Deep Transfer Learning Based Intrusion Detection System for Electric Vehicular Networks
<p>The standard CAN bus node architecture.</p> "> Figure 2
<p>The standard CAN bus message frame format.</p> "> Figure 3
<p>(<b>a</b>) Flooding attack scenario against an IVN; (<b>b</b>) Fuzzing attack scenario against an IVN; (<b>c</b>) Spoofing attack scenario against an IVN.</p> "> Figure 4
<p>Application of intrusion detection model for IVN traffic. The figure illustrates how the proposed IDS can detect the possible attack vectors within an in-vehicle Network. The car image is adopted from [<a href="#B47-sensors-21-04736" class="html-bibr">47</a>].</p> "> Figure 5
<p>The block diagram of the proposed P-LeNet mode.</p> "> Figure 6
<p>The structure of the proposed P-LeNet model.</p> "> Figure 7
<p>Statistics of the dataset.</p> "> Figure 8
<p>Evaluation process on the datasets with the selected models.</p> "> Figure 9
<p>TML algorithms’ performance metrics visualization.</p> "> Figure 10
<p>DL models’ performance metrics visualization.</p> "> Figure 11
<p>DTL models’ training and testing accuracy.</p> "> Figure 12
<p>Training and testing accuracy of the proposed P-LeNet model.</p> "> Figure 13
<p>DTL models’ training and testing loss.</p> "> Figure 14
<p>Training and testing loss of the proposed P-LeNet model.</p> ">
Abstract
:1. Introduction
- In this work, a deep transfer learning-based LeCun Network (LeNet) model has been proposed for effective intrusion detection in-vehicle network CAN bus protocol. The proposed model enabled to develop effective models that speed up the training process and improve the performance of the deep learning model.
- The experiments have been conducted using an in-vehicle real-time dataset generated from heterogeneous sources that include three types of malicious messages. We have made observations on this practical data to identify the best features in the context of supervised learning for effective intrusion detection.
- In-depth architectural and statistical analyses have been conducted considering traditional machine learning, deep learning and deep transfer learning algorithms. Extensive analysis and performance evaluation show that the proposed deep transfer learning-based LeNet model outperforms other approaches.
2. Background and Related Work
2.1. Background of CAN and Security Vulnerabilities
2.2. TML and DL Based IDSs for IVN
2.3. DTL Based IDSs for IVN
3. Proposed Solution
3.1. Problem Statement
3.2. Solution Formulation
3.3. Architecture
4. Materials and Methods
4.1. Dataset Description
4.2. Data Preparation
4.2.1. Data Cleaning
4.2.2. Data Integration
4.2.3. Data Transformation
4.3. Training Process
5. Results
5.1. Experimental Evaluation Indicators
- True-positive (TP) refers to the number of actual attack instances that are correctly detected as attack.
- True-negative (TN) is the number of normal instances that are correctly detected as normal.
- False-positive (FP) is the number of normal instances that are incorrectly detected as attack.
- False-negative (FN) refers to the number of actual attack instances that are incorrectly detected as normal.
5.2. TML Models Analysis
5.3. DL Models Analysis
5.4. DTL Models Analysis
5.5. Performance Comparison
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
CAN | Controller Area Network |
ECU | Electronic Control Unit |
IVN | In-Vehicle Network |
CAN FD | CAN Flexible Data-Rate |
LIN | Local Interconnect Network |
MOST | Media Oriented Systems Transport |
ML | Machine Learning |
IDS | Intrusion Detection System |
TML | Traditional Machine Learning |
DL | Deep Learning |
DTL | Deep Transfer Learning |
DT | Decision Tree |
RF | Random Forest |
SVM | Support Vector Machine |
KNN | K-Nearest Neighbor |
NN | Neural Network |
