Improving Data Transmission Rate with Self Healing Activation Model for Intrusion Detection with Enhanced Quality of Service

Main Article Content

Simhadri Madhuri
S. Venkata Lakshmi

Abstract

Several types of attacks can easily compromise a Wireless Sensor Network (WSN). Although not all intrusions can be predicted, they may cause significant damage to the network and its nodes before being discovered. Due to its explosive growth and the infinite scope in terms of applications and processing brought about by 5G, WSN is becoming more and more deeply embedded in daily life. Security breaches, downed services, faulty hardware, and buggy software can all cripple these enormous systems. As a result, the platform becomes unmaintainable when there are a million or more interconnected devices. When it comes to network security, intrusion detection technology plays a crucial role, with its primary function being to constantly monitor the health of a network and, if any aberrant behavior is detected, to issue a timely warning to network administrators. The current network's availability and dependability are directly tied to the efficacy and timeliness of the Intrusion Detection System (IDS). An Intrusion-Tolerant system would incorporate self-healing mechanisms to restore compromised data. System attributes such as readiness for accurate service, supply identical and correct data, confidentiality, and availability are necessary for a system to merit trust. In this research, self-healing methods are considered that can detect intrusions and can remove with intellectual strategies that can make a system fully autonomous and fix any problems it encounters. In this study, a new architecture for an Intrusion Tolerant Self Healing Activation Model for Improved Data Transmission Rate (ITSHAM-IDTR) is proposed for accurate detection of intrusions and self repairing the network for better performance, which boosts the server's performance quality and enables it to mend itself without any intervention from the administrator. When compared to the existing paradigm, the proposed model performs in both self-healing and increased data transmission rates..

Article Details

How to Cite
Madhuri, S. ., & Lakshmi, S. V. . (2023). Improving Data Transmission Rate with Self Healing Activation Model for Intrusion Detection with Enhanced Quality of Service. International Journal on Recent and Innovation Trends in Computing and Communication, 11(9s), 233–243. https://doi.org/10.17762/ijritcc.v11i9s.7417
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Articles

References

X. -Y. Kong and G. -H. Yang, "An Intrusion Detection Method Based on Self-Generated Coding Technology for Stealthy False Data Injection Attacks in Train-Ground Communication Systems," in IEEE Transactions on Industrial Electronics, vol. 70, no. 8, pp. 8468-8476, Aug. 2023, doi: 10.1109/TIE.2022.3213899.

M. E. Aminanto, R. S. H. Wicaksono, A. E. Aminanto, H. C. Tanuwidjaja, L. Yola and K. Kim, "Multi-Class Intrusion Detection Using Two-Channel Color Mapping in IEEE 802.11 Wireless Network," in IEEE Access, vol. 10, pp. 36791-36801, 2022, doi: 10.1109/ACCESS.2022.3164104.

S. Subbiah, K. S. M. Anbananthen, S. Thangaraj, S. Kannan and D. Chelliah, "Intrusion detection technique in wireless sensor network using grid search random forest with Boruta feature selection algorithm," in Journal of Communications and Networks, vol. 24, no. 2, pp. 264-273, April 2022, doi: 10.23919/JCN.2022.000002.

M. Ozkan-Okay, Ö. Aslan, R. Eryigit and R. Samet, "SABADT: Hybrid Intrusion Detection Approach for Cyber Attacks Identification in WLAN," in IEEE Access, vol. 9, pp. 157639-157653, 2021, doi: 10.1109/ACCESS.2021.3129600.

J. Wang, Z. Tian, M. Zhou, J. Wang, X. Yang and X. Liu, "Leveraging Hypothesis Testing for CSI Based Passive Human Intrusion Direction Detection," in IEEE Transactions on Vehicular Technology, vol. 70, no. 8, pp. 7749-7763, Aug. 2021, doi: 10.1109/TVT.2021.3090800.

R. Zhao et al., "An Efficient Intrusion Detection Method Based on Dynamic Autoencoder," in IEEE Wireless Communications Letters, vol. 10, no. 8, pp. 1707-1711, Aug. 2021, doi: 10.1109/LWC.2021.3077946.

