Mobile QR Code QR CODE

2025

Reject Ratio

81.5%

References

1 
S. M. Kasongo , Y. Sun , Performance analysis of intrusion detection systems using a feature selection method on the UNSW-NB15 dataset, Journal of Big Data, Vol. 7, pp. 105, 2020DOI
2 
S. García , M. Grill , J. Stiborek , A. Zunino , An empirical comparison of botnet detection methods, Computers & Security, Vol. 45, pp. 100-123, 2014DOI
3 
J. Yu , X. Gao , B. Li , F. Zhai , J. Lu , B. Xue , S. Fu , C. Xiao , A filter-augmented auto-encoder with learnable normalization for robust multivariate time series anomaly detection, Neural Networks, Vol. 170, pp. 478-493, 2024DOI
4 
L. Yu , Q. Lu , Y. Xue , DTAAD: dual TCN-attention networks for anomaly detection in multivariate time series data, Knowledge-Based Systems, Vol. 295, No. 111849, 2024DOI
5 
S. Liu , B. Zhou , Q. Ding , B. Hooi , Z. Zhang , H. Shen , X. Cheng , Time series anomaly detection with adversarial reconstruction networks, IEEE Transactions on Knowledge and Data Engineering, Vol. 35, No. 4, pp. 4293-4306, 2022DOI
6 
M. Munir , S. A. Siddiqui , A. Dengel , S. Ahmed , DeepAnT: A deep learning approach for unsupervised anomaly detection in time series, IEEE Access, Vol. 7, pp. 1991-2005, 2019DOI
7 
D. L. Marino , C. S. Wickramasinghe , C. Rieger , M. Manic , Self-supervised and interpretable anomaly detection using network transformers, IEEE Transactions on Industrial Informatics, Vol. 21, No. 5, pp. 4252-4261, 2025DOI
8 
L. Xu , K. Xu , Y. Qin , Y. Li , X. Huang , Z. Lin , X. Ji , TGAN-AD: transformer-based GAN for anomaly detection of time series data, Applied Sciences, Vol. 12, No. 16, 2022DOI
9 
G. G. González , P. Casas , E. Martínez , A. Fernández , Towards foundation auto-encoders for time-series anomaly detection, arXiv preprint, 2025DOI
10 
B. Golchin , B. Rekabdar , Anomaly detection in time series data using reinforcement learning, variational autoencoder, and active learning, Proc. of 2024 Conference on AI, Science, Engineering, and Technology (AIxSET), 2025DOI
11 
S. D. D. Anton , S. Sinha , H. D. Schotten , Anomaly-based intrusion detection in industrial data with SVM and random forests, Proc. of the International Conference on Software, Telecommunications and Computer Networks, 2019DOI
12 
S. D. Anton , L. Ahrens , D. Fraunholz , H. D. Schotten , Time is of the essence: machine learning-based intrusion detection in industrial time series data, Extended version of a publication in the 2018 IEEE International Conference on Data Mining Workshops (ICDMW), pp. 1-6, 2018DOI
13 
K. Tscharke , M. Wendlinger , A. Ahouzi , P. Bhardwaj , K. Amoi-Taleghani , M. Schrödl-Baumann , P. Debus , Quantum autoencoder for multivariate time series anomaly detection, Proc. of 2025 IEEE International Conference on Quantum Computing and Engineering (QCE), 2025DOI
14 
Z. Z. Darban , G. I. Webb , S. Pan , C. Aggarwal , M. Salehi , Deep learning for time series anomaly detection: A survey, ACM Computing Surveys, Vol. 57, No. 1, pp. 1-42, 2025DOI
15 
Y. Qin , D. Song , H. Chen , W. Cheng , G. Jiang , G. Cottrell , A dual-stage attention-based recurrent neural network for time series prediction, arXiv preprint arXiv:1704.02971, 2017DOI
16 
S. Hochreiter , J. Schmidhuber , Long short-term memory, Neural Computation, Vol. 9, No. 8, pp. 1735-1780, 1997DOI
17 
X. Xu , H. Wang , Y. Liang , P. S. Yu , Y. Zhao , K. Shu , Can multimodal LLMs perform time series anomaly detection?, Proc. of the ACM Web Conference, pp. 5392-5403, 2026DOI
18 
V. Chandola , A. Banerjee , V. Kumar , Anomaly detection: A survey, ACM Computing Surveys, Vol. 41, No. 3, pp. 1-58, 2009DOI
19 
K. Haukat , T. M. Alam , S. Luo , S. Shabbir , I. Hameed , J. Li , S. Abbas , U. Javed , , Advances in Information and Communication, Vol. 1363, 2021DOI
20 
G. Ciaburro , G. Iannace , Machine learning-based algorithms to knowledge extraction from time series data: A review, Data, Vol. 6, No. 55, 2021DOI