RNN | Recurrent Neural Network |
CNN | Convolutional Neural Network |
LSTM | Long Short-Term Memory |
FCN | Fully Convolutional Networks |
IncepNet | Inception Network |
ResNet | Residual Neural Network |
LeNet | LeCun Network |
ReLu | Rectified Linear-Unit |
DLC | Data Length Code |
TNR | True Negative Rate |
TPR | True Positive Rate |
SOF | Start of Frame |
RTR | Remote Transmission Request |
IDE | Identifier Extension |
CRC | Cyclical Redundancy Check |
ACK | Acknowledgment |
EOF | End of Frame |
IFS | Inter Frame Space |
DEL | Delimiter |
ID | Identifier |
OTA | Over the Air |
DNN | Deep Neural Network |
DBN | Deep Belief network |
DoS | Denial of Service |
ARP | Address Resolution Protocol |
RPM | Radiation Portal Monitors |
GAN | Generative Adversarial Network |
DCAE | Deep Contractive Auto Encoder |
DCNN | Deep Convolutional Neural Network |
IoV | Internet of Vehicle |
MMN | Minimum Maximum Normalization |
MMD | Maximum Mean Discrepancy |
HEX2DEC | Hexadecimal to Decimal |
R-MPFR | Multiple Precision Floating-Point Reliable |
ROC-AUC | Receiver Operating Characteristic-Area Under the Curve |
References
- Han, M.L.; IlKwak, B.; Kim, H.K. Anomaly intrusion detection method for vehicular networks based on survival analysis. Veh. Commun. 2018, 14, 52–63. [Google Scholar] [CrossRef]
- Steger, M.; Boano, C.A.; Niedermayr, T.; Karner, M.; Hillebrand, J.; Roemer, K.; Rom, W.A. Efficient and Secure Automotive Wireless Software Update Framework. IEEE Trans. Ind. Inform. 2018, 14, 2181–2193. [Google Scholar] [CrossRef]
- Zeng, W.; Mohammed, A.S.; Khalid, M.A.S.; Chowdhury, S. In-Vehicle Networks Outlook: Achievements and Challenges. IEEE Commun. Surv. Tutor. 2016, 18, 1552–1571. [Google Scholar] [CrossRef]
- Mathur, R.; Saraswat, R.; Mathur, G. An Analytical Study of Communication Protocols Used in Automotive Industry. Int. J. Eng. Res. Technol. (IJERT) 2018, 2, 287–292. [Google Scholar]
- Kieu, T.; Yang, B.; Jensen, C.S. Outlier Detection for Multidimensional Time Series Using Deep Neural Networks. In Proceedings of the 2018 19th IEEE International Conference on Mobile Data Management (MDM), Aalborg, Denmark, 25–28 June 2018; pp. 125–134. [Google Scholar] [CrossRef]
- Sommer, C.; Hoefler, R.; Samwer, M.; Gerlich, D.W. A deep learning and novelty detection framework for rapid phenotyping in high-content screening. Mol. Biol. Cell 2017, 28, 3428–3436. [Google Scholar] [CrossRef] [PubMed]
- Sharma, S.; Krishna, C.R.; Sahay, S.K. Detection of Advanced Malware by Machine Learning Techniques. In Soft Computing: Theories and Applications; Springer: Singapore, 2019; pp. 333–342. [Google Scholar]
- Avatefipour, O.; Malik, H. State-of-the-Art Survey on In-Vehicle Network Communication “CAN-Bus” Security and Vulnerabilities. arXiv 2018, arXiv:1802.01725. [Google Scholar]
- Robert Bosch GmbH CAN Specification. Postfach 1991, 2, 1–72.
- Bozdal, M.; Samie, M.; Aslam, S.; Jennions, I. Evaluation of CAN Bus Security Challenges. Sensors 2020, 20, 2364. [Google Scholar] [CrossRef] [Green Version]
- Khatri, N.; Shrestha, R.; Nam, S.Y. Security Issues with In-Vehicle Networks, and Enhanced Countermeasures Based on Blockchain. Electronics 2021, 10, 893. [Google Scholar] [CrossRef]
- Lee, H.; Jeong, S.H.; Kim, H.K. OTIDS: A Novel Intrusion Detection System for In-vehicle Network by Using Remote Frame. In Proceedings of the 2017 15th Annual Conference on Privacy, Security and Trust (PST), Calgary, AB, Canada, 28–30 August 2017. [Google Scholar]
- Avatefipour, O.; Hafeez, A.; Tayyab, M.; Malik, H. Linking Received Packet to the Transmitter Through Physical-Fingerprinting of Controller Area Network. In Proceedings of the IEEE Workshop on Information Forensics and Security (WIFS), Rennes, France, 4–7 December 2017; pp. 1–6. [Google Scholar] [CrossRef] [Green Version]