Gajare, M. ., & Shedge, D. K. . (2023). CMOS Trans conductance Based Instrumentation Amplifier for Various Biomedical signal Analysis. International Journal of Intelligent Systems and Applications in Engineering, 11(3s), 63–71. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2532

M. Dener, S. Al and A. Orman, "STLGBM-DDS: An Efficient Data Balanced DoS Detection System for Wireless Sensor Networks on Big Data Environment," in IEEE Access, vol. 10, pp. 92931-92945, 2022, doi: 10.1109/ACCESS.2022.3202807.

Y. Wu, L. Nie, S. Wang, Z. Ning and S. Li, "Intelligent Intrusion Detection for Internet of Things Security: A Deep Convolutional Generative Adversarial Network-Enabled Approach," in IEEE Internet of Things Journal, vol. 10, no. 4, pp. 3094-3106, 15 Feb.15, 2023, doi: 10.1109/JIOT.2021.3112159.

P. Freitas De Araujo-Filho, A. J. Pinheiro, G. Kaddoum, D. R. Campelo and F. L. Soares, "An Efficient Intrusion Prevention System for CAN: Hindering Cyber-Attacks With a Low-Cost Platform," in IEEE Access, vol. 9, pp. 166855-166869, 2021, doi: 10.1109/ACCESS.2021.3136147.

G. A. N. Segura, A. Chorti and C. B. Margi, "Centralized and Distributed Intrusion Detection for Resource-Constrained Wireless SDN Networks," in IEEE Internet of Things Journal, vol. 9, no. 10, pp. 7746-7758, 15 May15, 2022, doi: 10.1109/JIOT.2021.3114270.

Y. Du, J. Xia, J. Ma and W. Zhang, "An Optimal Decision Method for Intrusion Detection System in Wireless Sensor Networks With Enhanced Cooperation Mechanism," in IEEE Access, vol. 9, pp. 69498-69512, 2021, doi: 10.1109/ACCESS.2021.3065571.

A.Ghasempour, "Internet of Things in smart grid: Architecture applications services key technologies and challenges", Inventions, vol. 4, no. 1, pp. 22, Mar. 2019.

M. Nivaashini and P. Thangaraj, "Computational intelligence techniques for automatic detection of Wi-Fi attacks in wireless IoT networks", Wireless Netw., vol. 27, no. 4, pp. 2761-2784, May 2021.

M. Hassaballah, M. A. Hameed, A. I. Awad and K. Muhammad, "A novel image steganography method for industrial Internet of Things security", IEEE Trans. Ind. Informat., vol. 17, no. 11, pp. 7743-7751, Nov. 2021.

Z. Ahmad, A. Shahid Khan, C. Wai Shiang, J. Abdullah and F. Ahmad, "Network intrusion detection system: A systematic study of machine learning and deep learning approaches", Trans. Emerg. Telecommun. Technol., vol. 32, no. 1, pp. e4150, Jan. 2021.

S. N. Mighan and M. Kahani, "A novel scalable intrusion detection system based on deep learning", Int. J. Inf. Secur., vol. 20, no. 3, pp. 387-403, Jun. 2021.

A.Thakkar and R. Lohiya, "A survey on intrusion detection system: Feature selection model performance measures application perspective challenges and future research directions", Artif. Intell. Rev., vol. 55, pp. 453-563, Jul. 2021.

S. M. Kasongo and Y. Sun, "A deep learning method with wrapper based feature extraction for wireless intrusion detection system", Comput. Secur., vol. 92, May 2020.

M. E. Aminanto, R. Choi, H. C. Tanuwidjaja, P. D. Yoo and K. Kim, "Deep abstraction and weighted feature selection for Wi-Fi impersonation detection", IEEE Trans. Inf. Forensics Security, vol. 13, no. 3, pp. 621-636, Mar. 2018.

L. Jing and Y. Tian, "Self-supervised visual feature learning with deep neural networks: A survey", IEEE Trans. Pattern Anal. Mach. Intell., vol. 43, no. 11, pp. 4037-4058, Nov. 2021.

