Security

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.

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This is a communication dataset for the simulation of WSNs in TinyOS.

There are two groups of files. They are used separately for the simulation of topology and noise in the communication of WSNs. They work for the platform TinyOS.

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This sheet contains the answers from our european Cyber Security MSc Education Survey. The data shows which knowledge units various educations in Europe cover and to which extend. We drew conclusions in the paper "Are We Preparing Students to Build Security In? A Survey of European Cybersecurity in Higher Education Programs". The present dataset is newer and therefore extends the one we based our paper on.

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Dataset used in the article "An Ensemble Method for Keystroke Dynamics Authentication in Free-Text Using Word Boundaries". For each user and free-text sample of the companion dataset LSIA, contains a CSV file with the list of words in the sample that survived the filters described in the article, together with the CSV files with training instances for each word. The source data comes from a dataset used in previous studies by the authors. The language of the free-text samples is Spanish.

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66% of Prestashop websites are at high risk from cyber criminals.

Common Hacks in Prestashop

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The Dada dataset is associated with the paper “Debiasing Android Malware Datasets: How can I trust your results if your dataset is biased?”. The goal of this dataset is to provide a new updated dataset of goodware/malware applications that can be used by other researchers for performing experiments, for example, detection or classification algorithms. The dataset contains the applications hashes and some characteristics.

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This dataset contains multimodal sensor data collected from side-channels while printing several types of objects on an Ultimaker 3 3D printer. Our related research paper titled "Sabotage Attack Detection for Additive Manufacturing Systems" can be found here: https://doi.org/10.1109/ACCESS.2020.2971947. In our work, we demonstrate that this sensor data can be used with machine learning algorithms to detect sabotage attacks on the 3D printer.

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The DroneDetect dataset consists of 7 different models of popular Unmanned Aerial Systems (UAS) including the new DJI Mavic 2 Air S, DJI Mavic Pro, DJI Mavic Pro 2, DJI Inspire 2, DJI Mavic Mini, DJI Phantom 4 and the Parrot Disco. Recordings were collected using a Nuand BladeRF SDR and using open source software GNURadio. There are 4 subsets of data included in this dataset, the UAS signals in the presence of Bluetooth interference, in the presence of Wi-Fi signals, in the presence of both and with no interference.

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12133 Views

 

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Dataset used in the article "The Reverse Problem of Keystroke Dynamics: Guessing Typed Text with Keystroke Timings". CSV files with dataset results summaries, the evaluated sentences, detailed results, and scores. Results data contains training and evaluation ARFF files for each user, containing features of synthetic and legitimate samples as described in the article. The source data comes from three free text keystroke dynamics datasets used in previous studies, by the authors (LSIA) and two other unrelated groups (KM, and PROSODY, subdivided in GAY, GUN, and REVIEW).

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