Cited by 507Network Security and Intrusion DetectionNetwork Packet Processing and OptimizationArtificial Immune Systems Applications

The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.

Enhanced Network Anomaly Detection Based on Deep Neural Networks

Open access

Naseer, Sheraz, Saleem, Yasir, Khalid, Shehzad, Bashir, Muhammad Khawar, Han, Jihun, Iqbal, Muhammad Munwar, Han, Kijun·2018·IEEE Access

DOI 10.1109/access.2018.2863036

Open this paper in CoChat — free account

Abstract

Due to the monumental growth of Internet applications in the last decade, the need for security of information network has increased manifolds. As a primary defense of network infrastructure, an intrusion detection system is expected to adapt to dynamically changing threat landscape. Many supervised and unsupervised techniques have been devised by researchers from the discipline of machine learning and data mining to achieve reliable detection of anomalies. Deep learning is an area of machine learning which applies neuron-like structure for learning tasks. Deep learning has profoundly changed the way we approach learning tasks by delivering monumental progress in different disciplines like speech processing, computer vision, and natural language processing to name a few. It is only relevant that this new technology must be investigated for information security applications. The aim of this paper is to investigate the suitability of deep learning approaches for anomaly-based intrusion detection system. For this research, we developed anomaly detection models based on different deep neural network structures, including convolutional neural networks, autoencoders, and recurrent neural networks. These deep models were trained on NSLKDD training data set and evaluated on both test data sets provided by NSLKDD, namely NSLKDDTest+ and NSLKDDTest21. All experiments in this paper are performed by authors on a GPU-based test bed. Conventional machine learning-based intrusion detection models were implemented using well-known classification techniques, including extreme learning machine, nearest neighbor, decision-tree, random-forest, support vector machine, naive-bays, and quadratic discriminant analysis. Both deep and conventional machine learning models were evaluated using well-known classification metrics, including receiver operating characteristics, area under curve, precision-recall curve, mean average precision and accuracy of classification. Experimental results of deep IDS models showed promising results for real-world application in anomaly detection systems.

Integrity signals

Registry

Crossref · 10.1109/access.2018.2863036

Cites

44 of 44 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Long Short-Term Memory
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
An Introduction to the Bootstrap
Efron, B., & Tibshirani, R. J. (1994) · doi:10.1201/9780429246593 · Cited by 40,500
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
Untitled work
(2014) · Cited by 33,419
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.123 · Cited by 19,741
Extreme learning machine: Theory and applications
Huang, G.-B., Zhu, Q.-Y., & Siew, C.-K. (2006) · Neurocomputing · doi:10.1016/j.neucom.2005.12.126 · Cited by 13,285
Approximation capabilities of multilayer feedforward networks
Hornik, K. (1991) · Neural Networks · doi:10.1016/0893-6080(91)90009-t · Cited by 6,091

Flagged first, then by citations

Show all 44 references

Research works printed but not linked: 8

mcginnis, 2017, Beyond One-Hot An Exploration of Categorical Variables
mcginnis, 2017, BaseN Encoding and Grid Search in Categorical Variables
2011, Contrast Coding Systems for Categorical Variables
goodfellow, 2016, Deep Learning
minsky, 1969, Perceptrons
chollet, 2015, Keras
zhang, 2017, Strategies to Encode Categorical Variables With Many Categories
wang, 2015, The Applications of Deep Learning on Traffic Identification

