Towards a Hybrid Machine Learning Model for Intelligent Cyber Threat Identification in Smart City Environments
Source abstract
Abstract
The concept of a smart city requires the integration of information and communication technologies and devices over a network for the better provision of services to citizens. As a result, the quality of living is improved by continuous analyses of data to improve service delivery by governments and other organizations. Due to the presence of extensive devices and data flow over networks, the probability of cyber attacks and intrusion detection has increased. The monitoring of this huge amount of data traffic is very difficult, though machine learning algorithms have huge potential to support this task. In this study, we compared different machine learning models used for cyber threat classification. Our comparison was focused on the analyzed cyber threats, algorithms, and performance of these models. We have identified that real-time classification, accuracy, and false-positive rates are still the major issues in the performance of existing models. Accordingly, we have proposed a hybrid deep learning (DL) model for cyber threat intelligence (CTI) to improve threat classification performance. Our model was based on a convolutional neural network (CNN) and quasi-recurrent neural network (QRNN). The use of QRNN not only resulted in improved accuracy but also enabled real-time classification. The model was tested on BoT-IoT and TON_IoT datasets, and the results showed that the proposed model outperformed the other models. Due to this improved performance, we emphasize that the application of this model in the real-time environment of a smart system network will help in reducing threats in a reasonable time.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Network Security and Intrusion Detection · Internet Traffic Analysis and Secure E-voting · Advanced Malware Detection Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
SAUDI ARAMCO Cybersecurity Chair, Department of Networks and Communications, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
References (60)
Show source-provided references
1. AlZaabi, K.A.J.A. (2019). The Value of Intelligent Cybersecurity Strategies for Dubai Smart City. Smart Technologies and Innovation for a Sustainable Future, Springer International Publishing.10.1007/978-3-030-01659-3_49 ↗
2. Behzadan, V., and Munir, A. (2018, January 16–19). Adversarial Exploitation of Emergent Dynamics in Smart Cities. Proceedings of the 2018 IEEE International Smart Cities Conference (ISC2), Kansas City, MO, USA.10.1109/ISC2.2018.8656789 ↗
3. Butt, T.A., and Afzaal, M. (2019). Security and Privacy in Smart Cities: Issues and Current Solutions. Smart Technologies and Innovation for a Sustainable Future, Springer International Publishing.10.1007/978-3-030-01659-3_37 ↗
4. Lee, J., Kim, J., and Seo, J. (2019, January 28–30). Cyber attack scenarios on smart city and their ripple effects. Proceedings of the 2019 International Conference on Platform Technology and Service (PlatCon), Jeju, Korea.10.1109/PlatCon.2019.8669431 ↗
5. Ahmad, F., Adnane, A., Franqueira, V.N.L., Kurugollu, F., and Liu, L. (2018). Man-In-The-Middle Attacks in Vehicular Ad-Hoc Networks: Evaluating the Impact of Attackers’ Strategies. Sensors, 18.10.3390/s18114040 ↗
6. Alibasic, A., Junaibi, R.A., Aung, Z., Woon, W.L., and Omar, M.A. (2017). Cybersecurity for Smart Cities: A Brief Review. International Workshop on Data Analytics for Renewable Energy Integration, Springer.10.1007/978-3-319-50947-1_3 ↗
7. Security and privacy challenges in smart cities10.1016/j.scs.2018.02.039 ↗
8. Security and Privacy in Smart Cities: Challenges and Opportunities10.1109/ACCESS.2018.2853985 ↗
9. Kettani, H., and Cannistra, R.M. (2018, January 18–20). On Cyber Threats to Smart Digital Environments. Proceedings of the 2nd International Conference on Smart Digital Environment, Rabat, Morocco.10.1145/3289100.3289130 ↗
10. Sookhak, M., Tang, H., and Yu, F.R. (2018, January 28–30). Security and Privacy of Smart Cities: Issues and Challenges. Proceedings of the 2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS), Exeter, UK.10.1109/HPCC/SmartCity/DSS.2018.00224 ↗