- Yuan, Y.; Yuan, H.; Guo, L.; Yang, H.; Sun, S. Resilient Control of Networked Control System Under DoS Attacks: A Unified Game Approach. IEEE Trans. Ind. Inform. 2016, 12, 1786–1794. [Google Scholar] [CrossRef]
- Markovitz, M.; Wool, A. Field classification, modeling and anomaly detection in unknown CAN bus networks. Veh. Commun. 2017, 9, 43–52. [Google Scholar] [CrossRef]
- Theissler, A.; Ulmer, D.; Dear, I. Interactive knowledge discovery in recordings from vehicle tests. In Proceedings of the FISITA World Automotive Congress, Budapest, Hungary, 30 May–4 June 2010; pp. 1–10. [Google Scholar]
- Chandola, V.; Banerjee, A.; Kumar, V. Anomaly detection: A survey. ACM Comput. Surv. 2009, 41, 15. [Google Scholar] [CrossRef]
- Alshammari, A.; Zohdy, M.A.; Debnath, D.; Corser, G. Classification Approach for Intrusion Detection in Vehicle Systems. Wirel. Eng. Technol. 2018, 9, 79–94. [Google Scholar] [CrossRef] [Green Version]
- Wang, L.; Jones, R. Big Data Analytics for Network Intrusion Detection: A Survey. Int. J. Netw. Commun. 2017, 7, 24–31. [Google Scholar] [CrossRef]
- Nazakat, I.; Khurshid, K. Intrusion Detection System for In-Vehicular Communication. In Proceedings of the 2019 15th International Conference on Emerging Technologies (ICET), Peshawar, Pakistan, 2–3 December 2019; pp. 1–6. [Google Scholar] [CrossRef]
- Bozdal, M.; Samie, M.; Aslam, S.; Jennions, I. A Survey on CAN Bus Protocol: Attacks, Challenges, and Potential Solutions. In Proceedings of the 2018 International Conference on Computing, Electronics Communications Engineering (iCCECE), Southend, UK, 16–17 August 2018; pp. 201–205. [Google Scholar] [CrossRef]
- Lokman, S.F.; Othman, A.T.; Abu-Bakar, M.H. Intrusion detection system for automotive Controller Area Network (CAN) bus system: A review. EURASIP J. Wirel. Commun. Netw. 2019, 7, 24–31. [Google Scholar] [CrossRef] [Green Version]
- Kang, M.J.; Kang, J.W. Intrusion Detection System Using Deep Neural Network for In-Vehicle Network Security. PLoS ONE 2016, 11, e0155781. [Google Scholar] [CrossRef]
- Loukas, G.; Vuong, T.; Heartfield, R.; Sakellari, G.; Yoon, Y.; Gan, D. Cloud-Based Cyber-Physical Intrusion Detection for Vehicles Using Deep Learning. IEEE Access 2017, 6, 3491–3508. [Google Scholar] [CrossRef]
- Seo, E.; Song, H.M.; Kim, H.K. GIDS: GAN based Intrusion Detection System for In-Vehicle Network. In Proceedings of the IEEE Access 2018 16th Annual Conference on Privacy, Security and Trust (PST), Belfast, Ireland, 28–30 August 2018; pp. 1–6. [Google Scholar] [CrossRef] [Green Version]
- Lokman, S.F.; Othman, A.T.; Musa, S.; Abu-Bakar, M.H. Deep Contractive Autoencoder-Based Anomaly Detection for In-Vehicle Controller Area Network (CAN). Prog. Eng. Technol. Adv. Struct. Mater. 2019, 119. [Google Scholar] [CrossRef]
- Zhang, J.; Li, F.; Zhang, H.; Li, R.; Li, Y. Intrusion detection system using deep learning for in-vehicle security. Ad Hoc Netw. 2019, 95. [Google Scholar] [CrossRef]
- Zhu, K.; Chen, Z.; Peng, Y.; Zhang, L. Mobile Edge Assisted Literal Multi-Dimensional Anomaly Detection of In-Vehicle Network Using LSTM. IEEE Trans. Veh. Technol. 2019, 68, 4275–4284. [Google Scholar] [CrossRef]
- Avatefipour, O.; Al-Sumaiti, A.S.; El-Sherbeeny, A.M.; Awwad, E.M.; Elmeligy, M.A.; Mohamed, M.A.; Malik, H. An Intelligent Secured Framework for Cyberattack Detection in Electric Vehicles’ CAN Bus Using Machine Learning. IEEE Access 2019, 7, 127580–127592. [Google Scholar] [CrossRef]
- Xiao, J.; Wu, H.; Li, X. Internet of Things Meets Vehicles: Sheltering In-Vehicle Network through Lightweight Machine Learning. Symmetry 2019, 11, 1388. [Google Scholar] [CrossRef] [Green Version]
