Security
Device identification using network traffic analysis is being researched for IoT and non-IoT devices against cyber-attacks. The idea is to define a device specific unique fingerprint by analyzing the solely inter-arrival time (IAT) of packets as feature to identify a device. Deep learning is used on IAT signature for device fingerprinting of 58 non-IoT devices. We observed maximum recall and accuracy of 97.9% and 97.7% to identify device. A comparitive research GTID found using defined IAT signature that models of device identification are better than device type identification.
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We have included the code of protocol verification for ProVerif (in .pv format) and Scyther (in .spdl format) in the “Supplementary Materials” (Supplementary-Materials.pdf) of the revised manuscript. Essentially, the .pdf file of the “Supplementary Materials” includes: A. The source code for the protocol verifier Scyther (in .spdl format); and B. The source code for the protocol verifier ProVerif (in .pv format).
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This dataset was collected from an industrial control system running the Modbus protocol. It is used to train a deep adversarial learning model. This model is used to generate fuzzing data in the same format as the real one. The data is a sequence of hexadecimal numbers. The followed generated data is produced by the already trained model.
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This enclosed file comprises two parts: one is the social network simulation data set generated based on R-MART and power law distribution model; the other is the sourse code of alogorithm 1 to 6 for PBCN.
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330 Views
Spectroreflectometric techniques for dactiloscopy and optical nail diagnostics are proposed and aproved on "DATACOLOR 3890"-based measuring system.
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252 Views
These datasets were generated in a computer network environment where eXfiltration Advanced Persistent Threats were launched against a number of high-value targets.
It is the alert log of the Security Onion SIEM which aggregates alerts from network and host-based intrusion detection systems that are securing the network environment.
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469 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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Efficient intrusion detection and analysis of the security landscape in big data environments present challenge for today's users. Intrusion behavior can be described by provenance graphs that record the dependency relationships between intrusion processes and the infected files. Existing intrusion detection methods typically analyze and identify the anomaly either in a single provenance path or the whole provenance graph, neither of which can achieve the benefit on both detection accuracy and detection time.
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177 Views
Mp3 is a very popular audio format and hence it can be a good host for carrying hidden messages. Therefore, different steganography methods have been proposed for mp3 hosts. UnderMp3Cover is one of such algorithms and has some important advantage over other comparable methods. First, the popular steganography method mp3stego, works directly on non-compressed samples. Therefore, using covers that have been compressed before could lead to serious degradation of its security. UnderMp3Cover does not have this important limitation.
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The raw EEG signals are collected from seven adult participants (a~g, 4 males and 3 females, their ages range from 21 to 45, the average age is 24.71 and the average deviation is 6.49). None of them has a case history of brain injury or brain disease. the "EMOTIV EPOC+"EEG head-worn device is employed, which has a total of 14 channels, namely: AF3, AF4, F3, F4, F7, F8, FC5, FC6, T7, T8, P7, P8, O1 and O2. The sampling frequency is 128Hz and the signals can generate 128 sample points per second per channel.
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253 Views