A multi-label network attack detection approach based on two-stage model fusion
References
[1]
Xie J., Li S., Zhang Y., Sun P., Xu H., Analysis and detection against network attacks in the overlapping phenomenon of behavior attribute, Comput Secur 121 (2022).
[2]
Moustafa N., Slay J., UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set), in: 2015 military communications and information systems conference (milCIS), IEEE, 2015, pp. 1–6.
[3]
Keyes D.S., Li B., Kaur G., Lashkari A.H., Gagnon F., Massicotte F., EntropLyzer: Android malware classification and characterization using entropy analysis of dynamic characteristics, in: 2021 reconciling data analytics, automation, privacy, and security: a big data challenge, RDAAPS, IEEE, 2021, pp. 1–12.
[4]
Rashid M., Kamruzzaman J., Imam T., Wibowo S., Gordon S., A tree-based stacking ensemble technique with feature selection for network intrusion detection, Appl Intell 52 (9) (2022) 9768–9781.
[5]
Rajapaksha S., Kalutarage H., Al-Kadri M.O., Petrovski A., Madzudzo G., Beyond vanilla: Improved autoencoder-based ensemble in-vehicle intrusion detection system, J. Inf. Secur. Appl. 77 (2023).
[6]
Singh K., Kaur L., Maini R., Ensemble voting based intrusion detection technique using negative selection algorithm., Int Arab J Inf Technol 20 (2) (2023) 151–158.
[7]
Agrawal S., Chowdhuri A., Sarkar S., Selvanambi R., Gadekallu T.R., et al., Temporal weighted averaging for asynchronous federated intrusion detection systems, Comput Intell Neurosci 2021 (2021).
[8]
Mushtaq E., Zameer A., Khan A., A two-stage stacked ensemble intrusion detection system using five base classifiers and MLP with optimal feature selection, Microprocess Microsyst 94 (2022).
[9]
Lazzarini R., Tianfield H., Charissis V., A stacking ensemble of deep learning models for IoT intrusion detection, Knowl-Based Syst 279 (2023).
[10]
Yao W., Hu L., Hou Y., Li X., A two-layer soft-voting ensemble learning model for network intrusion detection, in: 2022 52nd annual IEEE/iFIP international conference on dependable systems and networks workshops (DSN-w), IEEE, 2022, pp. 155–161.
[11]
Gautam S., Om H., Dixit K., A novel multilevel classifier hybrid model for intrusion detection using machine learning, Nat-Inspir Comput Smart Appl Des (2021) 249–266.
[12]
Zhang H., Zhang B., Huang L., Zhang Z., Huang H., An efficient two-stage network intrusion detection system in the Internet of Things, Information 14 (2) (2023) 77.
[13]
Hnamte V., Nhung-Nguyen H., Hussain J., Hwa-Kim Y., A novel two-stage deep learning model for network intrusion detection: LSTM-AE, IEEE Access 11 (2023) 37131–37148.
[14]
Zhang M.-L., Zhou Z.-H., A review on multi-label learning algorithms, IEEE Trans Knowl Data Eng 26 (8) (2013) 1819–1837.
[15]
Boutell M.R., Luo J., Shen X., Brown C.M., Learning multi-label scene classification, Pattern Recognit 37 (9) (2004) 1757–1771.
[16]
Fürnkranz J., Hüllermeier E., Loza Mencía E., Brinker K., Multilabel classification via calibrated label ranking, Mach Learn 73 (2008) 133–153.
[17]
Read J., Pfahringer B., Holmes G., Frank E., Classifier chains for multi-label classification, in: Machine learning and knowledge discovery in databases: European conference, ECML pKDD 2009, bled, Slovenia, September 7-11, 2009, proceedings, part II 20, Springer, 2009, pp. 254–269.
[18]
Read J., Pfahringer B., Holmes G., Frank E., Classifier chains for multi-label classification, Mach Learn 85 (2011) 333–359.
[19]
Tsoumakas G., Vlahavas I., Random k-labelsets: An ensemble method for multilabel classification, in: European conference on machine learning, Springer, 2007, pp. 406–417.
