Study of long short-term memory in flow-based network intrusion detection system

Nicholas, Lee; Ooi, Shih Yin; Pang, Ying Han; Hwang, Seong Oun; Tan, Syh-Yuan

doi:10.3233/jifs-169836

Cited by 22 publications

(11 citation statements)

References 26 publications

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“…Although sequence size is not mentioned, most of the hyperparameter values that were used are described in detail. Nicholas et al, in [19], also evaluated the performance of an LSTM model in the flow-based data of CIDDS-001 and compared the obtained results with other traditional classifiers.…”

Section: Related Workmentioning

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: Related Workmentioning

confidence: 99%

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

Oliveira¹,

Praça²,

Maia³

et al. 2021

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Although sequence size is not mentioned, most of the hyperparameter values that were used are described in detail. Nicholas et al in [18] also evaluated the performance of an LSTM model in the flow-based data of CIDDS-001 and compared the obtained results with other traditional classifiers.…”

Section: Related Workmentioning

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%.

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“…10) CIDDS-001 Dataset: In [507], the effectiveness of LSTM for flow-based network IDS is studied. LSTM models of different combination of hyperparameters are tested using a flow-based network traffic dataset and its performance is evaluated.…”

Section: A Deep Learning In Intrusion Detectionmentioning

confidence: 99%

A Comprehensive Tutorial and Survey of Applications of Deep Learning for Cyber Security

Ravi¹,

Alazab²,

Sriram³

et al. 2020

Preprint

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Study of long short-term memory in flow-based network intrusion detection system

Cited by 22 publications

References 26 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

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

A Comprehensive Tutorial and Survey of Applications of Deep Learning for Cyber Security

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