Towards secure intrusion detection systems using deep learning techniques: Comprehensive analysis and review

Journal of Network and Computer Applications - Tập 187 - Trang 103111 - 2021
Sang-Woong Lee1, Haval Mohammed Sidqi2, Mokhtar Mohammadi3, Shima Rashidi4, Amir Masoud Rahmani5,6, Mohammad Masdari7, Mehdi Hosseinzadeh1
1Pattern Recognition and Machine Learning Lab, Gachon University, 1342, Seongnamdaero, Sujeonggu, Seongnam 13120, Republic of Korea
2Department of Database, College of Informatics, Sulaimani Polytechnic University, Sulaymaniyah, Iraq
3Department of Information Technology, Lebanese French University, Erbil, Kurdistan Region, Iraq
4Department of Computer Science, University of Human Development, Sulaymaniyah, Iraq
5Department of Computer Science, Khazar University, Baku, Azerbaijan
6Future Technology Research Center, National Yunlin University of Science and Technology, Yunlin, Taiwan
7Department of computer science, Urmia Branch, Islamic Azad University, Urmia, Iran

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AbdAllah, 2018, Preventing unauthorized access in information centric networking, Security and Privacy, 1, e33, 10.1002/spy2.33

Abusitta, 2019, A deep learning approach for proactive multi-cloud cooperative intrusion detection system, Future Generat. Comput. Syst., 98, 308, 10.1016/j.future.2019.03.043

Akiba, 2017

Al Jallad, 2019, Big data analysis and distributed deep learning for next-generation intrusion detection system optimization, Journal of Big Data, 6, 1, 10.1186/s40537-019-0248-6

Al-Garadi, 2020, A survey of machine and deep learning methods for internet of things (IoT) security, IEEE Communications Surveys & Tutorials, 22, 1646, 10.1109/COMST.2020.2988293

Al-Qatf, 2018, Deep learning approach combining sparse autoencoder with SVM for network intrusion detection, IEEE Access, 6, 52843, 10.1109/ACCESS.2018.2869577

Albawi, 2017, Understanding of a convolutional neural network, 1

Aldweesh, 2020, Deep learning approaches for anomaly-based intrusion detection systems: a survey, taxonomy, and open issues, Knowl. Base Syst., 189, 105124, 10.1016/j.knosys.2019.105124

Almiani, 2020, Deep recurrent neural network for IoT intrusion detection system, Simulat. Model. Pract. Theor., 101, 102031, 10.1016/j.simpat.2019.102031

Alom, 2017, Network intrusion detection for cyber security using unsupervised deep learning approaches, 63

Alom, 2019, A state-of-the-art survey on deep learning theory and architectures, Electronics, 8, 292, 10.3390/electronics8030292

Andresini, 2021, Nearest cluster-based intrusion detection through convolutional neural networks, Knowl. Base Syst., 216, 106798, 10.1016/j.knosys.2021.106798

Asharf, 2020, A review of intrusion detection systems using machine and deep learning in internet of things: challenges, solutions and future directions, Electronics, 9, 1177, 10.3390/electronics9071177

Ashfaq, 2017, Fuzziness based semi-supervised learning approach for intrusion detection system, Inf. Sci., 378, 484, 10.1016/j.ins.2016.04.019

Azmin, 2020, Network intrusion detection system based on conditional variational Laplace AutoEncoder, 82

Ben-Nun, 2019, Demystifying parallel and distributed deep learning: an in-depth concurrency analysis, ACM Comput. Surv., 52, 1, 10.1145/3320060

Binbusayyis, 2021, 1

A. Boukhalfa, A. Abdellaoui, N. Hmina, and H. Chaoui, "LSTM deep learning method for network intrusion detection system," Int. J. Electr. Comput. Eng. (2088-8708), vol. 10, 2020.

Boutros, 2018, You cannot improve what you do not measure: FPGA vs. ASIC efficiency gaps for convolutional neural network inference, ACM Trans. Reconfigurable Technol. Syst. (TRETS), 11, 1, 10.1145/3242898

Bridges, 2019, A survey of intrusion detection systems leveraging host data, ACM Comput. Surv., 52, 1, 10.1145/3344382

Bu, 2017, A hybrid system of deep learning and learning classifier system for database intrusion detection, 615

Bu, 2020, A convolutional neural-based learning classifier system for detecting database intrusion via insider attack, Inf. Sci., 512, 123, 10.1016/j.ins.2019.09.055

Butun, 2013, A survey of intrusion detection systems in wireless sensor networks, IEEE communications surveys & tutorials, 16, 266, 10.1109/SURV.2013.050113.00191

