Deep Learning-Based Intrusion Detection Systems: A Systematic Review

Lánský, Jan; Ali, Saqib; Mohammadi, Mokhtar; Majeed, Mohammed Kamal; Karim, Sarkhel H. Taher; Rashidi, Shima; Hosseinzadeh, Mehdi; Rahmani, Amir Masoud

doi:10.1109/access.2021.3097247

Cited by 135 publications

(57 citation statements)

References 202 publications

(116 reference statements)

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“…In particular, this work will only consider evasion attacks against Deep Neural Networkbased IDS (DNN-IDS). Although DNNs seem to be the most promising research area in terms of IDS performance [8], these models also seem to be the most vulnerable to adversarial attacks [6].…”

Section: Adversarial Learningmentioning

confidence: 99%

TAD: Transfer learning-based multi-adversarial detection of evasion attacks against network intrusion detection systems

Debicha

Bauwens²,

Debatty³

et al. 2023

Future Generation Computer Systems

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Section: Adversarial Learningmentioning

confidence: 99%

TAD: Transfer learning-based multi-adversarial detection of evasion attacks against network intrusion detection systems

Debicha

Bauwens²,

Debatty³

et al. 2023

Future Generation Computer Systems

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“…Compared with the machine learning network, the DL network optimizes the hierarchical data of the network structure; therefore, making the overall structure more complex. Furthermore, the internal operation algorithm has also undergone greater progress [11]. e most common algorithms are classified according to common machine learning algorithms and DL algorithms.…”

Section: E DL Neural Network Analysis Geoffrey Hinton Firstmentioning

confidence: 99%

Educational Evaluation of Piano Performance by the Deep Learning Neural Network Model

Liao

2022

Mobile Information Systems

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In recent years, the piano education industry has occupied a huge market. However, the automatic evaluation function of piano performance has shortcomings in existing piano education products. Deep learning (DL) algorithm and the recurrent neural network (RNN) structure can help in automatics evaluation function of the piano performance. This paper proposes a Musical Instrument Digital Interface (MIDI) piano evaluation scheme based on the RNN structure and the Spark computing engine using the Deeplearning4J DL framework. The Deeplearning4J framework can run on the Java Virtual Machine; therefore, the entire system does not require cross-platform development. The Spark distributed computing engine realizes parallelization in music data preprocessing, feature extraction, and model training. Combined with the training user interface (UI) provided by the Deeplearning4J, it can improve developmental efficiency. Additionally, the RNN parameters are analyzed. The results demonstrate that the error value of the three-layer RNN structure is smaller than other closest rivals’ techniques. In particular, few piano training institutions and MIDI website datasets are used as the basis, and the experimental samples are collected. The neural network is trained, and the performance of the evaluation model is tested. The results show that the evaluation outcomes of the designed performance evaluation model for the piano are fundamentally consistent with the real levels of the players with assured feasibility; after 3k times of the training periods, the error of the RNN model is close to 0.01 and the network converges.

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“…Recently, many approaches have been proposed, exploring different techniques for different types of IDS (Phadke, Kulkarni, Bhawalkar, & Bhattad, 2019; Lansky et al. , 2021).…”

Section: Related Workmentioning

confidence: 99%

Enabling intrusion detection systems with dueling double deep Q-learning

Badr

2022

DTS

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PurposeIn this research, the authors demonstrate the advantage of reinforcement learning (RL) based intrusion detection systems (IDS) to solve very complex problems (e.g. selecting input features, considering scarce resources and constrains) that cannot be solved by classical machine learning. The authors include a comparative study to build intrusion detection based on statistical machine learning and representational learning, using knowledge discovery in databases (KDD) Cup99 and Installation Support Center of Expertise (ISCX) 2012.Design/methodology/approachThe methodology applies a data analytics approach, consisting of data exploration and machine learning model training and evaluation. To build a network-based intrusion detection system, the authors apply dueling double deep Q-networks architecture enabled with costly features, k-nearest neighbors (K-NN), support-vector machines (SVM) and convolution neural networks (CNN).FindingsMachine learning-based intrusion detection are trained on historical datasets which lead to model drift and lack of generalization whereas RL is trained with data collected through interactions. RL is bound to learn from its interactions with a stochastic environment in the absence of a training dataset whereas supervised learning simply learns from collected data and require less computational resources.Research limitations/implicationsAll machine learning models have achieved high accuracy values and performance. One potential reason is that both datasets are simulated, and not realistic. It was not clear whether a validation was ever performed to show that data were collected from real network traffics.Practical implicationsThe study provides guidelines to implement IDS with classical supervised learning, deep learning and RL.Originality/valueThe research applied the dueling double deep Q-networks architecture enabled with costly features to build network-based intrusion detection from network traffics. This research presents a comparative study of reinforcement-based instruction detection with counterparts built with statistical and representational machine learning.

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Deep Learning-Based Intrusion Detection Systems: A Systematic Review

Cited by 135 publications

References 202 publications

TAD: Transfer learning-based multi-adversarial detection of evasion attacks against network intrusion detection systems

TAD: Transfer learning-based multi-adversarial detection of evasion attacks against network intrusion detection systems

Educational Evaluation of Piano Performance by the Deep Learning Neural Network Model

Enabling intrusion detection systems with dueling double deep Q-learning

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