Sorted by citations

Deep Learning Approach for Intelligent Intrusion Detection System
Vinayakumar, R., Alazab, M., Soman, K. P., Poornachandran, P., Al-Nemrat, A., & Venkatraman, S. (2019) · IEEE Access · doi:10.1109/access.2019.2895334 · Cited by 2,228
Network intrusion detection system: A systematic study of machine learning and deep learning approaches
Ahmad, Z., Khan, A. S., Shiang, C. W., Abdullah, J., & Ahmad, F. (2020) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.4150 · Cited by 1,514
Machine Learning for Anomaly Detection: A Systematic Review
Nassif, A. B., Talib, M. A., Nasir, Q., & Dakalbab, F. M. (2021) · IEEE Access · doi:10.1109/access.2021.3083060 · Cited by 748
Deep learning approaches for anomaly-based intrusion detection systems: A survey, taxonomy, and open issues
Aldweesh, A., Derhab, A., & Emam, A. Z. (2019) · Knowledge-Based Systems · doi:10.1016/j.knosys.2019.105124 · Cited by 703
Robust Intelligent Malware Detection Using Deep Learning
Vinayakumar, R., Alazab, M., Soman, K. P., Poornachandran, P., & Venkatraman, S. (2019) · IEEE Access · doi:10.1109/access.2019.2906934 · Cited by 622
Adversarial Machine Learning for Network Intrusion Detection Systems: A Comprehensive Survey
He, K., Kim, D. S., & Asghar, M. R. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3233793 · Cited by 518
A survey on anomaly detection for technical systems using LSTM networks
Lindemann, B., Maschler, B., Sahlab, N., & Weyrich, M. (2021) · Computers in Industry · doi:10.1016/j.compind.2021.103498 · Cited by 484
Deep Learning for Network Traffic Monitoring and Analysis (NTMA): A Survey
Abbasi, M., Shahraki, A., & Taherkordi, A. (2021) · Computer Communications · doi:10.1016/j.comcom.2021.01.021 · Cited by 484
CNN-LSTM: Hybrid Deep Neural Network for Network Intrusion Detection System
Halbouni, A. H., Gunawan, T. S., Habaebi, M. H., Halbouni, M., Kartiwi, M., & Ahmad, R. B. (2022) · IEEE Access · doi:10.1109/access.2022.3206425 · Cited by 500
A deep learning method with wrapper based feature extraction for wireless intrusion detection system
Kasongo, S. M., & Sun, Y. (2020) · Computers & Security · doi:10.1016/j.cose.2020.101752 · Cited by 374
A survey on intrusion detection system: feature selection, model, performance measures, application perspective, challenges, and future research directions
Thakkar, A., & Lohiya, R. (2021) · Artificial Intelligence Review · doi:10.1007/s10462-021-10037-9 · Cited by 394
Robust detection for network intrusion of industrial IoT based on multi-CNN fusion
Li, Y., Xu, Y., Liu, Z., Hou, H., Zheng, Y., Xin, Y., Zhao, Y., & Cui, L. (2019) · Measurement · doi:10.1016/j.measurement.2019.107450 · Cited by 354
Anomaly-Based Intrusion Detection From Network Flow Features Using Variational Autoencoder
Zavrak, S., & İskefiyeli, M. (2020) · IEEE Access · doi:10.1109/access.2020.3001350 · Cited by 341
Enhancing intrusion detection: a hybrid machine and deep learning approach
Sajid, M., Malik, K. R., Almogren, A., Malik, T. S., Khan, A. H., Tanveer, J., & Rehman, A. U. (2024) · Journal of Cloud Computing Advances Systems and Applications · doi:10.1186/s13677-024-00685-x · Cited by 292
AI-IDS: Application of Deep Learning to Real-Time Web Intrusion Detection
Kim, A., Park, M., & Lee, D. H. (2020) · IEEE Access · doi:10.1109/access.2020.2986882 · Cited by 278
IDS-INT: Intrusion detection system using transformer-based transfer learning for imbalanced network traffic
Ullah, F., Ullah, S., Srivastava, G., & Lin, J. C. (2023) · Digital Communications and Networks · doi:10.1016/j.dcan.2023.03.008 · Cited by 275
Design and Development of RNN Anomaly Detection Model for IoT Networks
Ullah, I., & Mahmoud, Q. H. (2022) · IEEE Access · doi:10.1109/access.2022.3176317 · Cited by 308
A Unified Deep Learning Anomaly Detection and Classification Approach for Smart Grid Environments
Siniosoglou, I., Grammatikis, P. I. R., Efstathopoulos, G., Fouliras, P., & Sarigiannidis, P. G. (2021) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2021.3078381 · Cited by 283
Comparative Analysis of Intrusion Detection Systems and Machine Learning Based Model Analysis through Decision Tree
Azam, Z., Islam, M. M., & Huda, M. N. (2023) · IEEE Access · doi:10.1109/access.2023.3296444 · Cited by 265
Machine Learning Models for Secure Data Analytics: A taxonomy and threat model
Gupta, R., Tanwar, S., Tyagi, S., & Kumar, N. (2020) · Computer Communications · doi:10.1016/j.comcom.2020.02.008 · Cited by 228
Machine Learning and Deep Learning Approaches for CyberSecurity: A Review
