Advanced search
Publication Cover
Connection Science Volume 35, 2023 - Issue 1
Submit an article Journal homepage
1,014
Views
0
CrossRef citations to date
0
Altmetric
Research Article

A multimodal hybrid parallel network intrusion detection model

Shuxin ShiCollege of Information Engineering, Shanghai Maritime University, Shanghai, People's Republic of China
,
Dezhi HanCollege of Information Engineering, Shanghai Maritime University, Shanghai, People's Republic of ChinaCorrespondence[email protected]
&
Mingming CuiCollege of Information Engineering, Shanghai Maritime University, Shanghai, People's Republic of China
Article: 2227780 | Received 16 Mar 2023, Accepted 15 Jun 2023, Published online: 16 Aug 2023

References

  • Aceto, G., Ciuonzo, D., Montieri, A., & Pescapé, A. (2019, December). MIMETIC: Mobile encrypted traffic classification using multimodal deep learning. Computer Networks, 165, 106944. https://doi.org/10.1016/j.comnet.2019.106944
  • Aceto, G., Ciuonzo, D., Montieri, A., & Pescapé, A. (2021, June). DISTILLER: Encrypted traffic classification via multimodal multitask deep learning. Journal of Network and Computer Applications, 183–184, 102985. https://doi.org/10.1016/j.jnca.2021.102985
  • Ahmim, A., Derdour, M., & Ferrag, M. A. (2018). An intrusion detection system based on combining probability predictions of a tree of classifiers. International Journal of Communication Systems, 31(9), e3547. https://doi.org/10.1002/dac.v31.9
  • Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Al-Amidie, M., & Farhan, L. (2021, March). Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions. Journal of Big Data, 8(1), 53. https://doi.org/10.1186/s40537-021-00444-8
  • Anthi, E., Williams, L., Słowińka, M., Theodorakopoulos, G., & Burnap, P. (2019, October). A supervised intrusion detection system for smart home IoT devices. IEEE Internet of Things Journal, 6(5), 9042–9053. https://doi.org/10.1109/JIoT.6488907
  • Bontemps, L., Cao, V. L., McDermott, J., & Le-Khac, N. A. (2016). Collective anomaly detection based on long short-term memory recurrent neural networks. In Future data and security engineering (pp. 141–152). Springer. Retrieved December 22, 2022, from https://doi.org/10.1007/978-3-319-48057-29.
  • Breiman, L. (2001, October). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
  • Buda, M., Maki, A., & Mazurowski, M. A. (2018, October). A systematic study of the class imbalance problem in convolutional neural networks. Neural Networks, 106, 249–259. https://doi.org/10.1016/j.neunet.2018.07.011
  • Cai, S., Han, D., Yin, X., Li, D., & Chang, C. C. (2022, December). A hybrid parallel deep learning model for efficient intrusion detection based on metric learning. Connection Science, 34(1), 551–577. https://doi.org/10.1080/09540091.2021.2024509
  • Chen, C., Han, D., & Chang, C. C. (2022, December). CAAN: Context-aware attention network for visual question answering. Pattern Recognition, 132, 108980. https://doi.org/10.1016/j.patcog.2022.108980
  • Dong, Y., Wang, R., & He, J. (2019, October). Real-time network intrusion detection system based on deep learning. In 2019 IEEE 10th international conference on software engineering and service science (ICSESS) (pp. 1–4). ISSN: 2327-0594.
  • Gao, N., Han, D., Weng, T. H., Xia, B., Li, D., Castiglione, A., & Li, K. C. (2022, October). Modeling and analysis of port supply chain system based on Fabric blockchain. Computers & Industrial Engineering, 172, 108527. https://doi.org/10.1016/j.cie.2022.108527
  • Guo, Z., & Han, D. (2023, January). Sparse co-attention visual question answering networks based on thresholds. Applied Intelligence, 53(1), 586–600. https://doi.org/10.1007/s10489-022-03559-4
