A Comparative Study on Deep Learning-Based Algorithms For Intruder Detection Systems and Cyber Security
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Abstract
For data protection, the most vital factors are the statistics' safety, use of cryptographic controls during data transmission, an effective access management system, and powerful tracking. This paper seeks to provide a committed evaluation of the very current studies works on using Deep studying strategies to remedy computer security demanding situations. In this study, we analyzed and reviewed using deep learning algorithms for the Intruder detection system and Cybersecurity programs. Deep learning consists of system-mastering strategies that permit the network to learn from unsupervised data and solve complicated problems. Deep learning approaches such as Convolutional Neural Network (CNN), Auto Encoder (AE), Deep Belief Network (DBN), Recurrent Neural Network (RNN), Generative Adversal Network (GAN), and Deep Reinforcement Learning (DIL) are used to categorize the papers referred. This paper discusses various challenges, issues, and types of cyber-attacks and security measures
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Shrivastava, V., & Kamble, M. (2023). A Comparative Study on Deep Learning-Based Algorithms For Intruder Detection Systems and Cyber Security. SAMRIDDHI : A Journal of Physical Sciences, Engineering and Technology, 15(01), 154-160. https://doi.org/10.18090/samriddhi.v15i01.30
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Review Article
References
1] Aldweesh, A., Derhab, A., & Emam, A. Z. (2020). Deep learning approaches for anomaly-based intrusion detection systems: A survey, taxonomy, and open issues. Knowledge-Based
Systems, 189, 105124. https://doi.org/https://doi.org/10.1016/j.
knosys.2019.105124
[2] Alqahtani, A. S. (2022). FSO-LSTM IDS: hybrid optimized and ensembled deep-learning network-based intrusion detection system for smart networks. The Journal of Supercomputing.
https://doi.org/10.1007/s11227-021-04285-3
[3] Alrowaily, M. (2020). Investigation of Machine Learning Algorithms for Intrusion Detection System in Cybersecurity.
[Digital Commons @ University of South Florida ].
[4] Andresini, G., Appice, A., Mauro, N. Di, Loglisci, C., & Malerba, D. (2020). Multi-Channel Deep Feature Learning for Intrusion Detection. IEEE Access, 8, 53346–53359. https://doi.org/10.1109/ ACCESS.2020.2980937
[5] Asharf, J., Moustafa, N., Khurshid, H., Debie, E., Haider, W., & Wahab, A. (2020). A Review of Intrusion Detection Systems Using Machine and Deep Learning in Internet of Things: Challenges, Solutions and Future Directions. Electronics, 9(7). https://doi.
org/10.3390/electronics9071177
[6] Ashraf, I., Narra, M., Umer, M., Majeed, R., Sadiq, S., Javaid, F., & Rasool, N. (2022). A Deep Learning-Based Smart Framework for Cyber-Physical and Satellite System Security Threats Detection.
Electronics, 11(4). https://doi.org/10.3390/electronics11040667 [7] Atul, D. J., Kamalraj, R., Ramesh, G., Sakthidasan Sankaran, K., Sharma, S., & Khasim, S. (2021). A machine learning based IoT for providing an intrusion detection system for security.
Microprocessors and Microsystems, 82, 103741. https://doi.org/ https://doi.org/10.1016/j.micpro.2020.103741
[8] Basnet, M., & Hasan Ali, M. (2020). Deep Learning-based Intrusion Detection System for Electric Vehicle Charging Station.
2020 2nd International Conference on Smart Power Internet Energy Systems (SPIES), 408–413. https://doi.org/10.1109/ SPIES48661.2020.9243152
[9] Choi, Y.-H., Liu, P., Shang, Z., Wang, H., Wang, Z., Zhang, L., Zhou, J., & Zou, Q. (2020). Using deep learning to solve computer security challenges: a survey. Cybersecurity, 3(1), 15. https:// doi.org/10.1186/s42400-020-00055-5 [10] Ferrag, M. A., Maglaras, L., Moschoyiannis, S., & Janicke, H.