21 
F. Wang , Y. Jiang , R. Zhang , A. Wei , J. Xie , X. Pang , A survey of deep anomaly detection in multivariate time series: taxonomy, applications, and directions, Sensors, Vol. 25, No. 190, 2025DOI
22 
N. Moustafa , J. Slay , UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set), Proceedings of the 2015 Military Communications and Information Systems Conference (MilCIS), pp. 1-6, 2015DOI
23 
M. Tavallaee , E. Bagheri , W. Lu , A. A. Ghorbani , A detailed analysis of the KDD CUP 99 data set, Proc. of the 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications, pp. 1-6, 2009DOI
24 
I. Sharafaldin , A. H. Lashkari , A. A. Ghorbani , Toward generating a new intrusion detection dataset and intrusion traffic characterization, Proc. of the International Conference on Information Systems Security and Privacy (ICISSP), Vol. 1, pp. 108-116, 2018DOI
25 
S. Bai , J. Z. Kolter , V. Koltun , An empirical evaluation of generic convolutional and recurrent networks for sequence modeling, arXiv preprint arXiv:1803.01271, 2018DOI
26 
B. Radford , L. Apolonio , A. Trias , J. Simpson , Network traffic anomaly detection using recurrent neural networks, arXiv preprint arXiv:1803.10769, 2018DOI
27 
B. Zhou , S. Liu , B. Hooi , X. Cheng , J. Ye , BeatGAN: anomalous rhythm detection using adversarially generated time series, Proc. of the 28th International Joint Conference on Artificial Intelligence (IJCAI), pp. 4433-4439, 2019DOI
28 
I. Sharafaldin , A. Gharib , A. H. Lashkari , A. A. Ghorbani , Towards a reliable intrusion detection benchmark dataset, Software Networking, Vol. 2018, No. 1, pp. 177-200, 2018DOI
29 
A. Thakkar , R. Lohiya , A review on machine learning and deep learning perspectives of IDS for IoT: recent updates, security issues, and challenges, Archives of Computational Methods in Engineering, Vol. 28, No. 4, pp. 3211-243, 2021DOI
30 
M. A. Umar , Z. Chen , Y. Liu , Network intrusion detection using wrapper-based decision tree for feature selection, Proc. of the 2020 International Conference on Internet Computing for Science and Engineering, pp. 5-13, 2020DOI
31 
C. Khammassi , S. Krichen , A GA-LR wrapper approach for feature selection in network intrusion detection, Computers Security, Vol. 70, pp. 255-277, 2017DOI
32 
S. Farhat , M. Abdelkader , A. Meddeb-Makhlouf , F. Zarai , Evaluation of DoS/DDoS attack detection with ML techniques on CIC-IDS2017 dataset, Proc. of the International Conference on Information Systems Security and Privacy (ICISSP), pp. 287-295, 2023DOI
33 
P. Wu , H. Guo , N. Moustafa , Pelican: A deep residual network for network intrusion detection, Proc. of the 2020 IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W), pp. 55-62, 2020DOI
34 
A. D. Vibhute , M. Khan , C. H. Patil , S. V. Gaikwad , A. V. Mane , K. K. Patel , Network anomaly detection and performance evaluation of convolutional neural networks on UNSW-NB15 dataset, Procedia Computer Science, Vol. 235, pp. 2227-2236, 2024DOI
35 
K. Psychogyios , A. Papadakis , S. Bourou , N. Nikolaou , A. Maniatis , T. Zahariadis , Deep learning for intrusion detection systems (IDSs) in time series data, Future Internet, Vol. 16, No. 3, 2024DOI
36 
A. Corsini , S. J. Yang , G. Apruzzese , On the evaluation of sequential machine learning for network intrusion detection, Proc. of the 16th International Conference on Availability, Reliability and Security (ARES), pp. 1-10, 2021DOI
37 
Z. Xu , Y. Liu , Robust anomaly detection in network traffic: evaluating machine learning models on CICIDS2017, Proc. of 2025 10th International Conference on Electronic Technology and Information Science (ICETIS), 2025DOI
38 
M. Jouhari , H. Benaddi , K. Ibrahimi , Efficient intrusion detection: combining X2 feature selection with CNN-BiLSTM on the UNSW-NB15 dataset, Proc. of the 2024 11th International Conference on Wireless Networks and Mobile Communications (WINCOM), pp. 1-6, 2024DOI
