CICIDS2017 dataset: performance improvements and validation as a robust intrusion detection system testbed

Boukhamla, Akram Zine Eddine; Gaviro, Javier Coronel

doi:10.1504/ijics.2021.117392

Cited by 13 publications

(7 citation statements)

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“…This paper proposed a novel attack detection system for IoT, denoted as cVAE-DML. And two public datasets were applied, including CIC-IDS 2017 [35] and CSE-CIC-IDS 2018 [36] , to verify the efficiency of the cVAE-DML. The experiment results showed that the efficiency of the cVAE-DML is better than traditional attack detection.…”

Section: Methodsmentioning

confidence: 99%

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Attack Detection Model for BCoT based on Contrastive Variational Autoencoder and Metric Learning

Wu,

Liu,

Ding

et al. 2024

Preprint

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With development of blockchain technology, clouding computing and Internet of Things (IoT), blockchain and cloud of things (BCoT) has become development tendency. But the security has become the most development hinder of BCoT. Attack detection model is a crucial part of attack revelation mechanism for BCoT. As a consequence, attack detection model has received more concerned. Due to the great diversity and variation of network attacks aiming to BCoT, tradition attack detection models are not suitable for BCoT. In this paper, we propose a novel attack detection model for BCoT, denoted as cVAE-DML. The novel model is based on contrastive variational autoencoder (cVAE) and deep metric learning (DML). By training the cVAE, the proposed model generates private features for attack visiting information as well as shared features between attack visiting information and normal visiting information. Based on those generated features, the proposed model can generate representative new samples to balance the training dataset. At last, the decoder of cVAE is connected to the deep metric learning network to detect attack aiming to BCoT. The efficiency of cVAE-DML is verified using the CIC-IDS 2017 dataset and CSE-CIC-IDS 2018 dataset. The results show that cVAE-DML can improve attack detection efficiency even under the condition of unbalanced samples.

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Section: Methodsmentioning

confidence: 99%

“…In this paper, five algorithms were used, that is, THEODORA [28] , AIDA [29] , MINDFUL [30] and, DNN-3 [35] and DNN4Layers [36] , as the comparative algorithms to verify the efficiency of the cVAE-DML. The details of those comparative algorithms are shown as follows.…”

Section: Comparative Experimentsmentioning

confidence: 99%

Attack Detection Model for BCoT based on Contrastive Variational Autoencoder and Metric Learning

Wu,

Liu,

Ding

et al. 2024

Preprint

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“…Furthermore, in 13 , MoEV has been validated and used to detect network attacks. To validate the tool, the researchers replicated the work presented in 45 , obtaining similar results.…”

Section: Methodsmentioning

confidence: 99%

Malicious traffic detection on sampled network flow data with novelty-detection-based models

Campazas-Vega,

Crespo-Martínez,

Guerrero-Higueras

et al. 2023

Sci Rep

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Cyber-attacks are a major problem for users, businesses, and institutions. Classical anomaly detection techniques can detect malicious traffic generated in a cyber-attack by analyzing individual network packets. However, routers that manage large traffic loads can only examine some packets. These devices often use lightweight flow-based protocols to collect network statistics. Analyzing flow data also allows for detecting malicious network traffic. But even gathering flow data has a high computational cost, so routers usually apply a sampling rate to generate flows. This sampling reduces the computational load on routers, but much information is lost. This work aims to demonstrate that malicious traffic can be detected even on flow data collected with a sampling rate of 1 out of 1,000 packets. To do so, we evaluate anomaly-detection-based models using synthetic sampled flow data and actual sampled flow data from RedCAYLE, the Castilla y León regional subnet of the Spanish academic and research network. The results presented show that detection of malicious traffic on sampled flow data is possible using novelty-detection-based models with a high accuracy score and a low false alarm rate.

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“…The CICIDS2017 dataset included eight varied files containing the traffic data for attacks and five normal days of the Canadian Institute of Cybersecurity. Table 1 provides short description of dataset files for the CICIDS2017 dataset [19], [20].…”

Section: Datasetmentioning

confidence: 99%

Network intrusion detection system: machine learning approach

Jaradat

Barhoush²,

Easa³

2022

IJEECS

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The main goal of intrusion detection system (IDS) is to monitor the network performance and to investigate any signs of any abnormalities over the network. Recently, intrusion detection systems employ machine learning techniques, due to the fact that machine learning techniques proved to have the ability of learning and adapting in addition to allowing a prompt response. This work proposes a model for intrusion detection and classification using machine learning techniques. The model first acquires the data set and transforms it in the proper format, then performs feature selection to pick out a subset of attributes that worth being considered. After that, the refined data set was processed by the Konstanz information miner (KNIME). To gain better performance and a decent comparative analysis, three different classifiers were applied. The anticipated classifiers have been executed and assessed utilizing the KNIME analytics platform using (CICIDS2017) datasets. The experimental results showed an accuracy rate ranging between (98.6) as the highest obtained while the average was (90.59%), which was satisfying compared to other approaches. The gained statistics of this research inspires the researchers of this field to use machine learning in cyber security and data analysis and build intrusion detection systems with higher accuracy.

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CICIDS2017 dataset: performance improvements and validation as a robust intrusion detection system testbed

Cited by 13 publications

References 0 publications

Attack Detection Model for BCoT based on Contrastive Variational Autoencoder and Metric Learning

Attack Detection Model for BCoT based on Contrastive Variational Autoencoder and Metric Learning

Malicious traffic detection on sampled network flow data with novelty-detection-based models

Network intrusion detection system: machine learning approach

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