(2020). Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study. Journal of
Information Security and Applications, 50, 102419. https://doi.
org/https://doi.org/10.1016/j.jisa.2019.102419
[11] Gottumukkala, R., Merchant, R., Tauzin, A., Leon, K., Roche, A., & Darby, P. (2019). Cyber-physical System Security of Vehicle Charging Stations. 2019 IEEE Green Technologies
Conference(GreenTech), 1–5. https://doi.org/10.1109/
GreenTech.2019.8767141
[12] Jiang, K., Wang, W., Wang, A., & Wu, H. (2020). Network Intrusion Detection Combined Hybrid Sampling With Deep Hierarchical Network. IEEE Access, 8, 32464–32476. https://doi.org/10.1109/ ACCESS.2020.2973730
[13] Kilincer, I. F., Ertam, F., & Sengur, A. (2021). Machine learning methods for cyber security intrusion detection: Datasets and comparative study. Computer Networks, 188, 107840. https:// doi.org/https://doi.org/10.1016/j.comnet.2021.107840
[14] Kshirsagar, D., & Kumar, S. (2022). Towards an intrusion detection system for detecting web attacks based on an ensemble of filter feature selection techniques. Cyber-Physical Systems, 0(0), 1–16.
https://doi.org/10.1080/23335777.2021.2023651
[15] Lansky, J., Ali, S., Mohammadi, M., Majeed, M. K., Karim, S. H.
T., Rashidi, S., Hosseinzadeh, M., & Rahmani, A. M. (2021). Deep Learning-Based Intrusion Detection Systems: A Systematic
Review. IEEE Access, 9, 101574–101599. https://doi.org/10.1109/ ACCESS.2021.3097247
[16] Lee, B., Amaresh, S., Green, C., Engels, D., & Engels, D. W. (2018). SMU Data Science Review Comparative Study of Deep Learning Models for Network Intrusion Detection Comparative Study of Deep Learning Models for Network Intrusion Detection. Other Computer Engineering Commons, Other Computer Sciences SMU Data Science Review, 1(1).
https://scholar.smu.
edu/datasciencereviewAvailableat:https://scholar.smu.edu/ datasciencereview/vol1/iss1/8
[17] Li, D., Deng, L., Lee, M., & Wang, H. (2019). IoT data feature extraction and intrusion detection system for smart cities based on deep migration learning. International Journal of Information Management, 49, 533–545. https://doi.org/https://
doi.org/10.1016/j.ijinfomgt.2019.04.006
[18] Nalluri, S. K., Parasaram, V. K. B., & Bathini, V. T. (2021). Autonomous Manufacturing Operations Using Intelligent MES and Cloud-Native Analytics. Journal of Multidisciplinary Knowledge, 1(1), 45–55.
Retrieved from
https://jmk.datatablets.com/index.php/j/article/view/127
[19] Li, W., Meng, W., & Au, M. H. (2020). Enhancing collaborative intrusion detection via disagreement-based semi-supervised learning in IoT environments. Journal of Network and Computer Applications, 161, 102631. https://doi.org/https:// doi.org/10.1016/j.jnca.2020.102631 [20] Li, Y., Xia, J., Zhang, S., Yan, J., Ai, X., & Dai, K. (2012). An efficient intrusion detection system based on support vector machines and gradually feature removal method. Expert Systems with Applications, 39(1), 424–430. https://doi.org/https://doi.
org/10.1016/j.eswa.2011.07.032
[21] Linda, O., Vollmer, T., & Manic, M. (2009). Neural Network based Intrusion Detection System for critical infrastructures. 2009 International Joint Conference on Neural Networks, 1827–1834.
https://doi.org/10.1109/IJCNN.2009.5178592
[22] Network Intrusion Detection | Kaggle. (n.d.). Retrieved January 24, 2023, from https://www.kaggle.com/datasets/sampadab17/ networkintrusiondetection
[23] Pan, Z., Pacheco, J., Hariri, S., Chen, Y., & Liu, B. (2019). Context Aware Anomaly Behavior Analysis for Smart Home Systems. 13(5), 261–274.
