Machine Learning
Nowadays, with the rapid increase in the number of applications and networks, the number of cyber multi-step attacks has been increasing exponentially. Thus, the need for a reliable and acceptable Intrusion Detection System (IDS) solution is becoming urgent to protect the networks and devices. However, implementing a robust IDS needs a reliable and up-to-date dataset in order to capture the behaviors of the new types of attacks, especially multi-step attacks. In this work, a new benchmark Multi-Step Cyber-Attack Dataset (MSCAD) is introduced.
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KSU-ArSL was developed by the Center of Smart Robotics Research at King Saud University (KSU) in conjunction with the Higher Education Program for the Deaf and Hard of Hearing. The dataset consists of 80 classes (belonging to 80 signs) recorded by 40 healthy subjects using three cameras (one RGB and two Microsoft Kinect cameras). Each subject repeated each sign 5 times in five separate sessions at the same day. As a result, there are 200 video samples per class, 16000 samples in total per camera.
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We created a 2563-image custom dragon fruit image dataset, with 1248 images of raw dragon fruits and 1315 photographs of ripe dragon fruits. The images were taken with the Nikon D5200 DSLR and OnePlus 6's Sony IMX 519 16 megapixel camera. The photographs taken with the DSLR camera had a resolution of 4000 by 6000 pixels, while those taken with the OnePlus6 had a resolution of 3456 by 4608 pixels. They were photographed in natural sunlight. The average temperature during that time was 28°C (84.2°F), with partly sunny skies, 65 percent humidity, and 17 km/h wind speeds.
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Contrast-enhanced computed tomography (CE-CT) is the gold standard for diagnosing AD. However, contrast agents can cause allergic reactions or renal failure in some patients. Moreover, AD diagnosis by radiologists using non- contrast-enhanced CT (NCE-CT) images has poor sensitivity. To address this issue, a novel deep learning methos was proposed for AD detection using NCE-CT volumes. It may have great potential to reduce the misdiagnosis of AD using NCE-CT in clinical practice.
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This dataset is used for the identification of video in the internet traffic. The dataset was prepared by using Wireshark. It comprises of two types of traffic data, VPN (Virtual Private Network) or encrypted traffic data and Non-VPN or unencrypted traffic. The dataset consist of the data streams (.pcap) of 43 videos. Each video is played 50 times in both VPN and Non-VPN mode. The streams were obtained by setting-up a dummy client on a PC which plays a YouTube video and Wireshark is used to capture the internet traffic.
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These are the coefficients of the equivalent velocity increment estimator for minimum-time low-thrust transfers to the geostationary orbit.
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This dataset contains raw captured packet headers from six commercial drones.
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