Abstract
In recent years, the Internet of Things (IoT) has received a lot of attention. It has been used in many applications such as the control industry, industrial plants, and medicine. In this regard, a fundamental necessity is to implement security in IoT. To this end, Network intrusion detection systems (NIDSs) have been recently in the detection of network attacks and threats. Currently, these systems use a variety of deep learning (DL) models such as the convolutional neural networks to improve the detection of attacks. However, almost all current DL-based NIDSs are made up of many layers, and therefore, they need a lot of processing resources because of their high number of parameters. On the other hand, due to the lack of processing resources, such inefficient DL models are unusable in IoT devices. This paper presents a very accurate NIDS that is named DFE, and it uses a very lightweight and efficient neural network based on the idea of deep feature extraction. In this model, the input vector of the network is permuted in a 3D space, and its individual values are brought close together. This allows the model to extract highly discriminative features using a small number of layers without the need to use large 2D or 3D convolution filters. As a result, the network can achieve an accurate classification using a significantly small number of required calculations. This makes the DFE ideal for real-time intrusion detection by IoT devices with limited processing capabilities. The efficacy of the DFE has been evaluated using three popular public datasets named UNSW-NB15, CICIDS2017, and KDDCup99, and the results show the superiority of the proposed model over the state-of-the-art algorithms
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Fan J, Zhang Y, Wen W, Gu S, Lu X, Guo X (2021) The future of Internet of Things in agriculture: plant high-throughput phenotypic platform. J Clean Prod 280:123651
Philip NY, Rodrigues JJ, Wang H, Fong SJ, Chen J (2021) Internet of Things for in-home health monitoring systems: current advances, challenges and future directions. IEEE J Sel Areas Commun 39(2):300–310
Oniani S, Marques G, Barnovi S, Pires IM, Bhoi AK (2021) Artificial intelligence for internet of things and enhanced medical systems. In: Bhoi Akash Kumar, Mallick Pradeep Kumar, Liu Chuan-Ming, Balas Valentina E (eds) Bio-inspired Neurocomputing. Springer Singapore, Singapore, pp 43–59. https://doi.org/10.1007/978-981-15-5495-7_3
Gopikumar S, Raja S, Robinson YH, Shanmuganathan V, Chang H, Rho S (2021) A method of landfill leachate management using internet of things for sustainable smart city development. Sustain Cities Soc 66:102521
Sohi SM, Seifert J-, Ganji F (2021) RNNIDS: Enhancing network intrusion detection systems through deep learning. Comp Secur 102:102151
Sahar N, Mishra R, Kalam S (2021) Deep learning approach-based network intrusion detection system for fog-assisted IoT. In: Tiwari S, Suryani E, Ng AK, Mishra KK, Singh N (eds) Proceedings of international conference on big data, machine learning and their applications: ICBMA 2019. Springer Singapore, Singapore, pp 39–50. https://doi.org/10.1007/978-981-15-8377-3_4
Banadaki YM, Brook J, Sharifi S (2021) “Design of the network intrusion detection systems for the internet of things infrastructure using machine learning algorithms,” in NDE 40 and smart structures for industry, smart cities, communication, and energy. Int Soc Opt Photon 11594:115940J
Wang F, Yang N, Shakeel M, Saravanan V (2021) Machine learning for mobile network payment security evaluation system. Trans Emerg Telecommun Technol. https://doi.org/10.1002/ett.4226
Ahmad Z, Shahid Khan A, Wai Shiang C, Abdullah J, Ahmad F (2021) Network intrusion detection system: a systematic study of machine learning and deep learning approaches. Trans Emerg Telecommun Technol 32(1):e4150. https://doi.org/10.1002/ett.4150
Jeong S, Jeon B, Chung B, Kim HK (2021) Convolutional neural network-based intrusion detection system for AVTP streams in automotive Ethernet-based networks. Vehicular Commun 29:100338
Ji DJ, Park J, Cho D-H (2019) ConvAE: A new channel autoencoder based on convolutional layers and residual connections. IEEE Commun Lett 23(10):1769–1772
Wang Z, Zeng Y, Liu Y, Li D (2021) Deep belief network integrating improved kernel-based extreme learning machine for network intrusion detection. IEEE Access 9:16062–16091
Süzen AA (2021) Developing a multi-level intrusion detection system using hybrid-DBN. J Ambient Intell Humaniz Comput 12(2):1913–1923
Bilski J, Rutkowski L, Smoląg J, Tao D (2021) A novel method for speed training acceleration of recurrent neural networks. Inf Sci 553:266–279
Ma B, Jiang Z, Lu NL, Jiang Z (2020) Cybersecurity named entity recognition using bidirectional long short-term memory with conditional random fields. Tsinghua Sci Technol 26(3):259–265
Yuan S, Wu X (2021) Deep learning for insider threat detection: review, challenges and opportunities. Comp Secur 104:102221
Sharma N, Panwar D (2021) Advance security and challenges with intelligent IoT Devices. In: Goyal D, Chaturvedi P, Nagar AK, Purohit SD (eds) Proceedings of second international conference on smart energy and communication: ICSEC 2020. Springer Singapore, Singapore, pp 177–189. https://doi.org/10.1007/978-981-15-6707-0_17
