Security
This data set corresponds to Table II:UNIFORMITY OF TC-PUF DESIGN of manuscript titled "A Lightweight and Secure Physical Unclonable Function Design on FPGA". The provided data is for FPGA board No. 1 to 15. Board no. 1 to 14 represent uniformity of 40X40 TC-PUF response implemented on Artix-7 FPGA, and board no. 15 represent uniformity of 20X40 TC-PUF response implemented on Zynq Z-7010 FPGA. It is observed that nearly all the TC-PUF implemented on individual FPGAs have a slight bias towards ‘0’.
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Smart homes contain programmable electronic devices (mostly IoT) that enable home automation. People who live in smart homes benefit from interconnected devices by controlling them either remotely or manually/autonomously. However, high interconnectivity comes with an increased attack surface, making the smart home an attractive target for adversaries. NCC Group and the Global Cyber Alliance recorded over 12,000 attacks to log into smart home devices maliciously. Recent statistics show that over 200 million smart homes can be subjected to these attacks.
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3458 Views
The deployment of unmanned aerial vehicles (UAV) for logistics and other civil purposes is consistently disrupting airspace security. Consequently, there is a scarcity of robust datasets for the development of real-time systems that can checkmate the incessant deployment of UAVs in carrying out criminal or terrorist activities. VisioDECT is a robust vision-based drone dataset for classifying, detecting, and countering unauthorized drone deployment using visual and electro-optical infra-red detection technologies.
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The time-to-market pressure and the continuous growing complexity of hardware designs have promoted the globalization of the Integrated Circuit (IC) supply chain. However, such globalization also poses various security threats in each phase of the IC supply chain. Although the advancements of Machine Learning (ML) have pushed the frontier of hardware security, most conventional ML-based methods can only achieve the desired performance by manually finding a robust feature representation for circuits that are non-Euclidean data. As a result, modeling these circuits using graph learning to imp
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2806 Views
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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1819 Views
Producing secure software is challenging. The poor usability
of security Application Programming Interfaces (APIs) makes this even
harder. Many recommendations have been proposed to support developers
by improving the usability of cryptography libraries and APIs; rooted in
wider best practice guidance in software engineering and API design. In
this SLR, we systematize knowledge regarding these recommendations.
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291 Views
BS-HMS-Dataset is a dataset of the users' brainwave signals and the corresponding hand movement signals from a large number of volunteer participants. The dataset has two parts; (1) Neurosky based Dataset (collected over several months in 2016 from 32 volunteer participants), and (2) Emotiv based Dataset (collected from 27 volunteer participants over several months in 2019).
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This dataset is benchmark dataset we use in our research for Intrusion Detection System.
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4738 Views
With the popularity of smartphones and widespread use of high-speed Internet, social media has become a vital part of people’s daily life. Currently, text messages are used in many applications, such as mobile chatting, mobile banking, and mobile commerce. However, when we send a text message via short message service (SMS) or social media, the information contained in the text message transmits as a plain text, which exposes it to attacks.
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788 Views
This dataset is a result of my research production into machine learning in android security. The data was obtained by a process that consisted to map a binary vector of permissions used for each application analyzed {1=used, 0=no used}. Moreover, the samples of malware/benign were devided by "Type"; 1 malware and 0 non-malware.
When I did my research, the datasets of malware and benign Android applications were not available, then I give to the community a part of my research results for the future works.
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6876 Views