[1] AHMAD Z, SHAHID KHAN A, WAI SHIANG C, et al. Network intrusion detection system:a systematic study of machine learning and deep learning approaches[J]. Transactions on Emerging Telecommunications Technologies, 2021, 32(1):e4150. [2] 刘奇旭,王君楠,尹捷,等.对抗机器学习在网络入侵检测领域的应用[J].通信学报, 2021, 42(11):1-12. LIU Q X, WANG J N, YIN J, et al. Application of adversarial machine learning in network intrusion detection[J]. Journal on Communications, 2021, 42(11):1-12.(in Chinese) [3] ROESCH M. Snort:lightweight intrusion detection for networks[EB/OL].[2023-06-20].https://facultystaff.richmond.edu/~dszajda/classes/cs334/papers/snort.pdf. [4] LIU G Y, ZHAO H Q, FAN F, et al. An enhanced intrusion detection model based on improved kNN in WSNs[J]. Sensors, 2022, 22(4):1407. [5] 石磊,张吉涛,高宇飞,等.基于Transformer与BiLSTM的网络流量入侵检测[J].计算机工程, 2023, 49(3):29-36, 57. SHI L, ZHANG J T, GAO Y F, et al. Intrusion detection of network traffic based on Transformer and BiLSTM[J]. Computer Engineering, 2023, 49(3):29-36, 57.(in Chinese) [6] 麻文刚,张亚东,郭进.基于LSTM与改进残差网络优化的异常流量检测方法[J].通信学报, 2021, 42(5):23-40. MA W G, ZHANG Y D, GUO J. Abnormal traffic detection method based on LSTM and improved residual neural network optimization[J]. Journal on Communications, 2021, 42(5):23-40.(in Chinese) [7] KANUMALLI S S, LAVANYA K, RAJESWARI A, et al. A scalable network intrusion detection system using Bi-LSTM and CNN[C]//Proceedings of the 3rd International Conference on Artificial Intelligence and Smart Energy. Washington D. C., USA:IEEE Press, 2023:1-6. [8] 席亮,王瑞东,樊好义,等.基于样本关联感知的无监督深度异常检测模型[J].计算机学报, 2021, 44(11):2317-2331. XI L, WANG R D, FAN H Y, et al. Sample-correlation-aware unsupervised deep anomaly detection model[J]. Chinese Journal of Computers, 2021, 44(11):2317-2331.(in Chinese) [9] SHI Y Q, SHEN H. Unsupervised anomaly detection for network traffic using artificial immune network[J]. Neural Computing and Applications, 2022, 34(15):13007-13027. [10] SINGH A, JANG-JACCARD J. Autoencoder-based unsupervised intrusion detection using multi-scale convolutional recurrent networks[EB/OL].[2023-06-20]. http://arxiv.org/abs/2204.03779. [11] XU W, JANG-JACCARD J, LIU T, et al. Training a bidirectional GAN-based one-class classifier for network intrusion detection[EB/OL].[2023-06-20]. http://arxiv.org/abs/2202.01332. [12] BINBUSAYYIS A, VAIYAPURI T. Unsupervised deep learning approach for network intrusion detection combining convolutional autoencoder and one-class SVM[J]. Applied Intelligence, 2021, 51(10):7094-7108. [13] ZHOU Y J, SONG X C, ZHANG Y R, et al. Feature encoding with autoencoders for weakly supervised anomaly detection[J]. IEEE Transactions on Neural Networks and Learning Systems, 2022, 33(6):2454-2465. [14] ILIYASU A S, DENG H F. N-GAN:a novel anomaly-based network intrusion detection with generative adversarial networks[J]. International Journal of Information Technology, 2022, 14(7):3365-3375. [15] HOU Y B, TEO S G, CHEN Z H, et al. Handling labeled data insufficiency:semi-supervised learning with self-training mixup decision tree for classification of network attacking traffic[J/OL]. IEEE Transactions on Dependable and Secure Computing:1-14[2023-06-20].https://ieeexplore.ieee.org/document/9847046. [16] AOUEDI O, PIAMRAT K, MULLER G, et al. FLUIDS:federated learning with semi-supervised approach for intrusion detection system[C]//Proceedings of the 19th Annual Consumer Communications&Networking Conference. Washington D. C., USA:IEEE Press, 2022:523-524. [17] ZHANG Y, NIU J, HE G J, et al. Network intrusion detection based on active semi-supervised learning[C]//Proceedings of the 51st Annual International Conference on Dependable Systems and Networks Workshops (DSN-W). Washington D. C., USA:IEEE Press, 2021:129-135. [18] MBONA I, ELOFF J H P. Detecting zero-day intrusion attacks using semi-supervised machine learning approaches[J]. IEEE Access, 2022, 10:69822-69838. [19] HAN X, CHEN X H, LIU L P. GAN ensemble for anomaly detection[C]//Proceedings of the AAAI Conference on Artificial Intelligence.[S.l.]:AAAI Press, 2021:4090-4097. [20] DU B W, SUN X X, YE J C, et al. GAN-based anomaly detection for multivariate time series using polluted training set[J]. IEEE Transactions on Knowledge and Data Engineering, 2023, 35(12):12208-12219. [21] GRAVES A, WAYNE G, DANIHELKA I. Neural Turing machines[EB/OL].[2023-06-20]. https://arxiv.org/pdf/1410.5401.pdf. [22] GONG D, LIU L Q, LE V, et al. Memorizing normality to detect anomaly:memory-augmented deep autoencoder for unsupervised anomaly detection[C]//Proceedings of the International Conference on Computer Vision. Washington D. C., USA:IEEE Press, 2019:1705-1714. [23] LU H M, WANG T, XU X, et al. Cognitive memory-guided autoencoder for effective intrusion detection in Internet of Things[J]. IEEE Transactions on Industrial Informatics, 2022, 18(5):3358-3366. [24] ZHONG Y, CHEN W Q, WANG Z L, et al. HELAD:a novel network anomaly detection model based on heterogeneous ensemble learning[J]. Computer Networks, 2020, 169:107049. [25] RAE J W, HUNT J J, HARLEY T, et al. Scaling memory-augmented neural networks with sparse reads and writes[EB/OL].[2023-06-20]. https://arxiv.org/pdf/1610.09027.pdf. [26] SANTORO A, BARTUNOV S, BOTVINICK M, et al. One-shot learning with memory-augmented neural networks[EB/OL].[2023-06-20]. https://arxiv.org/pdf/1605.06065.pdf. [27] FONTUGNE R, BORGNAT P, ABRY P, et al. MAWILab:combining diverse anomaly detectors for automated anomaly labeling and performance benchmarking[C]//Proceedings of the 6th International Conference. New York, USA:ACM Press, 2010:1-12. [28] SHIRAVI A, SHIRAVI H, TAVALLAEE M, et al. Toward developing a systematic approach to generate benchmark datasets for intrusion detection[J]. Computers and Security, 2012, 31(3):357-374. [29] SHARAFALDIN I, HABIBI LASHKARI A, GHORBANI A A. Toward generating a new intrusion detection dataset and intrusion traffic characterization[EB/OL].[2023-06-20]. http://www.cs.unb.ca/research-expo/expos/2018/submissions/20180403-14-56-isharafa-at-unb.ca-toward_generating_a_new_intrusion_detection_dataset_and_intrusion_traffic_characterization.pdf. [30] LASKAR M T R, HUANG J X, SMETANA V, et al. Extending isolation forest for anomaly detection in big data via K-means[J]. ACM Transactions on Cyber-Physical Systems, 2021,5(4):41. [31] SAHU S K, JENA S K. A multiclass SVM classification approach for intrusion detection[C]//Proceedings International Conference on Distributed Computing and Internet Technology. Berlin, Germany:Springer, 2016:175-181. [32] ZONG B, SONG Q, MIN M R, et al. Deep autoencoding Gaussian mixture model for unsupervised anomaly detection[C]//Proceedings of International Conference on Learning Representations.[S.l.]:AAAI Press, 2018:1-10. [33] BELARBI O, KHAN A, CARNELLI P, et al. An intrusion detection system based on deep belief networks[C]//Proceedings of International Conference on Science of Cyber Security. Berlin, Germany:Springer, 2022:377-392. [34] WU Z H, ZHANG H, WANG P H, et al. RTIDS:a robust transformer-based approach for intrusion detection system[J]. IEEE Access, 2022, 10:64375-64387. |