Security

Image representation of Malware-benign dataset. The Dataset were compiled from various sources malware repositories:  The Malware-Repo, TheZoo,Malware Bazar, Malware Database, TekDefense. Meanwhile benign samples were sourced from system application of Microsoft 10 and 11, as well as open source software repository such as Sourceforge, PortableFreeware, CNET, FileForum. The samples were validated by scanning them using Virustotal Malware scanning services. The Samples were pre-processed by transforming the malware binary into grayscale images following rules from Nataraj (2011).

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In today's world of online communication and digital media, hate speech has become an alarming problem worldwide. With the advancement of the internet, while people enjoy numerous benefits, there's also a dark side where individuals are subjected to horrendous bullying through hate speech. Tragically, some instances even lead to extreme actions like suicide or self-destructive behavior.

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The results are based on the measurements conducted on small drones and a bionic bird using a 60 GHz millimeter wave radar, analyzing their micro-Doppler characteristics in both time and frequency domain. The results are presented in .pkl format. The more detailed description of the data and how the authors processed it will be updated soon.

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This dataset presents real-world IoT device traffic captured under a scenario termed "Active," reflecting typical usage patterns encountered by everyday users. Our methodology emphasizes the collection of authentic data, employing rigorous testing and system evaluations to ensure fidelity to real-world conditions while minimizing noise and irrelevant capture.

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GNSS recordings on four distinct scenarios to evaluate the Galileo Open Service Navigation Message Authentication (OSNMA) protocol with different receiver optimizations. However, the recordings are generic and can be used for any other GNSS purpose as they contain PVT measurements and data for GPS, Galileo, Glonass, and BeiDou.

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The increasing integration of cyber-physical systems in industrial environments has under scored the critical need for robust security measures to counteract evolving cyber threats. In response to this need, this work introduces an open-source dataset designed to enhance the development and evaluation of cybersecurity solutions for smart industries. The dataset comprises a traffic capture of an industrial control system (ICS) subjected to a variety of simulated cyber-attacks, including but not limited to denial of service (DoS), man-in-the-middle (MITM), and malware infiltration.

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This research utilizes real-world malware samples that are reinforced with the latest VM-based packers and digitally signed to ensure runtime execution. For academic research purposes only, these packed malware samples are provided as running instances to facilitate behavioral, forensic and detection analysis. Users are forewarned on the potential risks of executing unknown malicious programs, and should refrain from installing or propagating these files outside of a controlled experimental environment.

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CTCS-2 level train control engineering data is primarily categorized into trackside infrastructure data and line parameters, including line velocity table, line gradient table, line broken chain detail table, balise position table, main line signal data table and so on. The dataset image above is an example of a balise position table.

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In our Systematic Literature Review (SLR), we systematically conducted searches in various repositories, utilizing a search string, based on titles and abstracts of publications. Following the retrieval of results, we meticulously eliminated duplicate publications, ensuring that only one instance with identical title, abstract, author names, and year of publication was retained. Subsequently, we excluded books and thesis reports from our dataset. A comprehensive manual review of the titles and abstracts of the remaining articles was then undertaken to further refine our dataset.

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Our DeepCoAST dataset specifically explores the vulnerabilities of various traffic-splitting Website Fingerprinting (WF) Defenses, such as TrafficSliver, HyWF, and CoMPS. Our dataset comprises defended traces generated from the BigEnough dataset, which includes Tor cell trace instances of 95 websites, each represented by 200 instances collected under the standard browser security level. We simulated the traffic-splitting defenses assuming there are two split traces from the vanilla trace.

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