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image of Detection of Intrusions in Internet of Things Based Deep Auto Encoder Using Deepnets

Abstract

Aims and background

As a communication paradigm that bridges the gap between virtual and physical spaces, the Internet of Things (IoT) has quickly gained popularity in recent years. In order to supplement the provisions made by security protocols, a network-based intrusion detection system (IDS) has emerged as a standard component of network architecture. IDSs monitor and detect cyber threats continuously throughout the network lifetime.

Objectives and Methods

The main contribution is the development of a two-level neural network model that optimizes the number of neurons and features in the hidden layers, achieving superior accuracy in anomaly detection. Its include the utilization of deep anomaly detection (DAD) to protect networks from unknown threats without requiring rule modifications. The research also emphasizes the importance of feature extraction and selection, employing parallel deep models for segmenting attacks. The proposed supervised technique enables simultaneous feature selection using parallel models, enhancing the accuracy of IDS designs. A novel hybrid technique combining Deep Auto Encoders (DAEs) and DeepNets is proposed for intrusion detection.

Results

The research present a data preprocessing stage, converting symbolic and quantitative data into real-valued vectors and normalizing them. Feature extraction involves utilizing DAEs to learn concise representations of datasets. The deep network architecture, including multilayer perceptrons and activation functions, is employed for feature extraction and classification. The proposed approach utilizes a DeepNets in the final stage in order to improve the rate of 97% accurate outputs and make it possible to achieve greatly accelerated execution durations.

Conclusion

When measured against the performances of other approaches to the extraction of features, the performance of the deep network platform is superior.

© 2024 Bentham Science Publishers
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2024-09-26
2025-04-24