- Al-Saud, M.; Eltamaly, A.M.; Mohamed, M.A.; Kavousi-Fard, A. An Intelligent Data-Driven Model to Secure Intravehicle Communications Based on Machine Learning. IEEE Trans. Ind. Electron. 2020, 67, 5112–5119. [Google Scholar] [CrossRef]
- Lin, Y.; Chen, C.; Xiao, F.; Avatefipour, O.; Alsubhi, K.; Yunianta, A. An Evolutionary Deep Learning Anomaly Detection Framework for In-Vehicle Networks—CAN Bus. IEEE Trans. Ind. Appl. 2020. [Google Scholar] [CrossRef]
- Yang, Y.; Duan, Z.; Tehranipoor, M. Identify a Spoofing Attack on an In-Vehicle CAN Bus Based on the Deep Features of an ECU Fingerprint Signal. Smart Cities 2020, 3, 2. [Google Scholar] [CrossRef] [Green Version]
- Hossain, M.D.; Inoue, H.; Ochiai, H.; Fall, D.; Kadobayashi, Y. LSTM-Based Intrusion Detection System for In-Vehicle Can Bus Communications. IEEE Access 2020, 8, 185489–185502. [Google Scholar] [CrossRef]
- Song, H.M.; Woo, J.; Kim, H.K. In-vehicle network intrusion detection using deep convolutional neural network. Veh. Commun. 2020, 21. [Google Scholar] [CrossRef]
- Li, X.; Hu, Z.; Xu, M.; Wang, Y.; Ma, J. Transfer learning based intrusion detection scheme for Internet of vehicles. Inf. Sci. 2021, 547, 119–135. [Google Scholar] [CrossRef]
- Lu, J.; Behbood, V.; Hao, P.; Zuo, H.; Xue, S.; Zhang, G. Transfer Learning using Computational Intelligence: A Survey. Knowl. Based Syst. 2015, 80, 14–23. [Google Scholar] [CrossRef]
- Zadrozny, B. Learning and Evaluating Classiers under Sample Selection Bias. In Proceedings of the Twenty-First International Conference on Machine Learning, Banff, AB, Canada, 4–8 July 2004. [Google Scholar] [CrossRef]
- Dai, W.; Yang, Q.; Xue, G.; Yu, Y. Boosting for transfer learning. In Proceedings of the 24th international conference on Machine Learning (ICML’07), New York, NY, USA, 20–24 June 2007; pp. 193–200. [Google Scholar] [CrossRef]
- Raina, R.; Andrew, Y.N.; Koller, D. Constructing Informative Priors using Transfer Learning. In Proceedings of the 23th International Conference on Machine Learning (ICML’06), Pittsburgh, PA, USA, 25–29 June 2006; Association for Computing Machinery: New York, NY, USA, 2006; pp. 713–720. [Google Scholar] [CrossRef] [Green Version]
- Gou, S.; Wang, Y.; Jiao, L.; Feng, J.; Yao, Y. Distributed Transfer Network Learning Based Intrusion Detection. In Proceedings of the IEEE International Symposium on Parallel and Distributed Processing with Applications, Chengdu, China, 10–12 August 2009; pp. 511–515. [Google Scholar] [CrossRef]
- Xu, Y.; Liu, Z.; Li, Y.; Zheng, Y.; Hou, H.; Gao, M.; Song, Y.; Xin, Y. Intrusion Detection Based on Fusing Deep Neural Networks and Transfer Learning. In Proceedings of the Digital TV and Wireless Multimedia Communication (IFTC 2019), Shanghai, China, 19–20 September 2009; Volume 1181. [Google Scholar] [CrossRef]
- Kang, H.; Kwak, B.; Lee, Y.H.; Lee, H.; Lee, H.; Kim, H.K. Car Hacking: Attack and Defense Challenge 2020 Dataset. IEEE Dataport 2021. [Google Scholar] [CrossRef]
- Li, F.; Shirahama, K.; Nisar, M.A.; Huang, X.; Grzegorzek, M. Deep Transfer Learning for Time Series Data Based on Sensor Modality Classification. Sensors 2020, 20, 4271. [Google Scholar] [CrossRef] [PubMed]
- Fawaz, H.I.; Forestier, G.; Weber, J.; Idoumghar, L.; Muller, P.A. Transfer learning for time series classification. In Proceedings of the IEEE International Conference on Big Data, Seattle, WA, USA, 10–13 December 2018. [Google Scholar] [CrossRef] [Green Version]
- Kimura, N.; Yoshinaga, I.; Sekijima, K.; Azechi, I.; Baba, D. Convolutional Neural Network Coupled with a Transfer-Learning Approach for Time-Series Flood Predictions. Water 2020, 12, 96. [Google Scholar] [CrossRef] [Green Version]
- Engine Structure Image from Pngtree. Available online: https://pngtree.com/so/engine-structure (accessed on 1 June 2021).