Li T, Wu B, Yang Y, Fan Y, Zhang Y, Liu W. 2019 Compressing convolutional neural networks via factorized convolutional filters. InProceedings of the IEEE/CVF Conference on computer vision and pattern recognition (pp 3977-3986)
Deng L, Li G, Han S, Shi L, Xie Y (2020) Model compression and hardware acceleration for neural networks: a comprehensive survey. Proc IEEE 108(4):485–532. https://doi.org/10.1109/JPROC.2020.2976475
Basati A, Faghih MM (2021) APAE: an IoT intrusion detection system using asymmetric parallel auto-encoder. Neural Comput Appl. https://doi.org/10.1007/s00521-021-06011-9
Xin Y et al (2018) Machine learning and deep learning methods for cybersecurity. IEEE Access 6:35365–35381. https://doi.org/10.1109/ACCESS.2018.2836950
Tripathi G, Singh K, Vishwakarma DK (2019) Convolutional neural networks for crowd behaviour analysis: a survey. Vis Comput 35(5):753–776. https://doi.org/10.1007/s00371-018-1499-5
Alaeddine H, Jihene M (2021) Deep network in network. Neural Comput Appl 33:1453–1465
Vijayan M, Raguraman, and R. Mohan, (2021) A fully residual convolutional neural network for background subtraction. Pattern Recogn Lett 146:63–69. https://doi.org/10.1016/j.patrec.2021.02.017
Lv L, Wang W, Zhang Z, Liu X (2020) A novel intrusion detection system based on an optimal hybrid kernel extreme learning machine. Knowl-based Syst 195:105648
Zhang J, Ling Y, Fu X, Yang X, Xiong G, Zhang R (2020) Model of the intrusion detection system based on the integration of spatial-temporal features. Comp Secur 89:101681
Tian Q, Li J, Liu H (2019) A method for guaranteeing wireless communication based on a combination of deep and shallow learning. IEEE Access 7:38688–38695
Agarap AFM, A neural network architecture combining gated recurrent unit (gru) and support vector machine (SVM) for intrusion detection in network traffic data," presented at the Proceedings of the 2018 10th international conference on machine learning and computing, Macau, China, 2018. [Online]. Available: https://doi.org/10.1145/3195106.3195117
Zhou Y, Cheng G, Jiang S, Dai M (2020) Building an efficient intrusion detection system based on feature selection and ensemble classifier. Comput Netw 174:107247
Singh A, Kaur GS, Aujla RS, Batth, and S. Kanhere, (2020) DaaS: dew computing as a service for intelligent intrusion detection in edge-of-things ecosystem. IEEE Internet Things J. https://doi.org/10.1109/JIOT.2020.3029248
Li X, Chen W, Zhang Q, Wu L (2020) Building auto-encoder intrusion detection system based on random forest feature selection. Comput Secur 95:101851
Shone N, Ngoc TN, Phai VD, Shi Q (2018) A deep learning approach to network intrusion detection. IEEE Trans Emerg Top Comput Intell 2(1):41–50. https://doi.org/10.1109/TETCI.2017.2772792
Injadat M, Moubayed A, Nassif AB, Shami A (2020) Multi-stage optimized machine learning framework for network intrusion detection. IEEE Trans Netw Serv Manage. https://doi.org/10.1109/TNSM.2020.3014929
Gong D, Liu L, Le V, Saha B, Mansour MR, Venkatesh S, Hengel AV. 2019 Memorizing normality to detect anomaly: Memory-augmented deep autoencoder for unsupervised anomaly detection. In: proceedings of the ieee/cvf international conference on computer vision (pp 1705-1714).
Miah MO, Khan SS, Shatabda S, Farid DM (2019) Improving detection accuracy for imbalanced network intrusion classification using cluster-based under-sampling with random forests, in 2019 1st international conference on advances in science, engineering and robotics technology (ICASERT), 1–5, doi: https://doi.org/10.1109/ICASERT.2019.8934495.
Roy AG, Navab N, Wachinger C (2018) Recalibrating fully convolutional networks with spatial and channel “squeeze and excitation” blocks. IEEE Trans Med Imaging 38(2):540–549
Tang J, Sun D, Liu S, Gaudiot J-L (2017) Enabling deep learning on IoT devices. Computer 50(10):92–96
Gong LLD, Le V, Saha B, Mansour MR, Venkatesh S, Van Den Hengel A, (2019) Memorizing normality to detect anomaly: memory-augmented deep autoencoder for unsupervised anomaly detection, in IEEE/CVF International conference on computer vision (ICCV), 1705–1714, doi: https://doi.org/10.1109/ICCV.2019.00179
Andresini G, Appice A, Di Mauro N, Loglisci C, Malerba D (2020) Multi-channel deep feature learning for intrusion detection. IEEE Access 8:53346–53359
Muhammad G, Hossain MS, Garg S (2020) Stacked autoencoder-based intrusion detection system to combat financial fraudulent. IEEE Internet Things J. https://doi.org/10.1109/JIOT.2020.3041184
Peng Y, Zhang L, Liu S, Wu X, Zhang Y, Wang X (2019) Dilated residual networks with symmetric skip connection for image denoising. Neurocomputing 345:67–76
Yao H, Fu D, Zhang ML, Liu Y (2019) MSML: a novel multilevel semi-supervised machine learning framework for intrusion detection system. IEEE Internet Things J 6(2):1949–1959. https://doi.org/10.1109/JIOT.2018.2873125
Al-Garadi MA, Mohamed A, Al-Ali AK, Du X, Ali I, Guizani M (2020) A survey of machine and deep learning methods for internet of things (IoT) security. IEEE Commun Surv Tutorials 22(3):1646–1685
Moustafa N, Slay J (2015) 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), 1–6, doi: https://doi.org/10.1109/MilCIS.2015.7348942.
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Basati, A., Faghih, M.M. DFE: efficient IoT network intrusion detection using deep feature extraction. Neural Comput & Applic 34, 15175–15195 (2022). https://doi.org/10.1007/s00521-021-06826-6
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DOI: https://doi.org/10.1007/s00521-021-06826-6