39 
Z. Liu , D. Ye , C. Yang , Y. Ding , Y. Liu , L. Tang , C. Chen , Simplicity over complexity: an ARN-based intrusion detection method for industrial control network, arXiv preprint arXiv:2412.14669, 2024DOI
40 
O. Belarbi , A. Khan , P. Carnelli , T. Spyridopoulos , An intrusion detection system based on deep belief networks, Proc. of 4th International Conference on Science of Cyber Security, pp. 377-392, 2022DOI
41 
B. Cao , C. Li , Y. Song , Y. Qin , C. Chen , Network intrusion detection model based on CNN and GRU, Applied Sciences, Vol. 12, pp. 4184, 2022DOI
42 
M. Khan , A. Rahman , S. Lee , Improving intrusion detection with hybrid deep learning models: A study on CIC-IDS2017, UNSW-NB15, and KDD CUP 99, Journal of Information Systems Engineering and Management, Vol. 10, No. 11s, pp. 1-12, 2025DOI
43 
A. S. BBarkah , S. R. Selamat , Z. Z. Abidin , R. Wahyudi , Data generative model to detect the anomalies for IDS imbalance CICIDS2017 dataset, TEM Journal, Vol. 12, No. 1, pp. 1-7, 2023DOI
44 
M. Al-Ajlan , M. Ykhlef , A review of generative adversarial networks for intrusion detection systems: advances, challenges, and future directions, Computers, 2024DOI
45 
H. Gwon , C. Lee , R. Keum , H. Choi , Network intrusion detection based on LSTM and feature embedding, arXiv preprint, 2019DOI
46 
M. Jouhari , M. Guizani , Lightweight CNN-BiLSTM based intrusion detection systems for resource-constrained IoT devices, Proc. of the 2024 International Wireless Communications and Mobile Computing Conference (IWCMC), pp. 1558-1563, 2024DOI
47 
S. Lotfi , M. Modirrousta , S. Shashaani , M. A. Shoorehdeli , Network intrusion detection with limited labeled data using self-supervision, arXiv preprint, 2022DOI
48 
M. Injadat , A. Moubayed , A. B. Nassif , A. Shami , Multi-stage optimized machine learning framework for network intrusion detection, IEEE Transactions on Network and Service Management, Vol. 18, No. 2, pp. 1803-1816, 2020DOI
49 
Y. Yin , J. Jang-Jaccard , W. Xu , A. Singh , J. Zhu , F. Sabrina , J. Kwak , IGRF-RFE: a hybrid feature selection method for MLP-based network intrusion detection on UNSW-NB15 dataset, Journal of Big Data, Vol. 10, No. 1, 2023DOI
50 
B. Tafreshian , S. Zhang , A defensive framework against adversarial attacks on machine learning-based network intrusion detection systems, Proc. of the 2024 IEEE 23rd International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), pp. 2436-2441, 2024DOI
51 
T. T. Huynh , T. Nguyen Hoang , Effective multi-stage training model for edge computing devices in intrusion detection, International Journal of Computer Networks Communications, Vol. 16, 2024DOI
52 
C. S. Sampath , P. Anuradha , Intrusion detection using machine learning: A random forest-based approach, International Journal for Multidisciplinary Research, Vol. 5, No. 3, pp. 1-6, 2023DOI
53 
N. Fathima , A. Pramod , Y. Srivastava , A. M. Thomas , Two-stage deep stacked autoencoder with shallow learning for network intrusion detection system, arXiv preprint, 2021DOI
54 
R. Mohammad , F. Saeed , A. A. Almazroi , F. S. Alsubaei , A. A. Almazroi , Enhancing intrusion detection systems using a deep learning and data augmentation approach, Systems, Vol. 12, No. 3, 2024DOI
55 
X. Zhao , K. W. Fok , V. L. Thing , Enhancing network intrusion detection performance using generative adversarial networks, Computers Security, Vol. 145, 2024DOI
56 
M. Umer , M. Tahir , M. Sardaraz , M. Sharif , H. Elmannai , A. D. Algarni , Network intrusion detection model using wrapper based feature selection and multi head attention transformers, Scientific Reports, Vol. 15, No. 1, 2025DOI
57 
F. S. Alsubaei , Smart deep learning model for enhanced IoT intrusion detection, Scientific Reports, Vol. 15, No. 1, 2025DOI
58 
H. Hindy , R. Atkinson , C. Tachtatzis , J. N. Colin , E. Bayne , X. Bellekens , Utilising deep learning techniques for effective zero-day attack detection, Electronics, Vol. 9, No. 10, 2020DOI