Halbouni, A. H., Gunawan, T. S., Habaebi, M. H., Halbouni, M., Kartiwi, M., & Ahmad, R. B. (2022) · IEEE Access · doi:10.1109/access.2022.3151248 · Cited by 227
Intrusion Detection Based on Autoencoder and Isolation Forest in Fog Computing
Sadaf, K., & Sultana, J. (2020) · IEEE Access · doi:10.1109/access.2020.3022855 · Cited by 225
A Survey of CNN-Based Network Intrusion Detection
Mohammadpour, L., Ling, T. C., Liew, C. S., & Aryanfar, A. (2022) · Applied Sciences · doi:10.3390/app12168162 · Cited by 251
An Industrial Network Intrusion Detection Algorithm Based on Multifeature Data Clustering Optimization Model
Liang, W., Li, K., Long, J., Kui, X., & Zomaya, A. Y. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2946791 · Cited by 207
A Machine-Learning-Based Technique for False Data Injection Attacks Detection in Industrial IoT
Aboelwafa, M., Seddik, K. G., Eldefrawy, M. H., Gadallah, Y., & Gidlund, M. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2991693 · Cited by 205
Cyber threat detection based on artificial neural networks using event profiles
Lee, J., Kim, J., Kim, I., & Han, K. (2019) · IEEE Access · doi:10.1109/access.2019.2953095 · Cited by 188
“[1] proposed, implemented and trained intrusion detection models using different deep neural network architectures including CNNs, Autoencoders, and RNNs.”
BackgroundMethodInfluentialSelf-citation
Anomaly-based intrusion detection system for IoT application
Bhavsar, M., Roy, K., Kelly, J. C., & Odeyomi, O. T. (2023) · Discover Internet of Things · doi:10.1007/s43926-023-00034-5 · Cited by 213
A deep learning approach for effective intrusion detection in wireless networks using CNN
Riyaz, B., & Ganapathy, S. (2020) · Soft Computing · doi:10.1007/s00500-020-05017-0 · Cited by 171
Machine Learning Techniques to Detect a DDoS Attack in SDN: A Systematic Review
Ali, T. E., Chong, Y.-W., & Manickam, S. (2023) · Applied Sciences · doi:10.3390/app13053183 · Cited by 167
Intrusion Detection System for NSL-KDD Dataset Using Convolutional Neural Networks
Ding, Y., & Zhai, Y. (2018) · doi:10.1145/3297156.3297230 · Cited by 166
Tight Arms Race: Overview of Current Malware Threats and Trends in Their Detection
Caviglione, L., Choraś, M., Corona, I., Janicki, A., Mazurczyk, W., Pawlicki, M., & Wasielewska, K. (2020) · IEEE Access · doi:10.1109/access.2020.3048319 · Cited by 163
A comprehensive review on detection of cyber-attacks: Data sets, methods, challenges, and future research directions
Ahmetoğlu, H., & Daş, R. (2022) · Internet of Things · doi:10.1016/j.iot.2022.100615 · Cited by 169
Network Attacks Detection Methods Based on Deep Learning Techniques: A Survey
Wu, Y., Wei, D., & Feng, J. (2020) · Security and Communication Networks · doi:10.1155/2020/8872923 · Cited by 154
GAN augmentation to deal with imbalance in imaging-based intrusion detection
Andresini, G., Appice, A., Rose, L., & Malerba, D. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2021.04.017 · Cited by 147
Multi-Channel Deep Feature Learning for Intrusion Detection
Andresini, G., Appice, A., Mauro, N. D., Loglisci, C. A. A. M. D., & Malerba, D. (2020) · IEEE Access · doi:10.1109/access.2020.2980937 · Cited by 148
Detection of Malware by Deep Learning as CNN-LSTM Machine Learning Techniques in Real Time
Akhtar, M. S., & Feng, T. (2022) · Symmetry · doi:10.3390/sym14112308 · Cited by 163
RouteNet-Fermi: Network Modeling With Graph Neural Networks
Ferriol-Galmés, M., Paillissé, J., Suárez‐Varela, J., Rusek, K., Xiao, S., Shi, X., Cheng, X., Barlet‐Ros, P., & Cabellos‐Aparicio, A. (2023) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2023.3269983 · Cited by 130
An optimized ensemble prediction model using AutoML based on soft voting classifier for network intrusion detection
Khan, M., Iqbal, N., Imran, I., Jamil, H., & Kim, D. (2022) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2022.103560 · Cited by 121
Deep learning-driven methods for network-based intrusion detection systems: A systematic review
Chinnasamy, R., Subramanian, M., Easwaramoorthy, S. V., & Cho, J. (2025) · ICT Express · doi:10.1016/j.icte.2025.01.005 · Cited by 145
Network Anomaly Detection Using Memory-Augmented Deep Autoencoder
Min, B., Yoo, J., Kim, S. S., Shin, D., & Shin, D. (2021) · IEEE Access · doi:10.1109/access.2021.3100087 · Cited by 116
Nearest cluster-based intrusion detection through convolutional neural networks
Andresini, G., Appice, A., & Malerba, D. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.106798 · Cited by 117
