Normal
0

false
false
false

EN-US
X-NONE
AR-SA

Categories:
368 Views

The dataset contains measurement results of Radar Cross Section of different Unmanned Aerial Vehicles at 26-40 GHz. The measurements have been performed fro quasi-monostatic case (when the transmitter and receiver are spatially co-located) in the anechoic chamber. The data shows how radio waves are scattered by different UAVs at the specified frequency range.

 

 

Instructions: 

Some of DJI, Walkera, Parrot and Kyosho drones were measured.

The data is in ".csv" format. Each file contains the following information: frequency, theta, phi, and RCS.

The RCS signatures of the following drone models are available:

-DJI Phantom 4 Pro

-DJI F450

-DJI Mavic Pro

-Helicopter Kyosho

-Parrot AR.drone

-DJI Matrice M100

-Walkera Voyager 4

-Custom built hexacopter

-Tricopter HMF600, frame only

Polarization is mentioned in the file name:

  • HH - horizontal polarisation of the transmitter and the receiver

  • HV/VH - horizontal and vertical or vice versa

  • VV - vertical polarization of the transmitter and the receiver

In addition, 6S LiPo battery RCS is available.

 

 Published article can be found at: https://ieeexplore.ieee.org/document/9032332

Categories:
3654 Views

The proliferation of IoT systems, has seen them targeted by malicious third parties. To address this challenge, realistic protection and investigation countermeasures, such as network intrusion detection and network forensic systems, need to be effectively developed. For this purpose, a well-structured and representative dataset is paramount for training and validating the credibility of the systems. Although there are several network datasets, in most cases, not much information is given about the Botnet scenarios that were used.

Categories:
8845 Views

One of the major research challenges in this field is the unavailability of a comprehensive network based data set which can reflect modern network traffic scenarios, vast varieties of low footprint intrusions and depth structured information about the network traffic. Evaluating network intrusion detection systems research efforts, KDD98, KDDCUP99 and NSLKDD benchmark data sets were generated a decade ago. However, numerous current studies showed that for the current network threat environment, these data sets do not inclusively reflect network traffic and modern low footprint attacks.

Categories:
4762 Views

The modified CASIA dataset is created for research topics like: perceptual image hash, image tampering detection, user-device physical unclonable function and so on. 

Instructions: 

 

"training" folder: 160 images are categorized into the "training" folder, in which, "au" folder contains the original (authentic) 160 images without any modifications; "au_cp_**" folder contains those 160 images that undergoes content-preserving (cp) operations. For example, "au_cp_gamma" means those images inside are obtained by appling gamma corrections to the authentic images in the "au" folder; The "tampered" folder is the tampered version of the "au" folder correspondingly. 

"Au_ani_0001" in "au" folder: authentic image, animal category, index 0001;

"ani00008_ani00011_105" in "tampered" folder: tamper image "Au_ani_0008" by applying partial contents of image "Au_ani_0011".  Here "Au_ani_0008" and "Au_ani_0011" are all from "au" folder. 

 

"testing" folder: Another 240 images are categorized into the "testing" folder. The naming rules of the sub-folders and the images are same as the "training" folder. Inside, "tampered_with_cp" is the  D_tampered_cp as introduced in the above Abstract. "testing/tampered_with_cp/gamma" folder indicate s the authentic images are applied both tampering operation and gamma correction. 

contains

Categories:
2074 Views

Dataset contains ten days real-world DNS traffic  captured from campus network comprising of 4000 hosts in peak load hours. Dataset also contains labelled features.

Categories:
2623 Views

在此数据集中,对于不同的范围:1000、10000、100000,输入数据分为三个txt文件。

每个特定的txt文档包含500个样本数据,平均分为5类,分别表示为N = 5,12,18,23,30。

Categories:
90 Views

This study seeks to obtain data which will help to address machine learning based malware research gaps. The specific objective of this study is to build a benchmark dataset for Windows operating system API calls of various malware. This is the first study to undertake metamorphic malware to build sequential API calls. It is hoped that this research will contribute to a deeper understanding of how metamorphic malware change their behavior (i.e. API calls) by adding meaningless opcodes with their own dissembler/assembler parts.

Instructions: 

Malware Types and System Overall

In our research, we have translated the families produced by each of the software into 8 main malware families: Trojan, Backdoor, Downloader, Worms, Spyware Adware, Dropper, Virus. Table 1 shows the number of malware belonging to malware families in our data set. As you can see in the table, the number of samples of other malware families except AdWare is quite close to each other. There is such a difference because we don't find too much of malware from the adware malware family.

Categories:
6269 Views

The compressed file contains C++ source code for performance measurement

Categories:
40 Views

Dataset for an article in IEEE Transactions on Industrial Informatics

Categories:
88 Views

Pages