Abstract
Bi-directional communication networks are the foundation of advanced metering infrastructure (AMI), but they also expose smart grids to serious intrusion risks. While previous studies have proposed various intrusion detection systems (IDS) for AMI, most have not comprehensively considered the impact of different factors on intrusions. To ensure the security of the bi-directional communication network of AMI, this paper proposes an IDS based on deep learning theory. First, the invalid features are eliminated according to the feature screening strategy based on eXtreme Gradient Boosting (XGBoost), after which the data distribution is balanced by the adaptive synthetic (ADASYN) sampling technique. Next, multi-space feature subsets based on the convolutional neural network (CNN) are constructed to enrich the spatial distribution of samples. Finally, the Transformer is used to construct feature associations and extract crucial traits, such as the temporal and fine-grained characteristics of features, to complete the identification of intrusion behaviors. The proposed IDS is tested on the KDDCup99, NSL-KDD, and CICIDS-2017 datasets, and the results show that it has high performance with accuracy of 97.85%, 91.04%, and 91.06% respectively.
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The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
References
Aa A, Yz A, Mz B (2021) Exploiting dynamic spatio-temporal correlations for citywide traffic flow prediction using attention based neural networks. Inf Sci 577:852–870
Aldaweri MS, Ariffin KZ, Abdullah S, Senan MM (2020) An analysis of the kdd99 and unsw-nb15 datasets for the intrusion detection system. Symmetry 12:1666
Ali A, Zhu Y, Zakarya M (2021) A data aggregation based approach to exploit dynamic spatio-temporal correlations for citywide crowd flows prediction in fog computing. Multimed Tools Appl 80:31401–31433
Ali A, Zhu Y, Zakarya M (2022) Exploiting dynamic spatio-temporal graph convolutional neural networks for citywide traffic flows prediction. Neural Netw 145:233–247
Alsharif A, Nabil M, Tonyali S, Mohammed H, Mahmoud M, Akkaya K (2018) Epic: efficient privacy-preserving scheme with e2e data integrity and authenticity for ami networks. IEEE Internet Things J 6:3309–3321
Alsharif A, Nabil M, Mahmoud M, Abdallah M (2019) Epda: efficient and privacy-preserving data collection and access control scheme for multi-recipient ami networks. IEEE Access 7:27829–27845
Alsharif A, Nabil M, Sherif A, Mahmoud M, Song M (2019) Mdms: efficient and privacy-preserving multidimension and multisubset data collection for ami networks. IEEE Internet Things J 6(6):10363–10374
Anderson JP (1980) Computer security threat monitoring and surveillance. James P. Anderson Co., Washington, pp 1–46
Ayub N, Aurangzeb K, Awais M, Ali U (2020) Electricity theft detection using cnn-gru and manta ray foraging optimization algorithm. In: 2020 IEEE 23Rd international multitopic conference (INMIC), pp 1–6
Benmalek M, Challal Y, Derhab A (2019) Authentication for smart grid ami systems: threat models, solutions, and challenges. In: 2019 IEEE 28Th international conference on enabling technologies: infrastructure for collaborative enterprises (WETICE), pp 208–213
Biswas R, Roy S (2021) Botnet traffic identification using neural networks. Multimed Tools Appl 80:24147–24171
Choudhary S, Kesswani N (2020) Analysis of kdd-cup’99, nsl-kdd and unsw-nb15 datasets using deep learning in iot. Proc Comput Sci 167:1561–1573
Das U, Namboodiri V (2018) A quality-aware multi-level data aggregation approach to manage smart grid ami traffic. IEEE Trans Parallel Distrib Syst PP(2):245–256
Das U, Namboodiri V (2019) A quality-aware multi-level data aggregation approach to manage smart grid ami traffic. IEEE Trans Parallel Distrib Syst 30(2):245–256