11. Cyberthreat-Intelligence Information Sharing: Enhancing Collaborative Security10.1109/MCE.2019.2892220 ↗
12. Security and Privacy in Smart City Applications: Challenges and Solutions10.1109/MCOM.2017.1600267CM ↗
13. Cyber Threat Intelligence—Issue and Challenges
14. Conti, M., Dehghantanha, A., and Dargahi, T. (2018). Cyberthreat intelligence: Challenges and opportunities. Cyber Threat Intelligence, Springer.10.1007/978-3-319-73951-9 ↗
15. Information Sharing of Cyber Threat Intelligence with their Issue and Challenges
16. Distributed attack detection scheme using deep learning approach for Internet of Things10.1016/j.future.2017.08.043 ↗
17. Berman, D.S., Buczak, A.L., Chavis, J.S., and Corbett, C.L. (2019). A Survey of Deep Learning Methods for Cyber Security. Information, 10.10.3390/info10040122 ↗
18. Identifying cyber threats to mobile-IoT applications in edge computing paradigm10.1016/j.future.2018.06.053 ↗
19. Internet of Things Cyber Attacks Detection using Machine Learning
20. Wu, P., and Guo, H. (2019, January 6–9). LuNet: A Deep Neural Network for Network Intrusion Detection. Proceedings of the 2019 IEEE Symposium Series on Computational Intelligence (SSCI), Xiamen, China.10.1109/SSCI44817.2019.9003126 ↗
21. Soe, Y.N., Feng, Y., Santosa, P.I., Hartanto, R., and Kouichi, S. (2020). Towards a Lightweight Detection System for Cyber Attacks in the IoT Environment Using Corresponding Features. Electronics, 9.10.3390/electronics9010144 ↗
22. Wu, P., Guo, H., and Moustafa, N. (July, January 29). Pelican: A Deep Residual Network for Network Intrusion Detection. Proceedings of the 2020 50th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W), Valencia, Spain.10.1109/DSN-W50199.2020.00018 ↗
23. Bradbury, J., Merity, S., Xiong, C., and Socher, R. (2017). Quasi-Recurrent Neural Networks. arXiv.
24. Elsaeidy, A., Munasinghe, K.S., Sharma, D., and Jamalipour, A. (2019, January 22–25). A Machine Learning Approach for Intrusion Detection in Smart Cities. Proceedings of the 2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall), Honolulu, HI, USA.10.1109/VTCFall.2019.8891281 ↗
25. IoT data feature extraction and intrusion detection system for smart cities based on deep migration learning10.1016/j.ijinfomgt.2019.04.006 ↗
26. Ge, M., Fu, X., Syed, N., Baig, Z., Teo, G., and Robles-kelly, A. (2019, January 1–3). Deep Learning-based Intrusion Detection for IoT Networks. Proceedings of the 2019 IEEE 24th Pacific Rim International Symposium on Dependable Computing (PRDC), Kyoto, Japan.10.1109/PRDC47002.2019.00056 ↗
27. Khraisat, A., Gondal, I., Vamplew, P., Kamruzzaman, J., and Alazab, A. (2019). A Novel Ensemble of Hybrid Intrusion Detection System for Detecting Internet of Things Attacks. Electronics, 8.10.3390/electronics8111210 ↗
28. Ullah, I., and Mahmoud, Q.H. (2020). A Two-Level Flow-Based Anomalous Activity Detection System for IoT Networks. Electronics, 9.10.3390/electronics9030530 ↗
29. Proactive Threat Detection for Connected Cars Using Recursive Bayesian Estimation10.1109/JSEN.2017.2782751 ↗
30. Cyber Threat Detection Based on Artificial Neural Networks Using Event Profiles10.1109/ACCESS.2019.2953095 ↗
31. A New Hybrid Machine Learning for Cybersecurity Threat Detection Based on Adaptive Boosting10.1080/08839514.2019.1582861 ↗
32. Thing, V.L.L. (2017, January 19–22). IEEE 802.11 Network Anomaly Detection and Attack Classification: A Deep Learning Approach. Proceedings of the 2017 IEEE Wireless Communications and Networking Conference (WCNC), San Francisco, CA, USA.10.1109/WCNC.2017.7925567 ↗
33. A Hybrid Deep Learning-Based Model for Anomaly Detection in Cloud Datacenter Networks10.1109/TNSM.2019.2927886 ↗
34. A hybrid deep learning model for efficient intrusion detection in big data environment10.1016/j.ins.2019.10.069 ↗
35. Vinayakumar, R., Kp, S., and Poornachandran, P. (2017, January 13–16). Applying Convolutional Neural Network for Network Intrusion Detection. Proceedings of the 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Udupi, India.10.1109/ICACCI.2017.8126009 ↗
36. HAST-IDS: Learning Hierarchical Spatial-Temporal Features Using Deep Neural Networks to Improve Intrusion Detection10.1109/ACCESS.2017.2780250 ↗