Conditional Variational Auto-Encoder and Extreme Value Theory Aided Two-Stage Learning Approach for Intelligent Fine-Grained Known/Unknown Intrusion Detection
Yang, J., Chen, X., Chen, S., Jiang, X., & Tan, X. (2021) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2021.3083422 · Cited by 114
Identifying and Benchmarking Key Features for Cyber Intrusion Detection: An Ensemble Approach
Binbusayyis, A., & Vaiyapuri, T. (2019) · IEEE Access · doi:10.1109/access.2019.2929487 · Cited by 109
Toward Developing Efficient Conv-AE-Based Intrusion Detection System Using Heterogeneous Dataset
Khan, M. A., & Kim, J. (2020) · Electronics · doi:10.3390/electronics9111771 · Cited by 107
A Survey of Deep Learning Technologies for Intrusion Detection in Internet of Things
Liao, H., Murah, M. Z., Hasan, M. K., Aman, A. H. M., Jin, F., Hu, X., & Khan, A. U. R. (2024) · IEEE Access · doi:10.1109/access.2023.3349287 · Cited by 135
Network Traffic Anomaly Detection via Deep Learning
Fotiadou, K., Velivassaki, T.-H., Voulkidis, A. C., Skias, D., Tsekeridou, S., & Zahariadis, T. B. (2021) · Information · doi:10.3390/info12050215 · Cited by 103
Image-based time series forecasting: A deep convolutional neural network approach
Semenoglou, A.-A., Spiliotis, E., & Assimakopoulos, V. (2022) · Neural Networks · doi:10.1016/j.neunet.2022.10.006 · Cited by 111
Artificial Intelligence Applications and Self-Learning 6G Networks for Smart Cities Digital Ecosystems: Taxonomy, Challenges, and Future Directions
Ismail, L., & Buyya, R. (2022) · Sensors · doi:10.3390/s22155750 · Cited by 103
Security of Things Intrusion Detection System for Smart Healthcare
Iwendi, C., Anajemba, J. H., Biamba, C. N., & Ngabo, D. (2021) · Electronics · doi:10.3390/electronics10121375 · Cited by 88
An adaptable deep learning-based intrusion detection system to zero-day attacks
Soltani, M., Ousat, B., Siavoshani, M. J., & Jahangir, A. H. (2023) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2023.103516 · Cited by 87
A Deep Learning Approach for Intrusion Detection Systems in Cloud Computing Environments
Aljuaid, W. H., & Alshamrani, S. S. (2024) · Applied Sciences · doi:10.3390/app14135381 · Cited by 111
Machine Learning and Deep Learning Based Traffic Classification and Prediction in Software Defined Networking
Mohammed, A. R., Mohammed, S., & Shirmohammadi, S. (2019) · IEEE International Symposium on Measurements & Networking (M&N) · doi:10.1109/iwmn.2019.8805044 · Cited by 83
A network intrusion detection method based on semantic Re-encoding and deep learning
Wu, Z., Wang, J., Hu, L., Zhang, Z., & Wu, H. (2020) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2020.102688 · Cited by 83
An efficient network behavior anomaly detection using a hybrid DBN-LSTM network
Chen, A., Fu, Y. L., Zheng, X., & Lu, G. (2022) · Computers & Security · doi:10.1016/j.cose.2021.102600 · Cited by 81
Deep SARSA-based reinforcement learning approach for anomaly network intrusion detection system
Mohamed, S., & Ejbali, R. (2022) · International Journal of Information Security · doi:10.1007/s10207-022-00634-2 · Cited by 85
A survey on neural networks for (cyber-) security and (cyber-) security of neural networks
Pawlicki, M., Kozik, R., & Choraś, M. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.06.002 · Cited by 84
Jellyfish optimized recurrent neural network for state of health estimation of lithium-ion batteries
Ansari, S., Ayob, A., Lipu, M. S. H., Hussain, A., & Saad, M. H. M. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121904 · Cited by 76
An Efficient Network Intrusion Detection and Classification System
Ahmad, I. A., Haq, Q. E. U., Imran, M., Alassafi, M. O., & AlGhamdi, R. A. (2022) · Mathematics · doi:10.3390/math10030530 · Cited by 72
Anomaly Detection in Network Traffic Using Advanced Machine Learning Techniques
Ness, S., Eswarakrishnan, V., Sridharan, H., Shinde, V., Janapareddy, N. V. P., & Dhanawat, V. (2025) · IEEE Access · doi:10.1109/access.2025.3526988 · Cited by 81
DRaNN: A Deep Random Neural Network Model for Intrusion Detection in Industrial IoT
Latif, S., Idrees, Z., Zou, Z., & Ahmad, J. E. (2020) · International Conference on UK-China Emerging Technologies (UCET) · doi:10.1109/ucet51115.2020.9205361 · Cited by 70
Recent advances in anomaly detection in Internet of Things: Status, challenges, and perspectives
Adhikari, D., Jiang, W., Zhan, J., Rawat, D. B., & Bhattarai, A. (2024) · Computer Science Review · doi:10.1016/j.cosrev.2024.100665 · Cited by 88