  • Han, D., Pan, N., & Li, K. C. (2022, January). A traceable and revocable ciphertext-policy attribute-based encryption scheme based on privacy protection. IEEE Transactions on Dependable and Secure Computing, 19(1), 316–327. https://doi.org/10.1109/TDSC.2020.2977646
  • Han, D., Zhu, Y., Li, D., Liang, W., Souri, A., & Li, K. C. (2022, May). A blockchain-based auditable access control system for private data in service-centric IoT environments. IEEE Transactions on Industrial Informatics, 18(5), 3530–3540. https://doi.org/10.1109/TII.2021.3114621
  • He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In (pp. 770–778). Retrieved December 22, 2022, from https://openaccess.thecvf.com/contentcvpr2016/html/HeDeepResidualLearningCVPR2016paper.html.
  • Hnaif, A., Jaber, K., Alia, M., & Daghbosheh, M. (2021, January). Parallel scalable approximate matching algorithm for network intrusion detection systems. International Arab Journal of Information Technology, 18(1), 77–84. https://www.webofscience.com/wos/alldb/full-record/WOS:000607690000009.
  • Ioffe, S., & Szegedy, C. (2015, June). Batch normalization: Accelerating deep network training by reducing internal covariate shift. In Proceedings of the 32nd international conference on machine learning (pp. 448–456). PMLR. Retrieved December 25, 2022, from https://proceedings.mlr.press/v37/ioffe15.html (ISSN: 1938-7228).
  • Jan, S. U., Ahmed, S., Shakhov, V., & Koo, I. (2019). Toward a lightweight intrusion detection system for the internet of things. IEEE Access, 7, 42450–42471. https://doi.org/10.1109/Access.6287639
  • Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998, November). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278–2324. https://doi.org/10.1109/5.726791
  • Li, D., Han, D., Weng, T. H., Zheng, Z., Li, H., Liu, H., Castiglione, A., & Li, K. C. (2022a, May). Blockchain for federated learning toward secure distributed machine learning systems: A systemic survey. Soft Computing, 26(9), 4423–4440. https://doi.org/10.1007/s00500-021-06496-5
  • Li, D., Han, D., Weng, T. H., Zheng, Z., Li, H., Liu, H., Castiglione, A., & Li, K. C. (2022b, April). MOOCsChain: A blockchain-based secure storage and sharing scheme for MOOCs learning. Computer Standards & Interfaces, 81, 103597. https://doi.org/10.1016/j.csi.2021.103597
  • Li, H., Han, D., & Tang, M. (2022, March). A privacy-preserving storage scheme for logistics data with assistance of blockchain. IEEE Internet of Things Journal, 9(6), 4704–4720. https://doi.org/10.1109/JIOT.2021.3107846
  • Li, J., Han, D., Wu, Z., Wang, J., Li, K. C., & Castiglione, A. (2023, May). A novel system for medical equipment supply chain traceability based on alliance chain and attribute and role access control. Future Generation Computer Systems, 142, 195–211. https://doi.org/10.1016/j.future.2022.12.037
  • Li, J., Zhao, Z., Li, R., & Zhang, H. (2019, April). AI-based two-stage intrusion detection for software defined IoT networks. IEEE Internet of Things Journal, 6(2), 2093–2102. https://doi.org/10.1109/JIoT.6488907
  • Liu, G., & Zhang, J. (2020, May). CNID: Research of network intrusion detection based on convolutional neural network. Discrete Dynamics in Nature and Society, 2020, 1–11. https://www.hindawi.com/journals/ddns/2020/4705982/.
  • Long, J., Shelhamer, E., & Darrell, T. (2015). Fully convolutional networks for semantic segmentation. In (pp. 3431–3440). Retrieved December 25, 2022, from https://openaccess.thecvf.com/contentcvpr2015/html/LongFullyConvolutionalNetworks2015CVPRpaper.
  • Rawat, W., & Wang, Z. (2017, September). Deep convolutional neural networks for image classification: A comprehensive review. Neural Computation, 29(9), 2352–2449. https://doi.org/10.1162/neco_a_00990