Chaabouni, 2019, Network intrusion detection for IoT security based on learning techniques, IEEE Communications Surveys & Tutorials, 21, 2671, 10.1109/COMST.2019.2896380

Chalapathy, 2019

Choi, 2019, Unsupervised learning approach for network intrusion detection system using autoencoders, J. Supercomput., 75, 5597, 10.1007/s11227-019-02805-w

Chuang, 2019, Applying deep learning to balancing network intrusion detection datasets, 213

da Costa, 2019, Internet of Things: a survey on machine learning-based intrusion detection approaches, Comput. Network., 151, 147, 10.1016/j.comnet.2019.01.023

Dawoud, 2018, Deep learning and software-defined networks: towards secure IoT architecture, Internet of Things, 3, 82, 10.1016/j.iot.2018.09.003

Dutta, 2020, Generative adversarial networks in security: a survey

Elsaeidy, 2019, Intrusion detection in smart cities using Restricted Boltzmann Machines, J. Netw. Comput. Appl., 135, 76, 10.1016/j.jnca.2019.02.026

Fernandes, 2019, A comprehensive survey on network anomaly detection, Telecommun. Syst., 70, 447, 10.1007/s11235-018-0475-8

Ferrag, 2020, Deep learning for cyber security intrusion detection: approaches, datasets, and comparative study, Journal of Information Security and Applications, 50, 102419, 10.1016/j.jisa.2019.102419

Folino, 2016, Ensemble based collaborative and distributed intrusion detection systems: a survey, J. Netw. Comput. Appl., 66, 1, 10.1016/j.jnca.2016.03.011

Geetha, 2020, A review on the effectiveness of machine learning and deep learning algorithms for cyber security, Arch. Comput. Methods Eng., 1

Gui, 2020

Haggag, 2020, Implementing a deep learning model for intrusion detection on Apache Spark platform, IEEE Access, 8, 163660, 10.1109/ACCESS.2020.3019931

Hande, 2020, A survey on intrusion detection system for software defined networks (SDN), Int. J. Bus. Data Commun. Netw., 16, 28, 10.4018/IJBDCN.2020010103

Hara, 2020, Intrusion detection system using semi-supervised learning with adversarial auto-encoder, 1

Hodo, 2017

Hosseinzadeh, 2020, Improving security using SVM-based anomaly detection: issues and challenges, Soft Computing, 1

Hu, 2020, A novel wireless network intrusion detection method based on adaptive synthetic sampling and an improved convolutional neural network, IEEE Access, 8, 195741, 10.1109/ACCESS.2020.3034015

Huang, 2020, IGAN-IDS: an imbalanced generative adversarial network towards intrusion detection system in ad-hoc networks, Ad Hoc Netw., 105, 102177, 10.1016/j.adhoc.2020.102177

Idrissi, 2020, IoT security with deep learning-based intrusion detection systems: a systematic literature review, 1

Ieracitano, 2020, A novel statistical analysis and autoencoder driven intelligent intrusion detection approach, Neurocomputing, 387, 51, 10.1016/j.neucom.2019.11.016

Jafarian, 2020, A survey and classification of the security anomaly detection mechanisms in software defined networks, Cluster Comput., 1

Ji, 2020, A network intrusion detection approach based on asymmetric convolutional autoencoder, 126

Jin, 2019, Parallel deep learning detection network in the MIMO channel, IEEE Commun. Lett., 24, 126, 10.1109/LCOMM.2019.2950201

Jing, 2016, Network intrusion detection method based on relevance deep learning, 237

Kaur, 2019, Hybrid intrusion detection and signature generation using deep recurrent neural networks, Neural Comput. Appl., 1

Keegan, 2016, A survey of cloud-based network intrusion detection analysis, Human-centric Computing and Information Sciences, 6, 19, 10.1186/s13673-016-0076-z

Keyvanrad, 2014

Khalaf, 2019, Comprehensive review of artificial intelligence and statistical approaches in distributed denial of service attack and defense methods, IEEE Access, 7, 51691, 10.1109/ACCESS.2019.2908998

Khan, 2019, A novel two-stage deep learning model for efficient network intrusion detection, IEEE Access, 7, 30373, 10.1109/ACCESS.2019.2899721

Khan, 2020, A survey on intrusion detection and prevention in wireless ad-hoc networks, J. Syst. Architect., 105, 101701, 10.1016/j.sysarc.2019.101701

Kim, 2020, AI-IDS: application of deep learning to real-time Web intrusion detection, IEEE Access, 8, 70245, 10.1109/ACCESS.2020.2986882

Kumar, 2020, Statistical analysis of the UNSW-NB15 dataset for intrusion detection, 279