- Borgwardt, K.M.; Gretton, A.; Rasch, M.J.; Kriegel, H.P.; Schölkopf, B.; Smola, A.J. Integrating structured biological data by kernel maximum mean discrepancy. Bioinformatics 2006, 22, 49–57. [Google Scholar] [CrossRef] [Green Version]
- Ioffe, S.; Szegedy, C. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. In Proceedings of the International Conference on Machine Learning (PMLR), Lille, France, 6–11 July 2015; Volume 37, pp. 448–456. [Google Scholar]
- Van Aswegen, A.S.; Engelbrecht, A.S. The relationship between transformational leadership, integrity and an ethical climate in organisations. SA J. Hum. Resour. Manag. 2009, 7, 9. [Google Scholar] [CrossRef]
- Chee, J.D. Pearson’s Product-Moment Correlation: Sample Analysis. Available online: https://www.academia.edu/21484289/PearsonsProduct-MomentCorrelationSampleAnalysis (accessed on 12 November 2020).
- Aminanto, M.E.; Choi, R.; Tanuwidjaja, H.C.; Yoo, P.D.; Kim, K. Deep Abstraction and Weighted Feature Selection for Wi-Fi Impersonation Detection. IEEE Trans. Inf. Forensics Secur. 2018, 12, 621–636. [Google Scholar] [CrossRef]
- Geller, S. Normalization vs. Standardization Quantitative Analysis. Available online: https://towardsdatascience.com/normalization-vs-standardization-quantitative-analysis-a91e8a79cebf (accessed on 9 November 2020).
- Geron, A. Hands-on Machine Learning with Scikit-Learn, Keras and TensorFlow; O’Reilly Media: Sebastopol, CA, USA, 2018. [Google Scholar]
- Guyon, I. A scaling law for the Validation-set training-set valid ratio. AT T Bell Lab. 1997, 1, 1–11. [Google Scholar]
- Szajna, A.; Kostrzewski, M.; Ciebiera, K.; Stryjski, R.; Wozniak, W. Application of the Deep CNN-Based Method in Industrial System for Wire Marking Identification. Energies 2021, 14, 3659. [Google Scholar] [CrossRef]
- Wu, X.; Kumar, V.J.; Quinlan, J.R.; Ghosh, J.; Yang, Q.; Motoda, H.; McLachlan, G.J.; Ng, A.; Liu, B.; Yu, P.S.; et al. Top 10 algorithms in data mining. Knowl. Inf. Syst. 2008, 14, 1–37. [Google Scholar] [CrossRef] [Green Version]
- Buczak, A.L.; Guven, E. A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection. IEEE Commun. Surv. Tutor. 2016, 18, 1153–1176. [Google Scholar] [CrossRef]
- Yang, Y.; Zheng, K.; Wu, C.; Niu, X.; Yang, Y. Building an Effective Intrusion Detection System Using the Modified Density Peak Clustering Algorithm and Deep Belief Networks. Appl. Sci. 2019, 9, 238. [Google Scholar] [CrossRef] [Green Version]
- Subba, B.; Biswas, S. Intrusion Detection Systems using Linear Discriminant Analysis and Logistic Regression. In Proceedings of the Annual IEEE India Conference (INDICON), New Delhi, India, 17–20 December 2015; pp. 1–6. [Google Scholar] [CrossRef]
- Nisioti, A.; Mylonas, A.; Yoo, P.D.; Katos, V. From Intrusion Detection to Attacker Attribution: A Comprehensive Survey of Unsupervised Methods. IEEE Commun. Surv. Tutor. 2018, 20, 3369–3388. [Google Scholar] [CrossRef]
- Goodfellow, I.; Bengio, Y.; Courville, A. Deep Learning; MIT Press: Cambridge, MA, USA, 2016. [Google Scholar]
- Brownlee, J. Gentle Introduction to the Adam Optimization Algorithm for Deep Learning. Available online: https://machinelearningmastery.com/adam-optimization-algorithm-for-deep-learning (accessed on 9 December 2020).
