreflectometric techniques for dactiloscopy and optical nail diagnostics are proposed and aproved on "DATACOLOR 3890"-based measuring system.

OWL file of PLATONT onotology and a sysml file of a reconstrcted architecure of a Raspberry-pi platform.

This Data provides a report on the number of Cyberattacks received by Zapaat Search Engine - the server being hosted in India. A list of offending IP addresses is also provided.

This dataset provides the source data for the figures and tables found in the paper "Elliptic Curve Lightweight Cryptography: a Survey."

We propose a general framework for using deep learning to detect vulnerabilities, named SySeVR. For evaluate the SySeVR, we collect the Semantics-based Vulnerability Candidate (SeVC) dataset, which contains all kinds of vulnerabilities that are available from the National Vulnerability Database (NVD) and the Software Assurance Reference Dataset (SARD).

The dataset contains several publicly available Bug Bounty Program policy documents collected from the internet.

These were used to develop the theory of Bug Bounty Program practices.

This database consists of the data used for the 2018 IEEE Signal Processing Cup.  This iteration of the Signal Processing Cup was a forensic camera model identification challenge.  Teams of undergraduate students were tasked with building a system capable of determining type of camera (manufacturer and model) that captured a digital image without relying on metadata.