Kwon, 2019, A survey of deep learning-based network anomaly detection, Cluster Comput., 1

Kwon, 2019, A survey of deep learning-based network anomaly detection, Cluster Comput., 22, 949, 10.1007/s10586-017-1117-8

Le, 2019, Network intrusion detection based on novel feature selection model and various recurrent neural networks, Appl. Sci., 9, 1392, 10.3390/app9071392

Leevy, 2020, A survey and analysis of intrusion detection models based on CSE-CIC-IDS2018 Big Data, Journal of Big Data, 7, 1, 10.1186/s40537-020-00382-x

Li, 2020, Building auto-encoder intrusion detection system based on random forest feature selection, Comput. Secur., 95, 101851, 10.1016/j.cose.2020.101851

Li, 2020, DeepFed: federated deep learning for intrusion detection in industrial cyber-physical systems, IEEE Transactions on Industrial Informatics

Liu, 2018, Fast neural network training on FPGA using quasi-Newton optimization method, IEEE Trans. Very Large Scale Integr. Syst., 26, 1575, 10.1109/TVLSI.2018.2820016

Liu, 2019, Deep learning based encryption policy intrusion detection using commodity WiFi, 2129

Liu, 2020

Lopez-Martin, 2017, Conditional variational autoencoder for prediction and feature recovery applied to intrusion detection in iot, Sensors, 17, 1967, 10.3390/s17091967

Louati, 2020, A deep learning-based multi-agent system for intrusion detection, SN Applied Sciences, 2, 1, 10.1007/s42452-020-2414-z

Lu, 2019

Mahjabin, 2017, A survey of distributed denial-of-service attack, prevention, and mitigation techniques, Int. J. Distributed Sens. Netw., 13

Masdari, 2016, A survey and taxonomy of DoS attacks in cloud computing, Secur. Commun. Network., 9, 3724, 10.1002/sec.1539

Masdari, 2020, 106301

Masdari, 2020, Efficient VM migrations using forecasting techniques in cloud computing: a comprehensive review, Cluster Comput., 1

Masdari, 2021, Towards fuzzy anomaly detection-based security: a comprehensive review, Fuzzy Optim. Decis. Making, 20, 1, 10.1007/s10700-020-09332-x

Masdari, 2019, A survey and classification of the workload forecasting methods in cloud computing, Cluster Comput., 1

Masdari, 2019, Green cloud computing using proactive virtual machine placement: challenges and issues, J. Grid Comput., 1

Mayuranathan, 2019, Best features based intrusion detection system by RBM model for detecting DDoS in cloud environment, Journal of Ambient Intelligence and Humanized Computing, 1

McHugh, 2000, Testing intrusion detection systems: a critique of the 1998 and 1999 darpa intrusion detection system evaluations as performed by lincoln laboratory, ACM Trans. Inf. Syst. Secur., 3, 262, 10.1145/382912.382923

Meena, 2017, A review paper on IDS classification using KDD 99 and NSL KDD dataset in WEKA, 553

Mighan, 2018, Deep learning based latent feature extraction for intrusion detection, 1511

Mighan, 2020, A novel scalable intrusion detection system based on deep learning, Int. J. Inf. Secur., 1

Mishra, 2017, Intrusion detection techniques in cloud environment: a survey, J. Netw. Comput. Appl., 77, 18, 10.1016/j.jnca.2016.10.015

Nadeem, 2013, A survey of MANET intrusion detection & prevention approaches for network layer attacks, IEEE communications surveys & tutorials, 15, 2027, 10.1109/SURV.2013.030713.00201

Nguyen, 2020, Genetic convolutional neural network for intrusion detection systems, Future Generat. Comput. Syst., 113, 418, 10.1016/j.future.2020.07.042

Nguyen, 2019, Gee: a gradient-based explainable variational autoencoder for network anomaly detection, 91

Nie, 2020, Data-driven intrusion detection for intelligent Internet of vehicles: a deep convolutional neural network-based method, IEEE Transactions on Network Science and Engineering, 7, 2219, 10.1109/TNSE.2020.2990984

Parvat, 2017, Network intrusion detection system using ensemble of binary deep learning classifiers, 3

Patel, 2013, An intrusion detection and prevention system in cloud computing: a systematic review, J. Netw. Comput. Appl., 36, 25, 10.1016/j.jnca.2012.08.007

Peng, 2019, Network intrusion detection based on deep learning, 431

Pouyanfar, 2018, A survey on deep learning: algorithms, techniques, and applications, ACM Comput. Surv., 51, 1