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References

  1. Agrawal S. Sarkar S. Aouedi O. Yenduri G. Piamrat K. Alazab M. Bhattacharya S. Maddikunta P.K.R. Gadekallu T.R. Federated Learning for intrusion detection system: Concepts, challenges and future directions. Comput. Commun. 2022 195 346 361 10.1016/j.comcom.2022.09.012
    [Google Scholar]
  2. Nasir M.H. Khan S.A. Khan M.M. Fatima M. Swarm Intelligence inspired Intrusion Detection Systems — A systematic literature review. Comput. Netw. 2022 205 108708 10.1016/j.comnet.2021.108708
    [Google Scholar]
  3. Thakkar A. Lohiya R. A survey on intrusion detection system: feature selection, model, performance measures, application perspective, challenges, and future research directions. Artif. Intell. Rev. 2022 55 1 453 563 10.1007/s10462‑021‑10037‑9
    [Google Scholar]
  4. Saba T. Rehman A. Sadad T. Kolivand H. Bahaj S.A. Anomaly-based intrusion detection system for IoT networks through deep learning model. Comput. Electr. Eng. 2022 99 107810 10.1016/j.compeleceng.2022.107810
    [Google Scholar]
  5. Le K.H. Nguyen M.H. Tran T.D. Tran N.D. IMIDS: An intelligent intrusion detection system against cyber threats in IoT. Electronics (Basel) 2022 11 4 524 10.3390/electronics11040524
    [Google Scholar]
  6. Naseri T.S. Gharehchopogh F.S. A feature selection based on the farmland fertility algorithm for improved intrusion detection systems. J. Netw. Syst. Manage. 2022 30 3 40 10.1007/s10922‑022‑09653‑9
    [Google Scholar]
  7. Le T.T.H. Kim H. Kang H. Kim H. Classification and explanation for intrusion detection system based on ensemble trees and SHAP method. Sensors (Basel) 2022 22 3 1154 10.3390/s22031154 35161899
    [Google Scholar]
  8. Alzaqebah A. Aljarah I. Al-Kadi O. Damaševičius R. A modified grey wolf optimization algorithm for an intrusion detection system. Mathematics 2022 10 6 999 10.3390/math10060999
    [Google Scholar]
  9. Yu J. Ye X. Li H. A high precision intrusion detection system for network security communication based on multi-scale convolutional neural network. Future Gener. Comput. Syst. 2022 129 399 406 10.1016/j.future.2021.10.018
    [Google Scholar]
  10. Qazi E-H. Imran M. Haider N. Shoaib M. Razzak I. An intelligent and efficient network intrusion detection system using deep learning. Comput. Electr. Eng. 2022 99 107764 10.1016/j.compeleceng.2022.107764
    [Google Scholar]
  11. Mendonça R.V. Silva J.C. Rosa R.L. Saadi M. Rodriguez D.Z. Farouk A. A lightweight intelligent intrusion detection system for industrial internet of things using deep learning algorithms. Expert Syst. 2022 39 5 e12917 10.1111/exsy.12917
    [Google Scholar]
  12. Whelan J. Almehmadi A. El-Khatib K. Artificial intelligence for intrusion detection systems in Unmanned Aerial Vehicles. Comput. Electr. Eng. 2022 99 107784 10.1016/j.compeleceng.2022.107784
    [Google Scholar]
  13. Yadav N. Pande S. Khamparia A. Gupta D. Intrusion detection system on IoT with 5G network using deep learning. Wirel. Commun. Mob. Comput. 2022 2022 1 13 10.1155/2022/9304689
    [Google Scholar]
  14. Mushtaq E. Zameer A. Umer M. Abbasi A.A. A two-stage intrusion detection system with auto-encoder and LSTMs. Appl. Soft Comput. 2022 121 108768 10.1016/j.asoc.2022.108768
    [Google Scholar]
  15. Qiu W. Ma Y. Chen X. Yu H. Chen L. Hybrid intrusion detection system based on Dempster-Shafer evidence theory. Comput. Secur. 2022 117 102709 10.1016/j.cose.2022.102709
    [Google Scholar]
  16. Wang W. Sheng Y. Wang J. Zeng X. Ye X. Huang Y. Zhu M. HAST-IDS: Learning hierarchical spatial-temporal features using deep neural networks to improve intrusion detection. IEEE Access 2018 6 1792 1806 10.1109/ACCESS.2017.2780250
    [Google Scholar]
  17. Bai L. Yao L. Kanhere S.S. Wang X. Yang Z. Automatic Device Classification from Network Traffic Streams of Internet of Things. 2018 IEEE 43rd Conference on Local Computer Networks (LCN) 01-04 October 2018, Chicago, IL, USA, 2018, pp. 1-9. 10.1109/LCN.2018.8638232