Devlin J, Chang M-W, Lee K, Toutanova K (2019) BERT: pre-training of deep bidirectional transformers for language understanding: 4171–4186
Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S, Uszkoreit J, Houlsby N (2021) An image is worth 16x16 words: transformers for image recognition at scale. arXiv:2010.11929
Engelbrecht J, Hancke GP, Osifeko MO (2019) Design and implementation of an electrical tamper detection system. In: IECON 2019 - 45Th annual conference of the IEEE industrial electronics society, vol 1. pp 2952–2957
Gope P (2020) Pmake: privacy-aware multi-factor authenticated key establishment scheme for advance metering infrastructure in smart grid. Comput Commun 152:338–344
Gupta N, Jindal V, Bedi P (2021) LIO-IDS: handling class imbalance using LSTM and improved one-vs-one technique in intrusion detection system. Comput Netw 192(19):108076
Haddad Z, Mahmoud M, Taha S, Saroit IA (2015) Secure and privacy-preserving ami-utility communications via lte-a networks. :748–755
Hasan MN, Toma RN, Nahid AA, Islam M, Kim JM (2019) Electricity theft detection in smart grid systems: a cnn-lstm based approach. Energies 12:3310
He Y, Mendis GJ, Wei J (2017) Real-time detection of false data injection attacks in smart grid: a deep learning-based intelligent mechanism. IEEE Trans Smart Grid 8(5):2505–2516
Hsu C, Wang S (2021) Hffpnn classifier: a hybrid approach for intrusion detection based opso and hybridization of feed forward neural network (ffnn) and probabilistic neural network (pnn). Multimed Tools Appl
Ibrahem MI, Badr MM, Fouda MM, Mahmoud M, Alasmary W, Fadlullah ZM (2020) Pmbfe: efficient and privacy-preserving monitoring and billing using functional encryption for ami networks. In: 2020 international symposium on networks, computers and communications (ISNCC), pp 1–7
Ieracitano C, Adeel A, Morabito FC, Hussain A (2020) A novel statistical analysis and autoencoder driven intelligent intrusion detection approach. Neurocomputing 387:51–62
Ismail M, Shaaban MF, Naidu M, Serpedin E (2020) Deep learning detection of electricity theft cyber-attacks in renewable distributed generation. IEEE Trans Smart Grid 11(4):3428–3437
Javaid N, Jan N, Javed MU (2021) An adaptive synthesis to handle imbalanced big data with deep siamese network for electricity theft detection in smart grids - sciencedirect. J Parallel Distrib Comput 153:44–52
Jeong JH, Kwon S, Hong MP, Kwak J, Shon T (2019) Adversarial attack-based security vulnerability verification using deep learning library for multimedia video surveillance. Multimed Tools Appl 79:16077–16091
Kala TS, Christy A (2020) Hffpnn classifier: a hybrid approach for intrusion detection based opso and hybridization of feed forward neural network (ffnn) and probabilistic neural network (pnn). Multimed Tools Appl 80:6457–6478
Khammassi C, Krichen S (2017) A ga-lr wrapper approach for feature selection in network intrusion detection. Comput Secur 70:255–277
Kim J, Kim J, Thu H, Kim H (2016) Long short term memory recurrent neural network classifier for intrusion detection. In: International conference on platform technology & service, pp 1–5
Kong X, Zhao X, Liu C, Li Q, Li Y (2021) Electricity theft detection in low-voltage stations based on similarity measure and dt-ksvm. Int J Electr Power Energy Syst 125(3):106544
Liu G, Zhang J (2020) Cnid: research of network intrusion detection based on convolutional neural network. Discret Dyn Nat Soc 2020:1–11
Nabil M, Ismail M, Mahmoud M, Shahin M, Qaraqe K, Serpedin E (2018) Deep recurrent electricity theft detection in ami networks with random tuning of hyper-parameters,740–745
Pereira J, Saraiva F (2020) A comparative analysis of unbalanced data handling techniques for machine learning algorithms to electricity theft detection. In: 2020 IEEE congress on evolutionary computation (CEC), pp 1–8