An Intrusion Detection System Based on Convolutional Neural Network for Imbalanced Network Traffic
Zhang, X., Jing, R., & Mi, J. (2019) · International Conference on Computer Science and Network Technology (ICCSNT) · doi:10.1109/iccsnt47585.2019.8962490 · Cited by 68
Machine and Deep Learning Based Comparative Analysis Using Hybrid Approaches for Intrusion Detection System
Rashid, A., Siddique, M. J., & Ahmed, S. M. (2020) · International Conference on Advancements in Computational Sciences (ICACS) · doi:10.1109/icacs47775.2020.9055946 · Cited by 67
Deep anomaly detection in packet payload
Liu, J., Song, X., Zhou, Y., Peng, X., Zhang, Y., Liu, P., Wu, D. O., & Zhu, C. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.01.146 · Cited by 65
Particle swarm optimized data-driven model for remaining useful life prediction of lithium-ion batteries by systematic sampling
Ansari, S., Ayob, A., Lipu, M. S. H., Hussain, A., & Saad, M. H. M. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2022.106050 · Cited by 65
Evaluation of Deep Learning in Detecting Unknown Network Attacks
Sabeel, U., Heydari, S. S., Harsh, M., Bendhaou, Y., Elgazzar, K., & El‐Khatib, K. (2019) · International Conference on Smart Applications, Communications and Networking (SmartNets) · doi:10.1109/smartnets48225.2019.9069788 · Cited by 68
Intelligent intrusion detection system in smart grid using computational intelligence and machine learning
Khan, S. A., Kifayat, K., Bashir, A. K., Gurtov, A., & Hassan, M. (2020) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.4062 · Cited by 64
Application of deep autoencoder as an one-class classifier for unsupervised network intrusion detection: a comparative evaluation
Vaiyapuri, T., & Binbusayyis, A. (2020) · PeerJ Computer Science · doi:10.7717/peerj-cs.327 · Cited by 69
ROULETTE: A neural attention multi-output model for explainable Network Intrusion Detection
Andresini, G., Appice, A., Caforio, F. P., Malerba, D., & Vessio, G. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.117144 · Cited by 69
E-SFD: Explainable Sensor Fault Detection in the ICS Anomaly Detection System
Hwang, C., & Lee, T. (2021) · IEEE Access · doi:10.1109/access.2021.3119573 · Cited by 62
Computational Intelligence Enabled Cybersecurity for the Internet of Things
Zhao, S., Li, S., Qi, L., & Xu, L. D. (2020) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2019.2941757 · Cited by 61
Comparative Analysis of Deep Convolutional Neural Network—Bidirectional Long Short-Term Memory and Machine Learning Methods in Intrusion Detection Systems
Udurume, M., Shakhov, V., & Koo, I. (2024) · Applied Sciences · doi:10.3390/app14166967 · Cited by 67
A new method of hybrid time window embedding with transformer-based traffic data classification in IoT-networked environment
Kozik, R., Pawlicki, M., & Choraś, M. (2021) · Pattern Analysis and Applications · doi:10.1007/s10044-021-00980-2 · Cited by 59
Machine Learning for Detecting Anomalies and Intrusions in Communication Networks
Li, Z., Rios, A. L. G., & Trajković, L. (2021) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2021.3078497 · Cited by 61
Anomalous node detection in attributed social networks using dual variational autoencoder with generative adversarial networks
Khan, W., Abidin, S., Arif, M., Ishrat, M., Haleem, M., Shaikh, A. A., Farooqui, N. A., & Faisal, S. M. (2023) · Data Science and Management · doi:10.1016/j.dsm.2023.10.005 · Cited by 59
Machine Learning based Anomaly Detection for 5G NetworksPreprint
Lam, J., & Abbas, H. (2020) · arXiv (Cornell University) · doi:10.13140/rg.2.2.17317.86247 · Cited by 70
iPhosS(Deep)-PseAAC: Identification of Phosphoserine Sites in Proteins Using Deep Learning on General Pseudo Amino Acid Compositions
Naseer, S., Hussain, W., Khan, Y. D., & Rasool, N. (2020) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2020.3040747 · Cited by 58
Self-citation
Machine Learning for Misuse-Based Network Intrusion Detection: Overview, Unified Evaluation and Feature Choice Comparison Framework
Jeune, L. L., Goedemé, T., & Mentens, N. (2021) · IEEE Access · doi:10.1109/access.2021.3075066 · Cited by 62
A Machine Learning Approach to Anomaly Detection Based on Traffic Monitoring for Secure Blockchain Networking
Kim, J., Nakashima, M., Fan, W., Wuthier, S., Zhou, X., Kim, I., & Chang, S. (2022) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2022.3173598 · Cited by 57
Deep Learning-Based Hybrid Intelligent Intrusion Detection System