    [Google Scholar]
  18. de Souza C.A. Westphall C.B. Machado R.B. Sobral J.B.M. Vieira G.S. Hybrid approach to intrusion detection in fog-based IoT environments. Comput. Netw. 2020 180 107417 10.1016/j.comnet.2020.107417
    [Google Scholar]
  19. Khan M. Karim M. Kim Y. A scalable and hybrid intrusion detection system based on the convolutional-LSTM network. Symmetry (Basel) 2019 11 4 583 10.3390/sym11040583
    [Google Scholar]
  20. Yazdinejad A. Dehghantanha A. Parizi R.M. Srivastava G. Karimipour H. Secure intelligent fuzzy blockchain framework: Effective threat detection in iot networks. Comput. Ind. 2023 144 103801 10.1016/j.compind.2022.103801
    [Google Scholar]
  21. Chakraborty A. Saha A. Roy Chowdhury S. Mukhopadhyay A. 2021 Deep Autoencoder Based Intrusion Detection System for Internet of Things Networks. 2021 International Conference on Computing, Power and Communication Technologies (GUCON) 11-14 February 2018, Chuncheon, Korea (South), 2018, pp. 178-183.
    [Google Scholar]
  22. Sudhakar B. Sikrant P.A. Prasad M.L. Latha S.B. Kumar G.R. Sarika S. Shaker Reddy P.C. Brain Tumor Image Prediction from MR Images Using CNN Based Deep Learning Networks. J. Inf. Technol. Manage. 2024 16 1 44 60
    [Google Scholar]
  23. Chang V. Golightly L. Modesti P. Xu Q.A. Doan L.M.T. Hall K. Boddu S. Kobusińska A. A survey on intrusion detection systems for fog and cloud computing. Future Internet 2022 14 3 89 10.3390/fi14030089
    [Google Scholar]
  24. Rekha M.N. Prasad M.L. Mukherjee S. Nikam S.V. Sharma S. Reddy P.C.S. An Automatic Error Recognition approach for Machine Translation Results based on Deep Learning. 2024 2nd International Conference on Computer, Communication and Control (IC4) 08-10 February 2024, Indore, India, 2024, pp. 1-8. 10.1109/IC457434.2024.10486776
    [Google Scholar]
  25. Yazdinejad A. Dehghantanha A. Parizi R.M. Hammoudeh M. Karimipour H. Srivastava G. Block hunter: Federated learning for cyber threat hunting in blockchain-based iiot networks. IEEE Trans. Industr. Inform. 2022 18 11 8356 8366 10.1109/TII.2022.3168011
    [Google Scholar]
  26. Rani R.Y. Prasad M.L. Reddy I.S. Tayubi I.A. Sultan G. Reddy P.C.S. 2024 Early Prediction and Diagnosis Cardiovascular Disease Using Deep Learning Models. 2024 International Conference on Emerging Technologies in Computer Science for Interdisciplinary Applications (ICETCS) 22-23 April 2024, Bengaluru, India, 2024, pp. 1-6. 10.1109/ICETCS61022.2024.10543851
    [Google Scholar]
  27. Azeroual O. Nikiforova A. Apache spark and mllib-based intrusion detection system or how the big data technologies can secure the data. Information 2022 13 2 58 10.3390/info13020058
    [Google Scholar]
  28. Suneel S. Balaram A. Amina Begum M. Umapathy K. Reddy P.C.S. Talasila V. Quantum mesh neural network model in precise image diagnosing. Opt. Quantum Electron. 2024 56 4 559 10.1007/s11082‑023‑06245‑y
    [Google Scholar]
  29. Thakkar A. Lohiya R. Fusion of statistical importance for feature selection in Deep Neural Network-based Intrusion Detection System. Inf. Fusion 2023 90 353 363 10.1016/j.inffus.2022.09.026
    [Google Scholar]
  30. Chalé M. Bastian N.D. Generating realistic cyber data for training and evaluating machine learning classifiers for network intrusion detection systems. Expert Syst. Appl. 2022 207 117936 10.1016/j.eswa.2022.117936
    [Google Scholar]
  31. Yang Y. Cheng J. Liu Z. Li H. Xu G. A multi-classification detection model for imbalanced data in NIDS based on reconstruction and feature matching. J. Cloud Comput. 2024 13 1 31 10.1186/s13677‑023‑00584‑7 36910722
    [Google Scholar]
  32. Prasad M.L. Kiran A. Shaker Reddy P.C. Chronic Kidney Disease Risk Prediction Using Machine Learning Techniques. J. Inf. Technol. Manage. 2024 16 1 118 134
    [Google Scholar]