http://waset.org/publications/10010351/pdf
[24] Papamartzivanos, D., Gomez Marmol, F., & Kambourakis, G. (2019). Introducing Deep Learning Self-Adaptive Misuse Network Intrusion Detection Systems. IEEE Access, 7(c), 13546– 13560.
https://doi.org/10.1109/ACCESS.2019.2893871
[25] Rahman, M. A., Asyhari, T., Leong, L. S., Satrya, G., Tao, M., & Zolkipli, M. (2020). Scalable Machine Learning-Based Intrusion Detection System for IoT-Enabled Smart Cities. Sustainable Cities and Society, 61, 102324. https://doi.org/10.1016/j. scs.2020.102324
[26] Sangkatsanee, P., Wattanapongsakorn, N., & Charnsripinyo, C.
(2011). Practical real-time intrusion detection using machine learning approaches. Computer Communications, 34(18), 2227–2235.
https://doi.org/https://doi.org/10.1016/j. comcom.2011.07.001 [27] Sarker, I. H., Colman, A., Han, J., Khan, A. I., Abushark, Y. B., & Salah, K.
(2020). BehavDT: A Behavioral Decision Tree Learning to Build UserCentric Context-Aware Predictive Model. Mobile Networks and Applications, 25(3), 1151–1161. https://doi. org/10.1007/s11036-01901443-z
[28] Sarker, I. H., Abushark, Y. B., Alsolami, F., & Khan, A. I. (2020).
IntruDTree: A Machine Learning Based Cyber Security Intrusion Detection Model. Symmetry, 12(5). https://doi.org/10.3390/ sym12050754
[29] Satish Kumar Nalluri, Venkata Krishna Bharadwaj Parasaram. (2019).
Software-Centric Automation Frameworks Integrating AI and Cybersecurity Principles. International Journal of Engineering Science & Humanities, 9(1), 30–40. Retrieved from
https://www.ijesh.com/j/article/view/539
[30] Nalluri, S. K., Parasaram, V. K. B., & Bathini, V. T. (2021). Autonomous Manufacturing Operations Using Intelligent MES and Cloud-Native Analytics. Journal of Multidisciplinary Knowledge, 1(1), 45–55.
Retrieved from
https://jmk.datatablets.com/index.php/j/article/view/127
[31] Transforming Diagnostics Manufacturing at Cepheid: Migration from Paper-Based Processes to Digital Manufacturing using Opcenter MES.
(2022). International Journal of Research and Applied Innovations, 5(1), 9451-9456. https://doi.org/10.15662/ IJRAI.2022.0501005
[32] Shojafar, M., Taheri, R., Pooranian, Z., Javidan, R., Miri, A., & Jararweh, Y. (2019). Automatic Clustering of Attacks in Intrusion Detection Systems. 2019 IEEE/ACS 16th International Conference on Computer Systems and Applications (AICCSA), 1–8.
https://doi.org/10.1109/AICCSA47632.2019.9035238
[33] Tertytchny, G., Nicolaou, N., & Michael, M. K. (2020). Classifying network abnormalities into faults and attacks in IoT-based cyber physical systems using machine learning. Microprocessors and
Microsystems, 77, 103121. https://doi.org/https://doi.
org/10.1016/j.micpro.2020.103121
[34] Wang, W., Harrou, F., Bouyeddou, B., Senouci, S.-M., & Sun, Y. (2022).
A stacked deep learning approach to cyber-attacks detection in industrial systems: application to power system and gas pipeline systems. Cluster Computing, 25(1), 561–578.
https://doi.org/10.1007/s10586-021-03426-w
[35] Yang, L., Cao, X., & Geng, X. (2019). A novel intelligent assessment method for SCADA information security risk based on causality analysis. Cluster Computing, 22(3), 5491–5503. https://doi.
org/10.1007/s10586-017-1315-4
[36] Zegeye, W. K., Dean, R. A., & Moazzami, F. (2019). Multi-Layer Hidden Markov Model Based Intrusion Detection System. Machine Learning and Knowledge Extraction, 1(1), 265–286.
https://doi.org/10.3390/make1010017
[37] Zhang, J., Wang, W., Lu, C., Wang, J., & Sangaiah, A. K. (2020).