Khan, M. A., & Kim, Y. (2021) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2021.015647 · Cited by 64
Network intrusion detection based on IE-DBN model
Jia, H., Liu, J., Zhang, M., He, X., & Sun, W. (2021) · Computer Communications · doi:10.1016/j.comcom.2021.07.016 · Cited by 54
Anomaly Detection based on Traffic Monitoring for Secure Blockchain Networking
Kim, J., Nakashima, M., Fan, W., Wuthier, S., Zhou, X., Kim, I., & Chang, S. (2021) · IEEE International Conference on Blockchain and Cryptocurrency (ICBC) · doi:10.1109/icbc51069.2021.9461119 · Cited by 53
Network intrusion detection system: A survey on artificial intelligence‐based techniques
Habeeb, M. S., & Babu, T. R. (2022) · Expert Systems · doi:10.1111/exsy.13066 · Cited by 60
An Exhaustive Research on the Application of Intrusion Detection Technology in Computer Network Security in Sensor Networks
Wang, Y., Juan, M., Sharma, A., Singh, P. K., Gaba, G. S., Masud, M., & Baz, M. (2021) · Journal of Sensors · doi:10.1155/2021/5558860 · Cited by 52
Deep learning approach for intrusion detection in IoT-multi cloud environment
Selvapandian, D., & Santhosh, R. (2021) · Automated Software Engineering · doi:10.1007/s10515-021-00298-7 · Cited by 50
Robust Unsupervised Anomaly Detection With Variational Autoencoder in Multivariate Time Series Data
Yokkampon, U., Mowshowitz, A., Chumkamon, S., & Hayashi, E. (2022) · IEEE Access · doi:10.1109/access.2022.3178592 · Cited by 51
Host-Based Intrusion Detection Model Using Siamese Network
Park, D., Kim, S. S., Kwon, H.-J., Shin, D., & Shin, D. (2021) · IEEE Access · doi:10.1109/access.2021.3082160 · Cited by 49
Abnormal event detection, identification and isolation in nuclear power plants using LSTM networks
Wang, M., Lin, T., Jhan, K.-C., & Wu, S. (2021) · Progress in Nuclear Energy · doi:10.1016/j.pnucene.2021.103928 · Cited by 49
Detection of Anomaly using Machine Learning: A Comprehensive Survey
Mane, D., Sangve, S. M., Upadhye, G. D., Kandhare, S., Mohole, S., Sonar, S., & Tupare, S. (2022) · International Journal of Emerging Technology and Advanced Engineering · doi:10.46338/ijetae1122_15 · Cited by 47
A Hierarchical Intrusion Detection Model Combining Multiple Deep Learning Models With Attention Mechanism
Xu, H., Sun, L., Fan, G., Li, W., & Kuang, G. (2023) · IEEE Access · doi:10.1109/access.2023.3290613 · Cited by 47
Exploiting the Auto-Encoder Residual Error for Intrusion Detection
Andresini, G., Appice, A., Mauro, N. D., Loglisci, C. A. A. M. D., & Malerba, D. (2019) · IEEE European Symposium on Security and Privacy Workshops (EuroS&PW) · doi:10.1109/eurospw.2019.00038 · Cited by 45
An Empirical Study on Unsupervised Network Anomaly Detection using Generative Adversarial Networks
Truong-Huu, T., Dheenadhayalan, N., Kundu, P. P., Ramnath, V., Jingyi, L., Teo, S. G., & Kadiyala, S. P. (2020) · doi:10.1145/3385003.3410924 · Cited by 45
Towards Real-Time Network Intrusion Detection With Image-Based Sequential Packets Representation
Ghadermazi, J., Shah, A., & Bastian, N. D. (2024) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2024.3403394 · Cited by 45
Unveiling machine learning strategies and considerations in intrusion detection systems: a comprehensive survey
Ali, A. H., Charfeddine, M., Ammar, B., Hamed, B. B., Albalwy, F., Alqarafi, A., & Hussain, A. (2024) · Frontiers in Computer Science · doi:10.3389/fcomp.2024.1387354 · Cited by 44
Computer Science & Information Technology
(2020) · doi:10.5121/csit.2020.1007 · Cited by 44
Continual Learning for Anomaly based Network Intrusion Detection
Amalapuram, S. K., Tadwai, A., Vinta, R., Channappayya, S. S., & Tamma, B. R. (2022) · International Conference on Communication Systems & Networks (COMSNETS) · doi:10.1109/comsnets53615.2022.9668482 · Cited by 43
Expert enhanced dynamic time warping based anomaly detection
Kloska, M., Grmanová, G., & Rozinajová, V. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120030 · Cited by 41
Deep Learning Entrusted to Fog Nodes (DLEFN) Based Smart Agriculture
Lee, K., Silva, B. N., & Han, K. (2020) · Applied Sciences · doi:10.3390/app10041544 · Cited by 40
Self-citation
Data Curation and Quality Evaluation for Machine Learning-Based Cyber Intrusion Detection
Tran, N., Chen, H., Bhuyan, J. N., & Ding, J. (2022) · IEEE Access · doi:10.1109/access.2022.3211313 · Cited by 45
Revolutionizing Structural Engineering: Applications of Machine Learning for Enhanced Performance and Safety
Chitkeshwar, A. K. (2024) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-024-10117-3 · Cited by 48