  • Roy, S. S., Mallik, A., Gulati, R., Obaidat, M. S., & Krishna, P. V. (2017). A deep learning based artificial neural network approach for intrusion detection. In D. Giri, R. N. Mohapatra, H. Begehr, & M. S. Obaidat (Eds.), Mathematics and computing (Vol. 655, pp. 44–53). Springer Singapore. Retrieved December 16, 2022, from https://doi.org/10.1007/978-981-10-4642-15.
  • Sharafaldin, I., Habibi Lashkari, A., & Ghorbani, A. A. (2018). Toward generating a new intrusion detection dataset and intrusion traffic characterization. In Proceedings of the 4th international conference on information systems security and privacy (pp. 108–116). SCITEPRESS - Science and Technology Publications. Retrieved December 22, 2022, from http://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/0006639801080116.
  • Shen, X., Han, D., Guo, Z., Chen, C., Hua, J., & Luo, G. (2022, December). Local self-attention in transformer for visual question answering. Applied Intelligence, 53(13), 16706–16723. https://doi.org/10.1007/s10489-022-04355-w.
  • Shiravi, A., Shiravi, H., Tavallaee, M., & Ghorbani, A. A. (2012, May). Toward developing a systematic approach to generate benchmark datasets for intrusion detection. Computers & Security, 31(3), 357–374. https://doi.org/10.1016/j.cose.2011.12.012
  • Su, T., Sun, H., Zhu, J., Wang, S., & Li, Y. (2020). BAT: Deep learning methods on network intrusion detection using NSL-KDD dataset. IEEE Access, 8, 29575–29585. https://doi.org/10.1109/Access.6287639
  • Vinayakumar, R., Soman, K. P., & Poornachandran, P. (2017, September). Applying convolutional neural network for network intrusion detection. In 2017 international conference on advances in computing, communications and informatics (ICACCI) (pp. 1222–1228).
  • Wang, X., Chen, S., & Su, J. (2020, July). App-Net: A hybrid neural network for encrypted mobile traffic classification. In IEEE INFOCOM 2020 - IEEE conference on computer communications workshops (INFOCOM WKSHPS) (pp. 424–429). IEEE. Retrieved May 08, 2023, from https://ieeexplore.ieee.org/document/9162891/.
  • Wang, Z., Han, D., Li, M., Liu, H., & Cui, M. (2022, December). The abnormal traffic detection scheme based on PCA and SSH. Connection Science, 34(1), 1201–1220. https://doi.org/10.1080/09540091.2022.2051434
  • Wu, P. f., & Shen, H. j. (2012, June). The research and amelioration of pattern-matching algorithm in intrusion detection system. In 2012 IEEE 14th international conference on high performance computing and communication & 2012 IEEE 9th international conference on embedded software and systems (pp. 1712–1715).
  • Yang, Z., Liu, X., Li, T., Wu, D., Wang, J., Zhao, Y., & Han, H. (2022). A systematic literature review of methods and datasets for anomaly-based network intrusion detection. Computers & Security, 116, 102675. https://doi.org/10.1016/j.cose.2022.102675
  • Yin, C., Zhu, Y., Fei, J., & He, X. (2017). A deep learning approach for intrusion detection using recurrent neural networks. IEEE Access, 5, 21954–21961. https://doi.org/10.1109/ACCESS.2017.2762418
  • Zhang, H. (2009, January). Design of intrusion detection system based on a new pattern matching algorithm. In 2009 international conference on computer engineering and technology (pp. 545–548). IEEE. Retrieved Decemebr 20, 2022, from https://ieeexplore.ieee.org/document/4769526/.
  • Zhang, Y., Chen, X., Guo, D., Song, M., Teng, Y., & Wang, X. (2019). PCCN: Parallel cross convolutional neural network for abnormal network traffic flows detection in multi-class imbalanced network traffic flows. IEEE Access, 7, 119904–119916. https://doi.org/10.1109/Access.6287639

Related research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.