Lightweight deep network for traffic sign classification. Annals of Telecommunications, 75(7), 369–379. https://doi.org/10.1007/ s12243019-00731-9
[38] Zhang, R., Condomines, J.-P., & Lochin, E. (2022). A Multifractal Analysis and Machine Learning Based Intrusion Detection System with an Application in a UAS/RADAR System. Drones, 6(1).
https://doi.org/10.3390/drones6010021
[39] Zhou, X., Hu, Y., Liang, W., Ma, J., & Jin, Q. (2021). Variational LSTM Enhanced Anomaly Detection for Industrial Big Data. IEEE
Transactions on Industrial Informatics, 17(5), 3469–3477.https://doi.org/10.1109/TII.2020.3022432
Systems, 189, 105124. https://doi.org/https://doi.org/10.1016/j.
knosys.2019.105124
[2] Alqahtani, A. S. (2022). FSO-LSTM IDS: hybrid optimized and ensembled deep-learning network-based intrusion detection system for smart networks. The Journal of Supercomputing.
https://doi.org/10.1007/s11227-021-04285-3
[3] Alrowaily, M. (2020). Investigation of Machine Learning Algorithms for Intrusion Detection System in Cybersecurity.
[Digital Commons @ University of South Florida ].
[4] Andresini, G., Appice, A., Mauro, N. Di, Loglisci, C., & Malerba, D. (2020). Multi-Channel Deep Feature Learning for Intrusion Detection. IEEE Access, 8, 53346–53359. https://doi.org/10.1109/ ACCESS.2020.2980937
[5] Asharf, J., Moustafa, N., Khurshid, H., Debie, E., Haider, W., & Wahab, A. (2020). A Review of Intrusion Detection Systems Using Machine and Deep Learning in Internet of Things: Challenges, Solutions and Future Directions. Electronics, 9(7). https://doi.
org/10.3390/electronics9071177
[6] Ashraf, I., Narra, M., Umer, M., Majeed, R., Sadiq, S., Javaid, F., & Rasool, N. (2022). A Deep Learning-Based Smart Framework for Cyber-Physical and Satellite System Security Threats Detection.
Electronics, 11(4). https://doi.org/10.3390/electronics11040667 [7] Atul, D. J., Kamalraj, R., Ramesh, G., Sakthidasan Sankaran, K., Sharma, S., & Khasim, S. (2021). A machine learning based IoT for providing an intrusion detection system for security.
Microprocessors and Microsystems, 82, 103741. https://doi.org/ https://doi.org/10.1016/j.micpro.2020.103741
[8] Basnet, M., & Hasan Ali, M. (2020). Deep Learning-based Intrusion Detection System for Electric Vehicle Charging Station.
2020 2nd International Conference on Smart Power Internet Energy Systems (SPIES), 408–413. https://doi.org/10.1109/ SPIES48661.2020.9243152
[9] Choi, Y.-H., Liu, P., Shang, Z., Wang, H., Wang, Z., Zhang, L., Zhou, J., & Zou, Q. (2020). Using deep learning to solve computer security challenges: a survey. Cybersecurity, 3(1), 15. https:// doi.org/10.1186/s42400-020-00055-5 [10] Ferrag, M. A., Maglaras, L., Moschoyiannis, S., & Janicke, H.
(2020). Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study. Journal of
Information Security and Applications, 50, 102419. https://doi.
org/https://doi.org/10.1016/j.jisa.2019.102419
[11] Gottumukkala, R., Merchant, R., Tauzin, A., Leon, K., Roche, A., & Darby, P. (2019). Cyber-physical System Security of Vehicle Charging Stations. 2019 IEEE Green Technologies
Conference(GreenTech), 1–5. https://doi.org/10.1109/
GreenTech.2019.8767141
[12] Jiang, K., Wang, W., Wang, A., & Wu, H. (2020). Network Intrusion Detection Combined Hybrid Sampling With Deep Hierarchical Network. IEEE Access, 8, 32464–32476. https://doi.org/10.1109/ ACCESS.2020.2973730
[13] Kilincer, I. F., Ertam, F., & Sengur, A. (2021). Machine learning methods for cyber security intrusion detection: Datasets and comparative study. Computer Networks, 188, 107840. https:// doi.org/https://doi.org/10.1016/j.comnet.2021.107840
[14] Kshirsagar, D., & Kumar, S. (2022). Towards an intrusion detection system for detecting web attacks based on an ensemble of filter feature selection techniques. Cyber-Physical Systems, 0(0), 1–16.
https://doi.org/10.1080/23335777.2021.2023651
[15] Lansky, J., Ali, S., Mohammadi, M., Majeed, M. K., Karim, S. H.