  33. Meng Z. Qian Q. Xu M. Yu B. Yıldız A.R. Mirjalili S. PINN-FORM: A new physics-informed neural network for reliability analysis with partial differential equation. Comput. Methods Appl. Mech. Eng. 2023 414 116172 10.1016/j.cma.2023.116172
    [Google Scholar]
  34. Gozuoglu A. Ozgonenel O. Gezegin C. CNN-LSTM based deep learning application on Jetson Nano: Estimating electrical energy consumption for future smart homes. Inter. Things 2024 26 101148
    [Google Scholar]
  35. Rao K.R. Kumari M.S. Eklarker R. Reddy P.C.S. Muley K. Burugari V.K. An Adaptive Deep Learning Framework for Prediction of Agricultural Yield. 2024 International Conference on Integrated Circuits and Communication Systems (ICICACS) 23-24 February 2024, Raichur, India, 2024, pp. 1-6. 10.1109/ICICACS60521.2024.10498465
    [Google Scholar]
  36. Rajarajan S. Kavitha M.G. Enhanced security for IoT networks: a hybrid optimized learning model for intrusion classification. Sadhana 2024 49 2 180 10.1007/s12046‑024‑02535‑7
    [Google Scholar]
  37. Ambika N. Early Detection of Darknet Traffic in Internet of Things Applications. In: Automated Secure Computing for Next‐Generation Systems Wiley 2024 139 153 10.1002/9781394213948.ch7
    [Google Scholar]
  38. Gadupudi A. Rani R.Y. Jayaram B. Sharma N. Deshmukh J.K. Reddy P.C.S. An Adaptive Deep Learning Model for Crop Yield Prediction. 2024 2nd International Conference on Computer, Communication and Control (IC4) 08-10 February 2024, Indore, India, 2024, pp. 1-5. 10.1109/IC457434.2024.10486733
    [Google Scholar]
  39. Sait S.M. Mehta P. Yıldız A.R. Yıldız B.S. Optimal design of structural engineering components using artificial neural network-assisted crayfish algorithm. Mater. Test. 2024 10.1515/mt‑2024‑0075
    [Google Scholar]
  40. Yazdinejadna A. Parizi R.M. Dehghantanha A. Khan M.S. A kangaroo-based intrusion detection system on software-defined networks. Comput. Netw. 2021 184 107688 10.1016/j.comnet.2020.107688
    [Google Scholar]
  41. Tayubi I.A. Pawar D.N. Kiran A. Reddy P.C.S. Sharma N. Chitra D. Facial emotion recognition using a local binary pattern based deep learning. Image and Vision Comput. 2024 27 6 803 816 10.1109/IC457434.2024.10486509
    [Google Scholar]
  42. Li X. Xie C. Zhao Z. Wang C. Yu H. Anomaly detection algorithm of industrial internet of things data platform based on deep learning. IEEE Trans. Green Commun. Netw. 2024 2024 1 10.1109/TGCN.2024.3403102
    [Google Scholar]
  43. Zhao L. Yang Q. Huang H. Guo L. Jiang S. Intelligent wireless sensing driven metaverse: A survey. Comput. Commun. 2024 214 46 56 10.1016/j.comcom.2023.11.024
    [Google Scholar]
  44. Rishikesh Sinha D. Traditional and blockchain based IoT and IIoT security in the context of agriculture: a survey. Wirel. Pers. Commun. 2023 133 4 2267 2295 10.1007/s11277‑024‑10866‑1
    [Google Scholar]
  45. Hughes J. Pastrana S. Hutchings A. Afroz S. Samtani S. Li W. Santana Marin E. The art of cybercrime community research. ACM Comput. Surv. 2024 56 6 1 26 10.1145/3639362
    [Google Scholar]
  46. Amrouni N. Benzaoui A. Zeroual A. Palmprint recognition: Extensive exploration of databases, methodologies, comparative assessment, and future directions. Appl. Sci. 2023 14 1 153 10.3390/app14010153
    [Google Scholar]
/content/journals/swcc/10.2174/0122103279327268240911034337
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Detection of Intrusions in Internet of Things Based Deep Auto Encoder Using Deepnets
Bentham Science Publishers ; https://doi.org/10.2174/0122103279327268240911034337
/content/journals/swcc/10.2174/0122103279327268240911034337
/content/journals/swcc/10.2174/0122103279327268240911034337
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  • Article Type:     Research Article
Keywords: Detection ; internet of things ; intrusion ; deep networks ; encoder ; IDS

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