R. S. H. Wicaksono, A. A. Septiandri and A. Jamal, "Human embryo classification using self-supervised learning", Proc. 2nd Int. Conf. Artif. Intell. Data Sci. (AiDAS), pp. 1-5, Sep. 2021.

H. Firat and D. Hanbay, "Classification of hyperspectral images using 3D CNN based ResNet50", Proc. 29th Signal Process. Commun. Appl. Conf. (SIU), pp. 1-4, Jun. 2021.

Simhadri Madhuri, S Venkata Lakshmi, “ Detecting Emotion from Natural Language Text Using Hybrid and NLP Pre-trained Models”, “Turkish Journal of Computer and Mathematics Education” Vol.12 No.10 (2021), 4095-4103, doi: 10.17762/turcomat.v12i10.5122

María, K., Järvinen, M., Dijk, A. van, Huber, K., & Weber, S. Machine Learning Approaches for Curriculum Design in Engineering Education. Kuwait Journal of Machine Learning, 1(1). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/111

A.A. Septiandri, A. Jamal, P. A. Iffanolida, O. Riayati and B. Wiweko, " Human blastocyst classification after in vitro fertilization using deep learning ", Proc. 7th Int. Conf. Advance Inform. Concepts Theory Appl. (ICAICTA), pp. 1-4, Sep. 2020.

Q. Duan, X. Wei, J. Fan, L. Yu and Y. Hu, "CNN-based intrusion classification for IEEE 802.11 wireless networks", Proc. IEEE 6th Int. Conf. Comput. Commun. (ICCC), pp. 830-833, Dec. 2020.

Al-Turaiki and N. Altwaijry, "A convolutional neural network for improved anomaly-based network intrusion detection", Big Data, vol. 9, no. 3, pp. 233-252, Jun. 2021.

L. Pan and X. Xie, "Network intrusion detection model based on PCA+ADASYN and XGBoost", Proc. 3rd Int. Conf. E-Bus. Inf. Manage. Comput. Sci., pp. 44-48, Dec. 2020.

Chang Lee, Deep Learning for Speech Recognition in Intelligent Assistants , Machine Learning Applications Conference Proceedings, Vol 1 2021.

M. Ozkan-Okay and R. Samet, "Hybrid intrusion detection approach for wireless local area network", Proc. 7th Int. Conf. Control Optim. Ind. Appl., pp. 311-313, 2020.

Ö. Aslan, M. Ozkan-Okay and D. Gupta, "A review of cloud-based malware detection system: Opportunities advances and challenges", Eur. J. Eng. Technol. Res., vol. 6, no. 3, pp. 1-8, Mar. 2021.

S Venkata Lakshmi, Valli Kumari Vatsavayi “Query optimization using clustering and Genetic Algorithm for Distributed Databases”, International Conference on Computer Communication and Informatics (ICCCI). IEEE, 2016, doi: 10.1109/ICCCI.2016.7479934.

S Venkata Lakshmi, Valli Kumari Vatsavayi “Teacher-Learner & Multi-Objective Genetic Algorithm Based Query Optimization Approach For Heterogeneous Distributed Database Systems”, Journal of Theoretical and Applied Information Technology, April 2017.

Dhabliya, D. (2021). An Integrated Optimization Model for Plant Diseases Prediction with Machine Learning Model . Machine Learning Applications in Engineering Education and Management, 1(2), 21–26. Retrieved from http://yashikajournals.com/index.php/mlaeem/article/view/15

Sunita A Yadwad, Dr V. Valli Kumari and Dr S Venkata Lakshmi. Service Outages Prediction through Logs and Tickets Analysis. (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 12, No. 4, 2021, doi: 10.14569/IJACSA.2021.0120424.

Simhadri Madhuri, S Venkata Lakshmi, “A Trusted Node Feedback Based Clustering Model For Detection Of Malicious Nodes In The Network”, Journal of Theoretical and Applied Information Technology, Vol.101. No 7, 2023

Simhadri Madhuri, S Venkata Lakshmi, “A machine learning-based normalized fuzzy subset linked model in networks for intrusion detection”, Soft Computing, 2023, doi: 10.1007/s00500-023-08160-6