[20]
Zhang M.-L., Zhou Z.-H., ML-KNN: A lazy learning approach to multi-label learning, Pattern Recognit 40 (7) (2007) 2038–2048.
[21]
Clare A., King R.D., Knowledge discovery in multi-label phenotype data, in: European conference on principles of data mining and knowledge discovery, Springer, 2001, pp. 42–53.
[22]
Benites F., Sapozhnikova E., Haram: a hierarchical aram neural network for large-scale text classification, in: 2015 IEEE international conference on data mining workshop, ICDMW, IEEE, 2015, pp. 847–854.
[23]
Liang Z., Guo J., Qiu W., Huang Z., Li S., When graph convolution meets double attention: online privacy disclosure detection with multi-label text classification, Data Min Knowl Discov 38 (2024) 1171–1192.
[24]
Mehmood F., Shahzadi R., Ghafoor H., Asim M.N., Ghani M.U., Mahmood W., Dengel A., Enml: multi-label ensemble learning for urdu text classification, ACM Trans Asian Low-Resour Lang Inf Process 22 (9) (2023) 1–31.
[25]
Ameer I., Bölücü N., Siddiqui M.H.F., Can B., Sidorov G., Gelbukh A., Multi-label emotion classification in texts using transfer learning, Expert Syst Appl 213 (2023).
[26]
Ciobotaru A, Constantinescu MV, Dinu LP, Dumitrescu S. RED v2: enhancing red dataset for multi-label emotion detection. In: Proceedings of the thirteenth language resources and evaluation conference. 2022, p. 1392–9.
[27]
Hu P., Sun X., Sclaroff S., Saenko K., DualCoOp++: Fast and effective adaptation to multi-label recognition with limited annotations, IEEE Trans Pattern Anal Mach Intell 46 (5) (2023) 3450–3462.
[28]
Chai Y., Liu H., Xu J., Samtani S., Jiang Y., Liu H., A multi-label classification with an adversarial-based denoising autoencoder for medical image annotation, ACM Trans Manage Inf Syst 14 (2) (2023) 1–21.
[29]
Abdullayeva F., Suleymanzade S., Using transfer adaptation method for dynamic features expansion in multi-label deep neural network for recommender systems, Stat Optim Inf Comput 12 (2) (2024) 524–529.
[30]
Manoharan S., Senthilkumar R., Jayakumar S., Optimized multi-label convolutional neural network using modified genetic algorithm for popularity based personalized news recommendation system, Concurr Comput: Pract Exper 34 (19) (2022).
[31]
Du H., Huang D., Multi-attack detection: General defense strategy based on neural networks for CV-QKD, Photonics 9 (3) (2022) 177.
[32]
Li M., Zheng R., Liu L., Yang P., Extraction of threat actions from threat-related articles using multi-label machine learning classification method, in: 2019 2nd international conference on safety produce informatization, IICSPI, IEEE, 2019, pp. 428–431.
[33]
Hegazy H.I., Tag Eldien A.S., Tantawy M.M., Fouda M.M., TagElDien H.A., Real-time locational detection of stealthy false data injection attack in smart grid: Using multivariate-based multi-label classification approach, Energies 15 (14) (2022) 5312.
[34]
Han Y., Feng H., Li K., Zhao Q., False data injection attacks detection with modified temporal multi-graph convolutional network in smart grids, Comput Secur 124 (2023).
[35]
Rahali A, Lashkari AH, Kaur G, Taheri L, Gagnon F, Massicotte F. Didroid: Android malware classification and characterization using deep image learning. In: 2020 the 10th international conference on communication and network security. 2020, p. 70–82.
[36]
Zhang H., Li J.-L., Liu X.-M., Dong C., Multi-dimensional feature fusion and stacking ensemble mechanism for network intrusion detection, Future Gener Comput Syst 122 (2021) 130–143.
[37]
Resende P.A.A., Drummond A.C., A survey of random forest based methods for intrusion detection systems, ACM Comput Surv 51 (3) (2018) 1–36.
[38]
Godbole S., Sarawagi S., Discriminative methods for multi-labeled classification, in: Pacific-Asia conference on knowledge discovery and data mining, Springer, 2004, pp. 22–30.
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Published: 08 August 2024
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