Preethi, 2020, 1

Ren, 2020

Resende, 2018, A survey of random forest based methods for intrusion detection systems, ACM Comput. Surv., 51, 1, 10.1145/3178582

Ring, 2019, Flow-based network traffic generation using generative adversarial networks, Comput. Secur., 82, 156, 10.1016/j.cose.2018.12.012

Riyaz, 2020, A deep learning approach for effective intrusion detection in wireless networks using CNN, Soft Computing, 24, 17265, 10.1007/s00500-020-05017-0

de Rosa, 2021, Enhancing anomaly detection through restricted Boltzmann machine features projection, Int. J. Inf. Technol., 13, 49

Sadaf, 2020, Intrusion detection based on autoencoder and isolation Forest in fog computing, IEEE Access, 8, 167059, 10.1109/ACCESS.2020.3022855

Salakhutdinov, 2009, Deep Boltzmann machines, 448

Saraeian, 2020, Application of deep learning technique in an intrusion detection system, Int. J. Comput. Intell. Appl., 19, 2050016, 10.1142/S1469026820500169

Sarker, 2021, Deep cybersecurity: a comprehensive overview from neural network and deep learning perspective, SN Computer Science, 2, 1, 10.1007/s42979-021-00535-6

Sarker, 2020, Cybersecurity data science: an overview from machine learning perspective, Journal of Big Data, 7, 1, 10.1186/s40537-020-00318-5

Sergeev, 2018

Shahriar, 2020, Generative adversarial networks assisted intrusion detection system, 376

Sharafaldin, 2018, A detailed analysis of the cicids2017 data set, 172

Shone, 2018, A deep learning approach to network intrusion detection, IEEE transactions on emerging topics in computational intelligence, 2, 41, 10.1109/TETCI.2017.2772792

Shu, 2020, Collaborative intrusion detection for VANETs: a deep learning-based distributed SDN approach, IEEE Trans. Intell. Transport. Syst.

Sohn, 2020, 114170

Von Solms, 2013, From information security to cyber security, Comput. Secur., 38, 97, 10.1016/j.cose.2013.04.004

Song, 2020, In-vehicle network intrusion detection using deep convolutional neural network, Vehicular Communications, 21, 100198, 10.1016/j.vehcom.2019.100198

Soni, 2015, A survey on intrusion detection techniques in MANET, 1027

Sperotto, 2010, An overview of IP flow-based intrusion detection, IEEE communications surveys & tutorials, 12, 343, 10.1109/SURV.2010.032210.00054

Su, 2020, BAT: deep learning methods on network intrusion detection using NSL-KDD dataset, IEEE Access, 8, 29575, 10.1109/ACCESS.2020.2972627

Sultana, 2019, Survey on SDN based network intrusion detection system using machine learning approaches, Peer-to-Peer Networking and Applications, 12, 493, 10.1007/s12083-017-0630-0

Tang, 2018, Deep recurrent neural network for intrusion detection in sdn-based networks, 202

Tang, 2019, Intrusion detection in sdn-based networks: deep recurrent neural network approach, 175

Tang, 2020, An efficient intrusion detection method based on LightGBM and autoencoder, Symmetry, 12, 1458, 10.3390/sym12091458

Tang, 2020, SAAE-DNN: deep learning method on intrusion detection, Symmetry, 12, 1695, 10.3390/sym12101695

Tang, 2020, DeepIDS: deep learning approach for intrusion detection in software defined networking, Electronics, 9, 1533, 10.3390/electronics9091533

Tavallaee, 2009, A detailed analysis of the KDD CUP 99 data set, 1

Tefai, 2020, ASIC implementation of a pre-trained neural network for ECG feature extraction, 1

Telikani, 2019, 100122

Thamilarasu, 2019, Towards deep-learning-driven intrusion detection for the internet of things, Sensors, 19, 1977, 10.3390/s19091977

Tschannen, 2018

Umer, 2017, Flow-based intrusion detection: techniques and challenges, Comput. Secur., 70, 238, 10.1016/j.cose.2017.05.009

Venkataramanaiah, 2020, Deep neural network training accelerator designs in ASIC and FPGA, 21

Verma, 2018, Statistical analysis of CIDDS-001 dataset for network intrusion detection systems using distance-based machine learning, Procedia Computer Science, 125, 709, 10.1016/j.procs.2017.12.091

Security Vulnerabilities," 2021.

