Security

Supplemental material for paper "Energy Efficiency Analysis of Post-Quantum Cryptographic Algorithms."

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·       9/11 hijackers network dataset [20]: The 9/11 hijackers network incorporates 61 nodes (each node is a terrorist involved in 9/11 bombing at World Trade Centers in 2011). Dataset was prepared based on some news report, and ties range from ‘at school with’ to ‘on the same plane’. The Data consists of a mode matrix with 19*19 terrorist by terrorist having trusted prior contacts with 1 mode matrix of 61 edges of other involved associates.

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The S3 dataset contains the behaviour (sensors, statistics of applications, and voice) of 21 volunteers interacting with their smartphones for more than 60 days. The type of users is diverse, males and females in the age range from 18 until 70 have been considered in the dataset generation. The wide range of age is a key aspect, due to the impact of age in terms of smartphone usage. To generate the dataset the volunteers installed a prototype of the smartphone application in on their Android mobile phones.

 

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The Development of an Internet of Things (IoT) Network Traffic Dataset with Simulated Attack Data.

Abstract— This research focuses on the requirements for and the creation of an intrusion detection system (IDS) dataset for an Internet of Things (IoT) network domain.

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Disclaimer 
DARPA is releasing these files in the public domain to stimulate further research. Their release implies no obligation or desire to support additional work in this space. The data is released as-is. DARPA makes no warranties as to the correctness, accuracy, or usefulness of the released data. In fact, since the data was produced by research prototypes, it is practically guaranteed to be imperfect.
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The Internet of Things (IoT) is reshaping our connected world, due to the prevalence of lightweight devices connected to the Internet and their communication technologies. Therefore, research towards intrusion detection in the IoT domain has a lot of significance. Network intrusion datasets are fundamental for this research, as many attack detection strategies have to be trained and evaluated using these datasets.

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This dataset is a supplementary material for paper "A Comprehensive and Reproducible Comparison of Cryptographic Primitives Execution on Android Devices"  with the measurements collected from 17 mobile devices and the code for reproducibility.

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Dataset with diverse type of attacks in Programmable Logic Controllers:

1- Denial of Service 

  • Flooding
  • Amplification/Volumetric

2- Man in the Middle

 

The full documentation of the dataset is available at: https://arxiv.org/abs/2103.09380 

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Datasets as described in the research paper "Intrusion Detection using Network Traffic Profiling and Machine Learning for IoT Applications".There are two main dataset provided here, firstly is the data relating to the initial training of the machine learning module for both normal and malicious traffic, these are in binary visulisation format, compresed into the document traffic-dataset.zip.

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Smart speakers and voice-based virtual assistants are core components for the success of the IoT paradigm. Unfortunately, they are vulnerable to various privacy threats exploiting machine learning to analyze the generated encrypted traffic. To cope with that, deep adversarial learning approaches can be used to build black-box countermeasures altering the network traffic (e.g., via packet padding) and its statistical information.

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