Prasad M, Tripathi S, Dahal K (2019) An efficient feature selection based bayesian and rough set approach for intrusion detection. Appl Soft Comput 87(9):105980
Punmiya R, Choe S (2019) Energy theft detection using gradient boosting theft detector with feature engineering-based preprocessing. IEEE Trans Smart Grid 10(2):2326–2329
Rahman MA, Asyhari AT, Wen OW, Ajra H, Ahmed Y, Anwar F (2021) Effective combining of feature selection techniques for machine learning-enabled iot intrusion detection. Multimed Tools Appl 80:31381–31399
Rajesh Kanna P, Santhi P (2021) Unified deep learning approach for efficient intrusion detection system using integrated spatial–temporal features. Knowl-Based Syst 226:107132
Razavi R, Gharipour A, Fleury M, Akpan IJ (2019) A practical feature-engineering framework for electricity theft detection in smart grids. Appl Energy 238:481–494
Saeed MS, Mustafa MW, Sheikh UU, Jumani TA, Khan I, Atawneh S, HamadneH NN (2020) An efficient boosted c5.0 decision-tree-based classification approach for detecting non-technical losses in power utilities. Energies 13:3242
Shen Y, Zheng K, Wu C, Zhang M, Niu X, Yang Y, Furnell S (2018) An ensemble method based on selection using bat algorithm for intrusion detection. Comput J 61(4):526–538
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
Tian C, Su C, Yang C, Zheng Y (2021) Big data analytics for cyber-physical system in smart city, Springer, pp 714–721. In: Atiquzzaman M, Yen N, Xu Z (eds)
Tonyali S, Akkaya K, Saputro N, Uluagac AS (2016) A reliable data aggregation mechanism with homomorphic encryption in smart grid ami networks. In: 2016 13Th IEEE annual consumer communications networking conference (CCNC), pp 550–555
Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser U, Polosukhin I (2017)
Wang W, Peng X, Su Y, Qiao Y, Cheng J (2021) Ttpp: temporal transformer with progressive prediction for efficient action anticipation. Neurocomputing 438:270–279
Yan Z, Wen H (2020) Electricity theft detection base on extreme gradient boosting in ami. In: 2020 IEEE international instrumentation and measurement technology conference (i2MTC), pp 1–6
Yan Z, Wen H (2021) Electricity theft detection base on extreme gradient boosting in ami. IEEE Trans Instrum Meas 70:1–9
Yang Z, Ping S, Aijaz A, Aghvami AH (2016) A global optimization-based routing protocol for cognitive-radio-enabled smart grid ami networks. IEEE Syst J 12(1):1015–1023
Yang H, Wang F (2019) Wireless network intrusion detection based on improved convolutional neural network. IEEE Access 7:64366–64374
Yao R, Wang N, Liu Z, Chen P, Sheng X (2021) Intrusion detection system in the advanced metering infrastructure: a cross-layer feature-fusion cnn-lstm-based approach. Sensors 21(2):626
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
Zhang K, Hu Z, Zhan Y, Wang X, Guo K (2020) A smart grid ami intrusion detection strategy based on extreme learning machine. Energies 13:4907
Zhang H, Huang L, Wu CQ, Li Z (2020) An effective convolutional neural network based on smote and gaussian mixture model for intrusion detection in imbalanced dataset. Comput Netw 177:107315
Zheng Z, Yang Y, Niu X, Dai H-N, Zhou Y (2018) Wide and deep convolutional neural networks for electricity-theft detection to secure smart grids. IEEE Trans Ind Inform 14(4):1606–1615
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Yao, R., Wang, N., Chen, P. et al. A CNN-transformer hybrid approach for an intrusion detection system in advanced metering infrastructure. Multimed Tools Appl 82, 19463–19486 (2023). https://doi.org/10.1007/s11042-022-14121-2
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DOI: https://doi.org/10.1007/s11042-022-14121-2