59 
P. R. Agbedanu , R. Musabe , J. Rwigema , I. Gatare , Y. Pavlidis , IPCA-SAMKNN: A novel network IDS for resource constrained devices, Proc. of the 2022 2nd International Seminar on Machine Learning, Optimization, and Data Science (ISMODE), pp. 540-545, 2022DOI
60 
M. Farhan , H. Waheed Ud Din , S. Ullah , M. S. Hussain , M. A. Khan , T. Mazhar , Network-based intrusion detection using deep learning technique, Scientific Reports, Vol. 15, No. 1, 2025DOI
61 
Y. Yang , X. Liu , D. Wang , Q. Sui , C. Yang , H. Li , A CE-GAN-based approach to address data imbalance in network intrusion detection systems, Scientific Reports, Vol. 15, No. 1, 2025DOI
62 
M. Tayebi , S. El Kafhali , Performance analysis of recurrent neural networks for intrusion detection systems in Industrial Internet of Things, Franklin Open, Vol. 12, 2025DOI
63 
S. Khanam , I. Ahmedy , M. Y. I. Idris , M. H. Jaward , Towards an effective intrusion detection model using focal loss variational autoencoder for internet of things (IoT), Sensors, Vol. 22, No. 15, 2022DOI
64 
C. Haripriya , M. P. Jagadeesh , An efficient autoencoder-based deep learning technique to detect network intrusions, International Transaction Journal of Engineering, Management, Applied Sciences Technologies, Vol. 13, No. 7, pp. 1-10, 2022DOI
65 
M. Gourceyraud , R. B. Salem , C. Neal , F. Cuppens , N. B. Cuppens , Federated intrusion detection system based on unsupervised machine learning, arXiv preprint, 2025DOI
66 
H. Chen , G.-R. You , Y.-R. Shiue , Hybrid intrusion detection system based on data resampling and deep learning, International Journal of Advanced Computer Science and Applications, Vol. 15, No. 2, 2024DOI
67 
W. Choukri , H. Lamaazi , N. Benamar , Abnormal network traffic detection using deep learning models in IoT environment, Proc. of the 2021 3rd IEEE Middle East and North Africa Communications Conference (MENACOMM), pp. 98-103, 2021DOI
68 
T. Sharma , S. Gandage , Network traffic classification using long-short term memory algorithm on UNSW-NB15 and KDD-CUP99 dataset, Mathematical Statistician and Engineering Applications, Vol. 71, No. 4, pp. 10166-10181, 2022DOI
69 
L. Xu , M. Skoularidou , A. Cuesta-Infante , K. Veeramachaneni , Modeling tabular data using conditional GAN, Proc. of the 33rd International Conference on Neural Information Processing Systems (NeurIPS), pp. 7335-7345, 2019DOI
70 
X. Zhao , K. W. Fok , V. L. L. Thing , Enhancing network intrusion detection performance using generative adversarial networks, Computers Security, Vol. 145, pp. 104005, 2024DOI
71 
J. Koumar , K. Hynek , T. Cejka , P. Šiška , CESNET-TimeSeries24: time series dataset for network traffic anomaly detection and forecasting, Scientific Data, Vol. 12, No. 1, pp. 338, 2025DOI
72 
J. Krupski , M. Iwanowski , W. Graniszewski , Extraction of minimal set of traffic features using ensemble of classifiers and rank aggregation for network intrusion detection systems, Applied Sciences, Vol. 14, No. 16, pp. 6995, 2024DOI
73 
S.-M. Tseng , Y.-Q. Wang , Y.-C. Wang , Multi-class intrusion detection based on transformer for IoT networks using CIC-IoT-2023 dataset, Future Internet, Vol. 16, No. 8, pp. 284, 2024DOI
74 
W.-S. Park , G.-N. Kim , S. Lee , Intrusion detection system based on packet payload analysis using transformer, Journal of The Korea Society of Computer and Information, Vol. 28, No. 11, pp. 81-87, 2023DOI
75 
X. Tan , J. Cheng , H. Li , Y. Yang , Contrastive learning for network intrusion detection: A comprehensive survey, Proceedings of the 2024 2nd International Conference on Computing, Internet of Things and Smart City (CIoTSC), pp. 160-166, 2025DOI
76 
M. A. Rahman , A survey on security and privacy of multimodal LLMs: connected healthcare perspective, Proc. of the 2023 IEEE Globecom Workshops (GC Wkshps), pp. 1807-1812, 2023DOI