Ref. | Algorithm | Accuracy | Robustness | Detection Coverage |
---|---|---|---|---|
[23] | DL | >95% | High | N/A |
[24] | DL | >85% | Medium | DoS, Command Injection, Malware |
[25] | GAN | >95% | High | DoS, Fuzzing, RPM, Gear attacks |
[26] | DCAE | >90% | Medium | DoS, Fuzzing, Impersonation |
[27] | DL | >95% | High | Spoofing, Replay |
[28] | LSTM | >80% | Medium | Spoofing, Replay, Flooding |
[29] | ML | >90% | High | DoS, Fuzzing, Spoofing |
[30] | RNN | >95% | High | DoS, Fuzzing, Impersonation |
[31] | ML | >90% | Medium | DoS |
[32] | DL | >80% | N/A | DoS, Fuzzing, Impersonation |
[33] | RNN-LSTM | >95% | High | Spoofing |
[34] | NN-LSTM | >90% | N/A | DoS, Fuzzing, Spoofing |
[35] | DCNN | >80% | Medium | DoS, Fuzzing, RPM, Gear attacks |
[36] | DTL | >90% | High | Impersonation, ARP, Flooding |
Description | Source (s) | Target (t) |
---|---|---|
Domain data | ||
Domain feature | ||
Domain label | ||
Number of domain data | n | m |
Timestamp | CAN_ID | DLC | Data_Field | |
---|---|---|---|---|
Timestamp | ||||
CAN_ID | ||||
DLC | ||||
Data_Field |
Parameters | NN | RNN | CNN | LSTM |
---|---|---|---|---|
Number of hidden Layers | 2 | 3 | 4 | 3 |
Units in hidden layers | 68, 68 | 64, 64, 64 | 32, 64, 256, 128 | 128, 100, 64 |
Batch size | 64 | 64 | 64 | 16 |
Hidden layer activation | relu | relu | relu | tanh |
Output activation function | sigmoid | softmax | sigmoid | sigmoid |
Dropout | N/A | 0.1 | N/A | 0.2 |
Optimizer | Adam | Adam | Adam | Adam |
Parameters | FCN | IncepNet | ResNet | P-LeNet |
---|---|---|---|---|
Number of hidden Layers | 3 | 3 | 3 | 2 |
Units in hidden layers | 128, 256, 128 | 32, 64, 32 | 128, 256, 128 | 5, 20 |
Batch size | 64 | 64 | 64 | 64 |
Hidden layer activation | relu | linear | relu | relu |
Output activation function | softmax | softmax | softmax | softmax |
Dropout | N/A | N/A | 0.1 | N/A |
Optimizer | Adam | Adam | Adam | Adam |
Algorithm | Accuracy | Precision | Recall | F1-Score | ROC AUC |
---|---|---|---|---|---|
FCN | 0.9786 | 0.9832 | 0.9617 | 0.9488 | 0.9248 |
IncepNet | 0.9803 | 0.9152 | 0.9265 | 0.9024 | 0.9129 |
ResNet | 0.9795 | 0.8958 | 0.8845 | 0.9001 | 0.8703 |
LeNet | 0.9810 | 0.9814 | 0.9804 | 0.9783 | 0.9542 |
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Mehedi, S.T.; Anwar, A.; Rahman, Z.; Ahmed, K. Deep Transfer Learning Based Intrusion Detection System for Electric Vehicular Networks. Sensors 2021, 21, 4736. https://doi.org/10.3390/s21144736
Mehedi ST, Anwar A, Rahman Z, Ahmed K. Deep Transfer Learning Based Intrusion Detection System for Electric Vehicular Networks. Sensors. 2021; 21(14):4736. https://doi.org/10.3390/s21144736
Chicago/Turabian StyleMehedi, Sk. Tanzir, Adnan Anwar, Ziaur Rahman, and Kawsar Ahmed. 2021. "Deep Transfer Learning Based Intrusion Detection System for Electric Vehicular Networks" Sensors 21, no. 14: 4736. https://doi.org/10.3390/s21144736