Authors: Naseer, S., Saleem, Y., Khalid, S., Bashir, M. K., Han, J., Iqbal, M. M., & Han, K.
Affiliations: University of Engineering and Technology Lahore (PK)
Bahria University (PK)
Kyungpook National University (KR)
University of Engineering and Technology Taxila (PK)
ORCID: Naseer, Sheraz
Saleem, Yasir
Khalid, Shehzad
Bashir, Muhammad Khawar
Han, Jihun
Iqbal, Muhammad Munwar
Han, Kijun
Venue: IEEE Access
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2018
Volume: 6
Pages: 48231-48246
ISSN: 2169-3536 (ISSN-L)
DOI: 10.1109/access.2018.2863036
OpenAlex: W2886020981
Cited by: 507
References: 44
Citations by year: 2026: 28 · 2025: 59 · 2024: 70 · 2023: 89 · 2022: 88 · 2021: 68 · 2020: 69 · 2019: 35 · 2018: 1
FWCI: 26.79
Type: journal article
Language: en
Open access: gold
doi.org
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/OAPA.html (vor)
Locations: IEEE Access (publishedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics: Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
Physical Sciences › Computer Science › Hardware and Architecture › Network Packet Processing and Optimization
Physical Sciences › Computer Science › Artificial Intelligence › Artificial Immune Systems Applications
Keywords: network anomaly detection, intrusion detection systems, deep neural network, convolutional neural networks, autoencoder, recurrent neural networks, KDD Cup 1999 dataset, network security

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Enhanced Network Anomaly Detection Based on Deep Neural Networks

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks