network security

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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275 Views

5G Network slicing is one of the key enabling technologies that offer dedicated logical resources to different applications on the same physical network. However, a Denial-of-Service (DoS) or Distributed Denial-of-Service (DDoS) attack can severely damage the performance and functionality of network slices. Furthermore, recent DoS/DDoS attack detection techniques are based on the available data sets which are collected from simulated 5G networks rather than from 5G network slices.

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1897 Views

This dataset consists of “.csv” files of 4 different routing attacks (Blackhole Attack, Flooding Attack, DODAG Version Number Attack, and Decreased Rank Attack) targeting the RPL protocol, and these files are taken from Cooja (Contiki network simulator). It allows researchers to develop IDS for RPL-based IoT networks using Artificial Intelligence and Machine Learning methods without simulating attacks. Simulating these attacks by mimicking real-world attack scenarios is essential to developing and testing protection mechanisms against such attacks.

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2564 Views

Datsets and scripts are for derivation of a lightweight AIoT malware detection model.

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224 Views

The dataset has been developed in Smart Connected Vehicles Innovation Centre (SCVIC) of the University of Ottawa in Kanata North Technology Park.

In order to define a benchmark for Machine Learning (ML)-based Advanced Persistent Threat (APT) detection in the network traffic, we create a dataset named SCVIC-APT-2021, that can realistically represent the contemporary network architecture and APT characteristics.  Please cite the following original article where this work was initially presented:

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2549 Views

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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5383 Views

As an alternative to classical cryptography, Physical Layer Security (PhySec) provides primitives to achieve fundamental security goals like confidentiality, authentication or key derivation. Through its origins in the field of information theory, these primitives are rigorously analysed and their information theoretic security is proven. Nevertheless, the practical realizations of the different approaches do take certain assumptions about the physical world as granted.

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1277 Views

Normal
0

false
false
false

EN-US
X-NONE
AR-SA

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732 Views

Dataset contains ten days real-world DNS traffic  captured from campus network comprising of 4000 hosts in peak load hours. Dataset also contains labelled features.

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4879 Views