Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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DoS
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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Computer networks face vulnerability to numerous attacks, which pose significant threats to our data security and the freedom of communication. This paper introduces a novel intrusion detection technique that diverges from traditional methods by leveraging Recurrent Neural Networks (RNNs) for both data preprocessing and feature extraction. The proposed process is based on the following steps: (1) training the data using RNNs, (2) extracting features from their hidden layers, and (3) applying various classification algorithms. This methodology offers significant advantages and greatly differs from existing intrusion detection practices. The effectiveness of our method is demonstrated through trials on the Network Security Laboratory (NSL) and Canadian Institute for Cybersecurity (CIC) 2017 datasets, where the application of RNNs for intrusion detection shows substantial practical implications. Specifically, we achieved accuracy scores of 99.6% with Decision Tree, Random Forest, and CatBoost classifiers on the NSL dataset, and 99.8% and 99.9%, respectively, on the CIC 2017 dataset. By reversing the conventional sequence of training data with RNNs and then extracting features before applying classification algorithms, our approach provides a major shift in intrusion detection methodologies. This modification in the pipeline underscores the benefits of utilizing RNNs for feature extraction and data preprocessing, meeting the critical need to safeguard data security and communication freedom against ever-evolving network threats.
Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
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makekali/CIC-IDS-2017 dataset hosted on Hugging Face and contributed by the HF Datasets community
Packet Capture (PCAP) files of UNSW-NB15 and CIC-IDS2017 dataset are processed and labelled utilizing the CSV files. Each packet is labelled by comparing the eight distinct features: Source IP, Destination IP, Source Port, Destination Port, Starting time, Ending time, Protocol and Time to live. The dimensions for the dataset is Nx1504. All column of the dataset are integers, therefore you can directly utilize this dataset in you machine learning models. Moreover, details of the whole processing and transformation is provided in the following GitHub Repo: https://github.com/Yasir-ali-farrukh/Payload-Byte You can utilize the tool available at the above mentioned GitHub repo to generate labelled dataset from scratch. All of the detail of processing and transformation is provided in the following paper: ```yaml @article{Payload, author = "Yasir Ali Farrukh and Irfan Khan and Syed Wali and David Bierbrauer and Nathaniel Bastian", title = "{Payload-Byte: A Tool for Extracting and Labeling Packet Capture Files of Modern Network Intrusion Detection Datasets}", year = "2022", month = "9", url = "https://www.techrxiv.org/articles/preprint/Payload-Byte_A_Tool_for_Extracting_and_Labeling_Packet_Capture_Files_of_Modern_Network_Intrusion_Detection_Datasets/20714221", doi = "10.36227/techrxiv.20714221.v1" }
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Cleaned CICIDS2017 Dataset
This dataset is a cleaned and preprocessed version of the CICIDS2017 dataset created by the Canadian Institute for Cybersecurity, University of New Brunswick.
Modifications
Removed duplicate records Normalized feature names Filtered specific attack types Piviot the different attack data into single dataset
Source
Original dataset: CICIDS2017
License & Citation
This dataset is provided for research purposes. Please referโฆ See the full description on the dataset page: https://huggingface.co/datasets/agrawalchaitany/cyberbert_dataset.
Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
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The CIC-IDS-V2 is an extended version of the original CIC-IDS 2017 dataset. The dataset is normalised and 1 new class called "Comb" is added which is a combination of synthesised data of multiple non-benign classes. To cite the dataset, please reference the original paper with DOI: 10.1109/SmartNets61466.2024.10577645. The paper is published in IEEE SmartNets and can be accessed here:โฆ See the full description on the dataset page: https://huggingface.co/datasets/abluva/CIC-IDS-2017-V2.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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Distributed Denial of Service (DDoS) attack is a menace to network security that aims at exhausting the target networks with malicious traffic. Although many statistical methods have been designed for DDoS attack detection, designing a real-time detector with low computational overhead is still one of the main concerns. On the other hand, the evaluation of new detection algorithms and techniques heavily relies on the existence of well-designed datasets. In this paper, first, we review the existing datasets comprehensively and propose a new taxonomy for DDoS attacks. Secondly, we generate a new dataset, namely CICDDoS2019, which remedies all current shortcomings. Thirdly, using the generated dataset, we propose a new detection and family classification approach based on a set of network flow features. Finally, we provide the most important feature sets to detect different types of DDoS attacks with their corresponding weights.
The dataset offers an extended set of Distributed Denial of Service attacks, most of which employ some form of amplification through reflection. The dataset shares its feature set with the other CIC NIDS datasets, IDS2017, IDS2018 and DoS2017
original paper link: https://ieeexplore.ieee.org/abstract/document/8888419 kaggle dataset link: https://www.kaggle.com/datasets/dhoogla/cicddos2019
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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With the continuous expansion of data exchange, the threat of cybercrime and network invasions is also on the rise. This project aims to address these concerns by investigating an innovative approach: an Attentive Transformer Deep Learning Algorithm for Intrusion Detection of IoT Systems using Automatic Xplainable Feature Selection. The primary focus of this project is to develop an effective Intrusion Detection System (IDS) using the aforementioned algorithm. To accomplish this, carefully curated datasets have been utilized, which have been created through a meticulous process involving data extraction from the University of New Brunswick repository. This repository houses the datasets used in this research and can be accessed publically in order to replicate the findings of this research.
This dataset was created by Zufar Asyraf
CICIDS2018 includes seven different attack scenarios: Brute-force, Heartbleed, Botnet, DoS, DDoS, Web attacks, and infiltration of the network from inside. The attacking infrastructure includes 50 machines and the victim organization has 5 departments and includes 420 machines and 30 servers. The dataset includes the captures network traffic and system logs of each machine, along with 80 features extracted from the captured traffic using CICFlowMeter-V3.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Computer networks face vulnerability to numerous attacks, which pose significant threats to our data security and the freedom of communication. This paper introduces a novel intrusion detection technique that diverges from traditional methods by leveraging Recurrent Neural Networks (RNNs) for both data preprocessing and feature extraction. The proposed process is based on the following steps: (1) training the data using RNNs, (2) extracting features from their hidden layers, and (3) applying various classification algorithms. This methodology offers significant advantages and greatly differs from existing intrusion detection practices. The effectiveness of our method is demonstrated through trials on the Network Security Laboratory (NSL) and Canadian Institute for Cybersecurity (CIC) 2017 datasets, where the application of RNNs for intrusion detection shows substantial practical implications. Specifically, we achieved accuracy scores of 99.6% with Decision Tree, Random Forest, and CatBoost classifiers on the NSL dataset, and 99.8% and 99.9%, respectively, on the CIC 2017 dataset. By reversing the conventional sequence of training data with RNNs and then extracting features before applying classification algorithms, our approach provides a major shift in intrusion detection methodologies. This modification in the pipeline underscores the benefits of utilizing RNNs for feature extraction and data preprocessing, meeting the critical need to safeguard data security and communication freedom against ever-evolving network threats.
MIT Licensehttps://opensource.org/licenses/MIT
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fikrimulyana/CIC-IDS-2017 dataset hosted on Hugging Face and contributed by the HF Datasets community
This dataset was created by Mohaned Mohammed Naji
his is an academic intrusion detection dataset. All the credit goes to the original authors: Dr. Iman Sharafaldin, Dr. Saqib Hakak, Dr. Arash Habibi Lashkari Dr. Ali Ghorbani. Please cite their original paper.
The dataset offers an extended set of Distributed Denial of Service attacks, most of which employ some form of amplification through reflection. The dataset shares its feature set with the other CIC NIDS datasets, IDS2017, IDS2018 and DoS2017
Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
License information was derived automatically
sonnh-tech1/cic-ids-2017 dataset hosted on Hugging Face and contributed by the HF Datasets community
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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and contextual features
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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Features of the CIC-IDS 2017 network intrusion dataset.
gyawalishiva/cic-ids-2017-textual dataset hosted on Hugging Face and contributed by the HF Datasets community
This dataset was created by Sweety
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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The NSL-KDD and CICIDS2017 datasets used on the research.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
DoS