37. TP2SF: A Trustworthy Privacy-Preserving Secured Framework for sustainable smart cities by leveraging blockchain and machine learning10.1016/j.sysarc.2020.101954 ↗
38. Niu, X., Ma, J., Wang, Y., Zhang, J., Chen, H., and Tang, H. (2021). A Novel Decomposition-Ensemble Learning Model Based on Ensemble Empirical Mode Decomposition and Recurrent Neural Network for Landslide Displacement Prediction. Appl. Sci., 11.10.3390/app11104684 ↗
39. Deep learning10.1038/nature14539 ↗
40. Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study
41. Hasan, M.N., Toma, R.N., Nahid, A., Islam, M.M.M., and Kim, J. (2019). Electricity Theft Detection in Smart Grid Systems: A CNN-LSTM Based Approach. Energies, 12.10.3390/en12173310 ↗
42. Kwon, D., Natarajan, K., Suh, S.C., Kim, H., and Kim, J. (2018, January 2–6). An Empirical Study on Network Anomaly Detection using Convolutional Neural Networks. Proceedings of the In 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS), Vienna, Austria.10.1109/ICDCS.2018.00178 ↗
43. Knowledge-Based Systems CNN and RNN based payload classification methods for attack detection10.1016/j.knosys.2018.08.036 ↗
44. RNN-LSTM-GRU based language transformation10.1007/s00500-019-04281-z ↗
45. Bolelli, F., Baraldi, L., Pollastri, F., and Grana, C. (2018, January 12–14). A Hierarchical Quasi-Recurrent approach to Video Captioning. Proceedings of the 2018 IEEE International Conference on Image Processing, Applications and Systems (IPAS), Sophia Antipolis, France.10.1109/IPAS.2018.8708893 ↗
46. Wang, M., Wu, X., Wu, Z., Kang, S., Tuo, D., Li, G., Su, D., Yu, D., and Meng, H. (2019, January 12–17). Quasi-fully Convolutional Neural Network with Variational Inference for Speech Synthesis. Proceedings of the ICASSP 2019–2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK.10.1109/ICASSP.2019.8682528 ↗
47. Huang, J., and Feng, Y. (2019, January 23–25). Optimization of Recurrent Neural Networks on Natural Language Processing. Proceedings of the Proceedings of the 2019 8th International Conference on Computing and Pattern Recognition, New York, NY, USA.10.1145/3373509.3373573 ↗
48. Energy Theft Detection with Energy Privacy Preservation in the Smart Grid10.1109/JIOT.2019.2903312 ↗
49. Towards the Development of Realistic Botnet Dataset in the Internet of Things for Network Forensic Analytics: Bot-IoT Dataset10.1016/j.future.2019.05.041 ↗
50. Moustafa, N. (2019, January 16). TON_IoT Datasets. Proceedings of the IEEE Dataport, Brisbane, Australia.
51. Safa, H., Nassar, M., and Al Orabi, W.A.R. (2019, January 24–28). Benchmarking Convolutional and Recurrent Neural Networks for Malware Classification. Proceedings of the 2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC), Tangier, Morocco.10.1109/IWCMC.2019.8766515 ↗
52. Selection of effective machine learning algorithm and Bot-IoT attacks traffic identification for internet of things in smart city10.1016/j.future.2020.02.017 ↗
53. Deep Learning Approach for Intelligent Intrusion Detection System10.1109/ACCESS.2019.2895334 ↗
54. Obaidan, F.A., and Saeed, S. (2021). Digital Transformation and Cybersecurity Challenges: A Study of Malware Detection Using Machine Learning Techniques. Handbook of Research on Advancing Cybersecurity for Digital Transformation, IGI Global.10.4018/978-1-7998-6975-7.ch011 ↗
55. Malware detection in industrial internet of things based on hybrid image visualization and deep learning model10.1016/j.adhoc.2020.102154 ↗
56. Advanced security and privacy technique for digital text in smart grid communications10.1016/j.compeleceng.2021.107205 ↗
57. Serious games as a tool to model attack and defense scenarios for cyber-security exercises10.1016/j.cose.2021.102450 ↗
58. Poleto, T., Carvalho VD, H.D., Silva AL, B.D., Clemente TR, N., Silva, M.M., Gusmão AP, H.D., Costa, A.P.C.S., and Nepomuceno, T.C.C. (2021). Fuzzy cognitive scenario mapping for causes of cybersecurity in telehealth services. Healthcare, 9.10.3390/healthcare9111504 ↗
59. Shayan, S., Kim, K.P., Ma, T., and Nguyen, T.H.D. (2020). The first two decades of smart city research from a risk perspective. Sustainability, 12.10.3390/su12219280 ↗
60. Antecedents for enhanced level of cyber-security in organisations10.1108/JEIM-06-2020-0240 ↗