Cyber Hacking Breaches Prediction Using Cat Boost Machine Learning

Authors

  • Gurram Kesavakrishna  Department of Computer Science and Engineering, Siddhartha Educational Academy Groups of Institutions, Tirupathi, Andhra Pradesh, India
  • N. Anand Reddy  Department of Computer Science and Engineering, Siddhartha Educational Academy Groups of Institutions, Tirupathi, Andhra Pradesh, India

Keywords:

Deep learning, Emotion recognition, Bi-LSTM, CNN, Formal text, Emotional states.

Abstract

Poetry and formal texts have gotten less attention in recent years from experts in artificial intelligence than informal textual content such as SMS, email, chat, and online user reviews. Using Deep Learning, this study proposes a text-based emotional state categorization system. The text corpus is used to construct an attention-based Bi-LSTM model and CNN, further which are compared for their accuracy. There are a number of distinct emotional states that which can be classified from the text using the suggested method. These states include neutral, joy, fear, sadness.

References

  1. Kwon, Cheolhyeon, Weiyi Liu, and Inseok Hwang. ”Security analysis for cyber-physical systems against stealthy deception attacks.” In 2013 American control conference, IEEE (2013): 3344-3349.
  2. Pajic, Miroslav, James Weimer, Nicola Bezzo, Oleg Sokolsky, George J. Pappas, and Insup Lee. ”Design and implementation of attack-resilient cyberphysical systems: With a focus on attack-resilient state estimators.” IEEE Control Systems Magazine 37, no. 2 (2017): 66-81.
  3. Sheng, Long, Ya-Jun Pan, and Xiang Gong. ”Consensus formation control for a class of networked multiple mobile robot systems.” Journal of Control Science and Engineering 2012 (2012).
  4. Zeng, Wente, and Mo-Yuen Chow. ”Resilient distributed control in the presence of misbehaving agents in networked control systems.” IEEE transactions on cybernetics 44, no. 11 (2014): 2038-2049.
  5. Sun, Hongtao, Chen Peng, Taicheng Yang, Hao Zhang, and Wangli He. ”Resilient control of networked control systems with stochastic denial of service attacks.” Neurocomputing 270 (2017): 170-177.
  6. Zhang, Haotian, and Shreyas Sundaram. ”Robustness of information diffusion algorithms to locally bounded adversaries.” In 2012 American Control Conference (ACC), IEEE (2012): 5855-5861.
  7. Fu, Weiming, Jiahu Qin, Yang Shi, Wei Xing Zheng, and Yu Kang. ”Resilient Consensus of Discrete-Time Complex Cyber-Physical Networks under Deception Attacks.” IEEE Transactions on Industrial Informatics (2019).
  8. Ozay, Mete, Inaki Esnaola, Fatos Tunay Yarman Vural, Sanjeev R. Kulkarni, and H. Vincent Poor. ”Machine learning methods for attack detection in the smart grid.” IEEE transactions on neural networks and learning systems 27, no. 8 (2015): 1773-1786.
  9. Tianfield, Huaglory. ”Data mining based cyber-attack detection.” System simulation technology 13, no. 2 (2017): 90-104.
  10. Pasqualetti, Fabio, Florian Dorfler, and Francesco Bullo. ”Attack detection and ¨ identification in cyber-physical systems.” IEEE Transactions on Automatic Control 58, no. 11 (2013): 2715-2729.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

Downloads

Published

2022-12-30

Issue

Volume 8, Issue 6, November-December-2022

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Gurram Kesavakrishna, N. Anand Reddy, " Cyber Hacking Breaches Prediction Using Cat Boost Machine Learning" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 8, Issue 6, pp.156-164, November-December-2022.