Cyber-security attack prediction using cognitive spectral clustering technique based on simulated annealing search

Research Article
Open access

Cyber-security attack prediction using cognitive spectral clustering technique based on simulated annealing search

N.R. Rajalakshmi 1 , Sathishkumar V. E. 2 , C.Kannika Parameshwari 3 , Maheshwari V. 4* , Prasanna M. 5
  • 1 Hanyang University    
  • 2 NPR College of Engineering and Technology    
  • 3 NPR College of Engineering and Technology    
  • 4 Vellore Institute of Technology    
  • 5 Vellore Institute of Technology    
  • *corresponding author maheshwarivenkat24@gmail.com
Published on 14 June 2023 | https://doi.org/10.54254/2755-2721/6/20230791
ACE Vol.6
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-59-1
ISBN (Online): 978-1-915371-60-7

Abstract

Data protection and security is a big challenging portion in a modern technical world against the cyber-attacks like; ransomware, man-in-the-middle, DDoS, etc. In order to overcome this scenario, there are lot of artificial intelligence framework have been introduced to detect and classify the cyber-attacks. In particular, neural networks, with their solid speculation execution ability, are capable to address an extensive variety of cyber-attacks. This article frames the training and testing of a neural network group such a way to deal with detection of cyber-attack using cognitive spectral clustering technique based on simulated annealing search method. The optimization of individual networks can be made by using adaptive memetic algorithm with simulated annealing search. It is used to enhance the neural network weights and hidden neurons respectively. This algorithm is a combination of both local and global search enhancement method and used to get rid of the premature convergence, and used to achieve the adaptive search output. The testing outcome of the proposed framework shows a better result 99.5% of overall accuracy, and effectively adaptive in terms of detecting the cyber-attacks.

Keywords:

cyber-attack, cognitive spectral clustering, artificial intelligence, simulated annealing search, neural network cluster.

Rajalakshmi,N.;E.,S.V.;Parameshwari,C.;V.,M.;M.,P. (2023). Cyber-security attack prediction using cognitive spectral clustering technique based on simulated annealing search. Applied and Computational Engineering,6,1360-1365.
Export citation

References

[1]. W. K. AL-Rashdan, R. Naoum, and A. S. Wafa'S, "Novel network intrusion detection system using hybrid neural network (Hopfield and Kohonen SOM with conscience function)," IJCSNS, vol. 10, no. 11, p. 10, 2010.

[2]. Alves LGA, Ribeiro HV, Rodrigues FA. 2018. Crime prediction through urban metrics and statistical learning. Physica A: Statistical Mechanics and its Applications 505:435–443.

[3]. Arora T, Sharma M, Khatri SK. 2019. Detection of cyber-crime on social media using random forest algorithm. In: 2nd International Conference on Power Energy, Environment and Intelligent Control (PEEIC). Piscataway: IEEE, 47–51.

[4]. Bharathi ST, Indrani B, Prabakar MA. 2017. A supervised learning approach for criminal identification using similarity measures and K-Medoids clustering. In: ICICICT. Piscataway: IEEE, 646–653.

[5]. Kuzlu, M., C. Fair, and O. Guler, Role of artificial intelligence in the Internet of Things (IoT) cybersecurity. Discover Internet of things, 2021. 1 (1): p. 1-14.

[6]. Truong, T. C., et al., Artificial intelligence and cybersecurity: Past, presence, and future, in Artificial intelligence and evolutionary computations in engineering systems. 2020, Springer. p. 351-363.

[7]. Azim, A. W., Bazzi, A., Shubair, R., & Chafii, M. (2022). Dual-Mode Chirp Spread Spectrum Modulation. IEEE Wireless Communications Letters, 1-1. doi:10.1109/LWC.2022.3190564.

[8]. Bazzi, A., & Meilhac, L. (2022). Method for decoding an rf signal bearing a sequence of symbols modulated by cpm and associated decoder: Google Patents.

[9]. Bazzi, A., & Slock, D. (2020). Robust Music Estimation Under Array Response Uncertainty. Paper presented at the ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[10]. Njima, W., Bazzi, A., & Chafii, M. (2022). DNN-based Indoor Localization Under Limited Dataset using GANs and Semi-Supervised Learning. IEEE Access, 10, 69896-69909.