T., Rashidi, S., Hosseinzadeh, M., & Rahmani, A. M. (2021). Deep Learning-Based Intrusion Detection Systems: A Systematic
Review. IEEE Access, 9, 101574–101599. https://doi.org/10.1109/ ACCESS.2021.3097247
[16] Lee, B., Amaresh, S., Green, C., Engels, D., & Engels, D. W. (2018). SMU Data Science Review Comparative Study of Deep Learning Models for Network Intrusion Detection Comparative Study of Deep Learning Models for Network Intrusion Detection. Other Computer Engineering Commons, Other Computer Sciences SMU Data Science Review, 1(1).
https://scholar.smu.
edu/datasciencereviewAvailableat:https://scholar.smu.edu/ datasciencereview/vol1/iss1/8
[17] Li, D., Deng, L., Lee, M., & Wang, H. (2019). IoT data feature extraction and intrusion detection system for smart cities based on deep migration learning. International Journal of Information Management, 49, 533–545. https://doi.org/https://
doi.org/10.1016/j.ijinfomgt.2019.04.006
[18] Nalluri, S. K., Parasaram, V. K. B., & Bathini, V. T. (2021). Autonomous Manufacturing Operations Using Intelligent MES and Cloud-Native Analytics. Journal of Multidisciplinary Knowledge, 1(1), 45–55.
Retrieved from
https://jmk.datatablets.com/index.php/j/article/view/127
[19] Li, W., Meng, W., & Au, M. H. (2020). Enhancing collaborative intrusion detection via disagreement-based semi-supervised learning in IoT environments. Journal of Network and Computer Applications, 161, 102631. https://doi.org/https:// doi.org/10.1016/j.jnca.2020.102631 [20] Li, Y., Xia, J., Zhang, S., Yan, J., Ai, X., & Dai, K. (2012). An efficient intrusion detection system based on support vector machines and gradually feature removal method. Expert Systems with Applications, 39(1), 424–430. https://doi.org/https://doi.
org/10.1016/j.eswa.2011.07.032
[21] Linda, O., Vollmer, T., & Manic, M. (2009). Neural Network based Intrusion Detection System for critical infrastructures. 2009 International Joint Conference on Neural Networks, 1827–1834.
https://doi.org/10.1109/IJCNN.2009.5178592
[22] Network Intrusion Detection | Kaggle. (n.d.). Retrieved January 24, 2023, from https://www.kaggle.com/datasets/sampadab17/ networkintrusiondetection
[23] Pan, Z., Pacheco, J., Hariri, S., Chen, Y., & Liu, B. (2019). Context Aware Anomaly Behavior Analysis for Smart Home Systems. 13(5), 261–274.
http://waset.org/publications/10010351/pdf
[24] Papamartzivanos, D., Gomez Marmol, F., & Kambourakis, G. (2019). Introducing Deep Learning Self-Adaptive Misuse Network Intrusion Detection Systems. IEEE Access, 7(c), 13546– 13560.
https://doi.org/10.1109/ACCESS.2019.2893871
[25] Rahman, M. A., Asyhari, T., Leong, L. S., Satrya, G., Tao, M., & Zolkipli, M. (2020). Scalable Machine Learning-Based Intrusion Detection System for IoT-Enabled Smart Cities. Sustainable Cities and Society, 61, 102324. https://doi.org/10.1016/j. scs.2020.102324
[26] Sangkatsanee, P., Wattanapongsakorn, N., & Charnsripinyo, C.
