Modeling Network Intrusion Detection System Using Feed-Forward Neural Network Using UNSW-NB15 Dataset

Liu, Zhiqiang; Mohi-Ud-Din, Ghulam; Li, Bing; Luo, Jian; Zhu, Yanchao; Zhijun, Lin

doi:10.1109/sege.2019.8859773

Cited by 33 publications

(14 citation statements)

References 11 publications

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“…One machine learning model, Random Forest [20], and two deep learning models, Multi-Layer Perceptron [25] and Long-Short Term Memory [14,15] were employed. These state-of-the-art techniques for intrusion detection systems presented very promising results in previous research for multiple state-of-the-art datasets, namely for the CICIDS2017 [10] and the UNSW-NB15 [11].…”

Section: Modelsmentioning

confidence: 99%

Intelligent Cyber Attack Detection and Classification for Network-Based Intrusion Detection Systems

Oliveira¹,

Praça²,

Maia³

et al. 2021

Applied Sciences

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With the latest advances in information and communication technologies, greater amounts of sensitive user and corporate information are shared continuously across the network, making it susceptible to an attack that can compromise data confidentiality, integrity, and availability. Intrusion Detection Systems (IDS) are important security mechanisms that can perform the timely detection of malicious events through the inspection of network traffic or host-based logs. Many machine learning techniques have proven to be successful at conducting anomaly detection throughout the years, but only a few considered the sequential nature of data. This work proposes a sequential approach and evaluates the performance of a Random Forest (RF), a Multi-Layer Perceptron (MLP), and a Long-Short Term Memory (LSTM) on the CIDDS-001 dataset. The resulting performance measures of this particular approach are compared with the ones obtained from a more traditional one, which only considers individual flow information, in order to determine which methodology best suits the concerned scenario. The experimental outcomes suggest that anomaly detection can be better addressed from a sequential perspective. The LSTM is a highly reliable model for acquiring sequential patterns in network traffic data, achieving an accuracy of 99.94% and an f1-score of 91.66%.

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Section: Modelsmentioning

confidence: 99%

Intelligent Cyber Attack Detection and Classification for Network-Based Intrusion Detection Systems

Oliveira¹,

Praça²,

Maia³

et al. 2021

Applied Sciences

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“…While many more complex or elaborate techniques are used, MLPs remain relevant and are used in NIDSs. Liu Zhiqiang et al [82] use a 10-layer MLP to binarily classify the traffic of UNSW-NB15. With an accuracy of 99.5% and FPR of 0.47%, they obtain very high results.…”

Section: ) Mlpmentioning

confidence: 99%

Machine Learning for Misuse-Based Network Intrusion Detection: Overview, Unified Evaluation and Feature Choice Comparison Framework

2021

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“…One machine learning model, Random Forest [19], and two deep learning models, Multi-Layer Perceptron [22] and Long-Short Term Memory [12,17] were employed. These state of the art techniques for intrusion detection systems presented very promising results in previous researches for multiple state of the art datasets, namely for the CICIDS2017 [9] and the UNSW-NB15 [10].…”

Section: Modelsmentioning

confidence: 99%

Intelligent Cyber-Attack Detection and Classification for Network-based Intrusion Detection Systems

Oliveira¹,

Praça²,

Maia³

et al. 2020

Preprint

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With the latest advances in information and communication technologies, greater amounts of sensitive user and corporate information are constantly shared across the network making it susceptible to an attack that can compromise data confidentiality, integrity and availability. Intrusion Detection Systems (IDS) are important security mechanisms that can perform a timely detection of malicious events through the inspection of network traffic or host-based logs. Throughout the years, many machine learning techniques have proven to be successful at conducting anomaly detection but only a few considered the sequential nature of data. This work proposes a sequential approach and evaluates the performance of a Random Forest (RF), a Multi-Layer Perceptron (MLP) and a Long-Short Term Memory (LSTM) on the CIDDS-001 dataset. The resulting performance measures of this particular approach are compared with the ones obtained from a more traditional one, that only considers individual flow information, in order to determine which methodology best suits the concerned scenario. The experimental outcomes lead to believe that anomaly detection can be better addressed from a sequential perspective and that the LSTM is a very reliable model for acquiring sequential patterns in network traffic data, achieving an accuracy of 99.94% and a f1-score of 91.66%.

show abstract

Modeling Network Intrusion Detection System Using Feed-Forward Neural Network Using UNSW-NB15 Dataset

Cited by 33 publications

References 11 publications

Intelligent Cyber Attack Detection and Classification for Network-Based Intrusion Detection Systems

Intelligent Cyber Attack Detection and Classification for Network-Based Intrusion Detection Systems

Machine Learning for Misuse-Based Network Intrusion Detection: Overview, Unified Evaluation and Feature Choice Comparison Framework

Intelligent Cyber-Attack Detection and Classification for Network-based Intrusion Detection Systems

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