Wang, 2017, HAST-IDS: learning hierarchical spatial-temporal features using deep neural networks to improve intrusion detection, Ieee Access, 6, 1792, 10.1109/ACCESS.2017.2780250

Wang, 2019, Deep learning for sensor-based activity recognition: a survey, Pattern Recogn. Lett., 119, 3, 10.1016/j.patrec.2018.02.010

Wang, 2019, An improved deep learning based intrusion detection method, 2092

Wang, 2019, Effective android malware detection with a hybrid model based on deep autoencoder and convolutional neural network, Journal of Ambient Intelligence and Humanized Computing, 10, 3035, 10.1007/s12652-018-0803-6

Wang, 2020, A network intrusion detection method based on deep multi-scale convolutional neural network, Int. J. Wireless Inf. Network, 27, 503, 10.1007/s10776-020-00495-3

Wang, 2021, Deep belief network integrating improved kernel-based extreme learning machine for network intrusion detection, IEEE Access, 9, 16062, 10.1109/ACCESS.2021.3051074

Wu, 2018, A novel intrusion detection model for a massive network using convolutional neural networks, Ieee Access, 6, 50850, 10.1109/ACCESS.2018.2868993

Xiao, 2019, An intrusion detection model based on feature reduction and convolutional neural networks, IEEE Access, 7, 42210, 10.1109/ACCESS.2019.2904620

Xin, 2018, Machine learning and deep learning methods for cybersecurity, Ieee access, 6, 35365, 10.1109/ACCESS.2018.2836950

Xu, 2018, An intrusion detection system using a deep neural network with gated recurrent units, IEEE Access, 6, 48697, 10.1109/ACCESS.2018.2867564

Xu, 2020

Yadav, 2021, 137

Yan, 2018, Effective feature extraction via stacked sparse autoencoder to improve intrusion detection system, IEEE Access, 6, 41238, 10.1109/ACCESS.2018.2858277

Yang, 2019, Wireless network intrusion detection based on improved convolutional neural network, Ieee Access, 7, 64366, 10.1109/ACCESS.2019.2917299

Yang, 2019, Combined wireless network intrusion detection model based on deep learning, IEEE Access, 7, 82624, 10.1109/ACCESS.2019.2923814

Yang, 2019, Improving the classification effectiveness of intrusion detection by using improved conditional variational autoencoder and deep neural network, Sensors, 19, 2528, 10.3390/s19112528

Yang, 2020, Real-time intrusion detection in wireless network: a deep learning-based intelligent mechanism, IEEE Access, 8, 170128, 10.1109/ACCESS.2020.3019973

Yang, 2020, Network intrusion detection based on supervised adversarial variational auto-encoder with regularization, IEEE Access, 8, 42169, 10.1109/ACCESS.2020.2977007

Yin, 2017, A deep learning approach for intrusion detection using recurrent neural networks, Ieee Access, 5, 21954, 10.1109/ACCESS.2017.2762418

Yu, 2019, A review of recurrent neural networks: LSTM cells and network architectures, Neural Comput., 31, 1235, 10.1162/neco_a_01199

Zarpelão, 2017, A survey of intrusion detection in Internet of Things, J. Netw. Comput. Appl., 84, 25, 10.1016/j.jnca.2017.02.009

Zavrak, 2020, Anomaly-based intrusion detection from network flow features using variational autoencoder, IEEE Access, 8, 108346, 10.1109/ACCESS.2020.3001350

Zhang, 2018, An overview on restricted Boltzmann machines, Neurocomputing, 275, 1186, 10.1016/j.neucom.2017.09.065

Zhang, 2018, An effective deep learning based scheme for network intrusion detection, 682

Zhang, 2018, Deep learning intrusion detection model based on optimized imbalanced network data, 1128

Zhang, 2019, Intrusion detection for IoT based on improved genetic algorithm and deep belief network, IEEE Access, 7, 31711, 10.1109/ACCESS.2019.2903723

Zhang, 2019, Intrusion detection system using deep learning for in-vehicle security, Ad Hoc Netw., 95, 101974, 10.1016/j.adhoc.2019.101974

Zhang, 2020, An effective convolutional neural network based on SMOTE and Gaussian mixture model for intrusion detection in imbalanced dataset, Comput. Network., 177, 107315, 10.1016/j.comnet.2020.107315

Zhang, 2020, Network intrusion detection based on conditional Wasserstein generative adversarial network and cost-sensitive stacked autoencoder, IEEE Access, 8, 190431, 10.1109/ACCESS.2020.3031892

Zhang, 2020, A network intrusion detection method based on deep learning with higher accuracy, Procedia Computer Science, 174, 50, 10.1016/j.procs.2020.06.055

Zhang, 2020, Tiki-taka: attacking and defending deep learning-based intrusion detection systems, 27

Zixu, 2020, Generative adversarial network and auto encoder based anomaly detection in distributed IoT networks, 1