(2011). Practical real-time intrusion detection using machine learning approaches. Computer Communications, 34(18), 2227–2235.
https://doi.org/https://doi.org/10.1016/j. comcom.2011.07.001 [27] Sarker, I. H., Colman, A., Han, J., Khan, A. I., Abushark, Y. B., & Salah, K.
(2020). BehavDT: A Behavioral Decision Tree Learning to Build UserCentric Context-Aware Predictive Model. Mobile Networks and Applications, 25(3), 1151–1161. https://doi. org/10.1007/s11036-01901443-z
[28] Sarker, I. H., Abushark, Y. B., Alsolami, F., & Khan, A. I. (2020).
IntruDTree: A Machine Learning Based Cyber Security Intrusion Detection Model. Symmetry, 12(5). https://doi.org/10.3390/ sym12050754
[29] Satish Kumar Nalluri, Venkata Krishna Bharadwaj Parasaram. (2019).
Software-Centric Automation Frameworks Integrating AI and Cybersecurity Principles. International Journal of Engineering Science & Humanities, 9(1), 30–40. Retrieved from
https://www.ijesh.com/j/article/view/539
[30] Nalluri, S. K., Parasaram, V. K. B., & Bathini, V. T. (2021). Autonomous Manufacturing Operations Using Intelligent MES and Cloud-Native Analytics. Journal of Multidisciplinary Knowledge, 1(1), 45–55.
Retrieved from
https://jmk.datatablets.com/index.php/j/article/view/127
[31] Transforming Diagnostics Manufacturing at Cepheid: Migration from Paper-Based Processes to Digital Manufacturing using Opcenter MES.
(2022). International Journal of Research and Applied Innovations, 5(1), 9451-9456. https://doi.org/10.15662/ IJRAI.2022.0501005
[32] Shojafar, M., Taheri, R., Pooranian, Z., Javidan, R., Miri, A., & Jararweh, Y. (2019). Automatic Clustering of Attacks in Intrusion Detection Systems. 2019 IEEE/ACS 16th International Conference on Computer Systems and Applications (AICCSA), 1–8.
https://doi.org/10.1109/AICCSA47632.2019.9035238
[33] Tertytchny, G., Nicolaou, N., & Michael, M. K. (2020). Classifying network abnormalities into faults and attacks in IoT-based cyber physical systems using machine learning. Microprocessors and
Microsystems, 77, 103121. https://doi.org/https://doi.
org/10.1016/j.micpro.2020.103121
[34] Wang, W., Harrou, F., Bouyeddou, B., Senouci, S.-M., & Sun, Y. (2022).
A stacked deep learning approach to cyber-attacks detection in industrial systems: application to power system and gas pipeline systems. Cluster Computing, 25(1), 561–578.
https://doi.org/10.1007/s10586-021-03426-w
[35] Yang, L., Cao, X., & Geng, X. (2019). A novel intelligent assessment method for SCADA information security risk based on causality analysis. Cluster Computing, 22(3), 5491–5503. https://doi.
org/10.1007/s10586-017-1315-4
[36] Zegeye, W. K., Dean, R. A., & Moazzami, F. (2019). Multi-Layer Hidden Markov Model Based Intrusion Detection System. Machine Learning and Knowledge Extraction, 1(1), 265–286.
https://doi.org/10.3390/make1010017
[37] Zhang, J., Wang, W., Lu, C., Wang, J., & Sangaiah, A. K. (2020).
Lightweight deep network for traffic sign classification. Annals of Telecommunications, 75(7), 369–379. https://doi.org/10.1007/ s12243019-00731-9
[38] Zhang, R., Condomines, J.-P., & Lochin, E. (2022). A Multifractal Analysis and Machine Learning Based Intrusion Detection System with an Application in a UAS/RADAR System. Drones, 6(1).
https://doi.org/10.3390/drones6010021
[39] Zhou, X., Hu, Y., Liang, W., Ma, J., & Jin, Q. (2021). Variational LSTM Enhanced Anomaly Detection for Industrial Big Data. IEEE
Transactions on Industrial Informatics, 17(5), 3469–3477.https://doi.org/10.1109/TII.2020.3022432