[11]. Reddy, V. A., Bazzi, A., Stuber, G. L., Al-Dharrab, S., Mesbah, W., & Muqaibel, A. H. (2020). ¨ Fundamental Performance Limits of mm-Wave Cooperative Localization in Linear Topologies. IEEE Wireless Communications Letters, 9 (11), 1899-1903.

[12]. Ghelani, D., & Hua, T. K. (2022). Conceptual Framework of Web 3.0 and Impact on Marketing, Artificial Intelligence, and Blockchain. International Journal of Information and Communication Sciences, 7 (1), 10.

Cite this article

Rajalakshmi,N.;E.,S.V.;Parameshwari,C.;V.,M.;M.,P. (2023). Cyber-security attack prediction using cognitive spectral clustering technique based on simulated annealing search. Applied and Computational Engineering,6,1360-1365.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

Disclaimer/Publisher's Note

The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

About volume

Volume title: Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

ISBN：978-1-915371-59-1(Print) / 978-1-915371-60-7(Online)
Editor：Omer Burak Istanbullu
Conference website: http://www.confspml.org
Conference date: 25 February 2023
Series: Applied and Computational Engineering
Volume number: Vol.6
ISSN：2755-2721(Print) / 2755-273X(Online)

© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Authors who publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open access policy for details).

References

[1]. W. K. AL-Rashdan, R. Naoum, and A. S. Wafa'S, "Novel network intrusion detection system using hybrid neural network (Hopfield and Kohonen SOM with conscience function)," IJCSNS, vol. 10, no. 11, p. 10, 2010.

[2]. Alves LGA, Ribeiro HV, Rodrigues FA. 2018. Crime prediction through urban metrics and statistical learning. Physica A: Statistical Mechanics and its Applications 505:435–443.

[3]. Arora T, Sharma M, Khatri SK. 2019. Detection of cyber-crime on social media using random forest algorithm. In: 2nd International Conference on Power Energy, Environment and Intelligent Control (PEEIC). Piscataway: IEEE, 47–51.

[4]. Bharathi ST, Indrani B, Prabakar MA. 2017. A supervised learning approach for criminal identification using similarity measures and K-Medoids clustering. In: ICICICT. Piscataway: IEEE, 646–653.

[5]. Kuzlu, M., C. Fair, and O. Guler, Role of artificial intelligence in the Internet of Things (IoT) cybersecurity. Discover Internet of things, 2021. 1 (1): p. 1-14.

[6]. Truong, T. C., et al., Artificial intelligence and cybersecurity: Past, presence, and future, in Artificial intelligence and evolutionary computations in engineering systems. 2020, Springer. p. 351-363.

[7]. Azim, A. W., Bazzi, A., Shubair, R., & Chafii, M. (2022). Dual-Mode Chirp Spread Spectrum Modulation. IEEE Wireless Communications Letters, 1-1. doi:10.1109/LWC.2022.3190564.

[8]. Bazzi, A., & Meilhac, L. (2022). Method for decoding an rf signal bearing a sequence of symbols modulated by cpm and associated decoder: Google Patents.

[9]. Bazzi, A., & Slock, D. (2020). Robust Music Estimation Under Array Response Uncertainty. Paper presented at the ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[10]. Njima, W., Bazzi, A., & Chafii, M. (2022). DNN-based Indoor Localization Under Limited Dataset using GANs and Semi-Supervised Learning. IEEE Access, 10, 69896-69909.

[11]. Reddy, V. A., Bazzi, A., Stuber, G. L., Al-Dharrab, S., Mesbah, W., & Muqaibel, A. H. (2020). ¨ Fundamental Performance Limits of mm-Wave Cooperative Localization in Linear Topologies. IEEE Wireless Communications Letters, 9 (11), 1899-1903.

[12]. Ghelani, D., & Hua, T. K. (2022). Conceptual Framework of Web 3.0 and Impact on Marketing, Artificial Intelligence, and Blockchain. International Journal of Information and Communication Sciences, 7 (1), 10.

For authors
Guide for authors Policies for authors Aims and scope Subscription Open access policy
For editors
Policies for editors Editorial board
For reviewers
Policies for reviewers Privacy policy

Cookies are used by this site.
Copyright © 2024 EWA Publishing, its licensors, and contributors. All rights reserved, including text and data mining, AI training, and similar technologies.