Abstract
The evolution of computer networks and Internet of Things (IoT) in various fields increases the privacy, and security concerns. The increased usage of network-related applications demands a cost-efficient cyber security framework to protect the system from attackers. In this article, an optimized neural-based cyber security model named Golden Eagle-based Dense Neural System (GEbDNS) was designed to detect the intrusion in the network. Initially, the network dataset CICIDS 2017 was collected and imported into the network. The dataset contains both normal and abnormal data. The raw dataset was pre-processed to eliminate the training flaws and errors and the important data features are extracted. Further, the optimal data features are selected for detection phase using the optimal solution of golden eagle optimization. Then, the selected data features are matched with the trained attack data for attack classification. Finally, the results are evaluated and verified with existing techniques in terms of accuracy, true-positive rate, and false-positive rate. The experimental analysis states that the developed security framework outperforms the traditional schemes with greater accuracy of 98.45%.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Arauz, T., Chanfreut, P., Maestre, J.M.: Cyber-security in networked and distributed model predictive control. Annu. Rev. Control 53, 338–355 (2022)
Al-Sanjary, O.I., et al.: Challenges on digital cyber-security and network forensics: a survey. In: Advances on Intelligent Informatics and Computing: Health Informatics, Intelligent Systems, Data Science and Smart Computing, pp. 524–537. Springer International Publishing, Cham (2022)
Mandru, D.B., et al.: Assessing deep neural network and shallow for network intrusion detection systems in cyber security. In: Computer Networks and Inventive Communication Technologies: Proceedings of Fourth ICCNCT 2021. Springer Singapore (2022)
Zhu, J., et al.: A few-shot meta-learning based siamese neural network using entropy features for ransomware classification. Comput. Sec. 117, 102691 (2022)
Ullah, I., Mahmoud, Q.H.: An anomaly detection model for IoT networks based on flow and flag features using a feed-forward neural network. In: 2022 IEEE 19th Annual Consumer Communications and Networking Conference (CCNC). IEEE (2022)
Lo, W.W., et al.: E-graphsage: a graph neural network-based intrusion detection system for iot. In: NOMS 2022–2022 IEEE/IFIP Network Operations and Management Symposium. IEEE (2022)
Tekerek, A., Yapici, M.M.: A novel malware classification and augmentation model based on convolutional neural network. Comput. Sec. 112, 102515 (2022)
Kanna, P.R., Santhi, P.: Hybrid intrusion detection using mapreduce based black widow optimized convolutional long short-term memory neural networks. Exp. Syst. Appl. 194, 116545 (2022)
Gehlot, A., et al.: Application of neural network in the prediction models of machine learning based design. In: 2022 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES). IEEE (2022)
Zhang, Z., et al.: Artificial intelligence in cyber security: research advances, challenges, and opportunities. Artif. Intell. Rev. 1–25 (2022)
Evangelou, M., Adams, N.M.: An anomaly detection framework for cyber-security data. Comput. Sec. 97, 101941 (2020)
Hossein, M.R., et al.: Anomaly detection in cyber-physical systems using machine learning. In: Handbook of Big Data Privacy, pp. 219–235 (2020)
Jia, Y., et al.: Adversarial attacks and mitigation for anomaly detectors of cyber-physical systems. Int. J. Crit. Infrast. Prot. 34, 100452 (2021)
Alguliyev, R., Imamverdiyev, Y., Sukhostat, L.: Hybrid DeepGCL model for cyber-attacks detection on cyber-physical systems. Neural Comput. Appl. 33(16), 10211–10226 (2021)
Acknowledgements
The authors would like to thank the reviewers for all of their careful, constructive and insightful comments in relation to this work.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Veerasamy, B., Nageswari, D., Kumar, S.N., Shirgire, A., Sitharthan, R., Jasmine Gnana Malar, A. (2023). An Optimized Cyber Security Framework for Network Applications. In: Bhateja, V., Carroll, F., Tavares, J.M.R.S., Sengar, S.S., Peer, P. (eds) Intelligent Data Engineering and Analytics. FICTA 2023. Smart Innovation, Systems and Technologies, vol 371. Springer, Singapore. https://doi.org/10.1007/978-981-99-6706-3_45
Download citation
DOI: https://doi.org/10.1007/978-981-99-6706-3_45
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-6705-6
Online ISBN: 978-981-99-6706-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)