ARTIFICIAL NEURAL NETWORKS IN CYBERSECURITY: A HOLISTIC OVERVIEW OF INNOVATIONS AND APPLICATIONS

*Anushree Anil
Acharya Bangalore B School, Dept. of Computer Science, BCA, Bangalore,
Karnataka, India; Email Id – anushreeanil6@gmail.com

C.Y Maanya Patel
Acharya Bangalore B School, Dept. of Computer Science, BCA, Bangalore,
Karnataka, India; Email id- pattpatel05@gmail.com

Bhoomika Kumbhar Shrikant
Acharya Bangalore B School, Dept. of Computer Science, BCA, Bangalore,
Karnataka, India; Email Id- bhoomikakumbhar8@gmail.com.

Chetan B.S
Assistant Professor, Acharya Bangalore B School, Dept. of Computer Science, BCA,
Bangalore; Email Id – chetanbs90@gmail.com

Abstract 

Cybersecurity is an essential field focused on protecting systems, networks, and data from digital threats. With the rapid growth of the Internet and the rise in cyberattacks, particularly on Internet of Things (IoT) networks, developing advanced tools is crucial. This paper provides an overview of deep learning (DL) techniques applied in cybersecurity, including methods like deep belief networks, generative adversarial networks, and recurrent neural networks, comparing them to traditional shallow learning approaches. It also highlights the growing cyberattacks in IoT networks and the effectiveness of DL methods in mitigating these threats. Studies discussed include DL applications in malware detection, classification, intrusion detection, and cyberattacks like spam and network traffic. For instance, restricted Boltzmann machines achieved 99.72% accuracy, while LSTM reached 99.80% on the KDD Cup 99 dataset. The paper concludes by emphasizing the role of cybersecurity in ensuring the reliability of IoT- driven healthcare systems and improving security outcomes.

Keywords: Cybersecurity, Deep Learning, Internet of Things (IoT), Malware Detection, Intrusion Detection.

Citation of this paper: Anil, A., Patel, C. Y. M., Shrikant, B. K., & B.S, C. (2025). Artificial neural networks in cybersecurity: A holistic overview of innovations and applications. VLEARNY Journal of Business, 2(1), 33–41. https://doi.org/10.5281/zenodo.14873930

References:

• Buczak, L.; Guven, E. A Survey of Data Mining and Machine Learning Methods for Cyber Security. IEEE Commun. Surv. Tutor. 2016, 18, 1153–1176.
• Milenkoski, M. Vieira, S. Kounev, A. Avritzer, and B. D. Payne. Evaluating computer intrusion detection systems: A survey of commonpractices. ACM Computer Surv., vol. 48, no. 1, pp. 1-41, 2015.
• N. Modi and K. Acha. Virtualization layer security challenges and intrusion detection/prevention systems in cloud computing: Acomprehensive review. J. Supercomputer, vol. 73, no. 3, pp. 1192-1234, 2017.
• Nguyen, T.T.T.; Armitage, G. A survey of techniques for internet traffic classification using machine learning. IEEE Commun. Surv. Tutor. 2008, 10, 56–76.
• Viegas, A. O. Santin, A. França, R. Jasinski, V. A. Pedroni, and L. S. Oliveira. Towards an energy-efficient anomaly- based intrusiondetection engine for embedded systems,” IEEE Trans. Computer., vol. 66, no. 1, pp. 163-177, Jan. 2017.
• Patcha and J.-M. Park, “An overview of anomaly detection techniques: Existing solutions and latest technological trends,” Comput. Netw., vol. 51, no. 12, pp. 34483470, Aug. 2007.
• Sperotto, ; Schaffrath, G.; Sadre, R.; Morariu, C.; Pras, A.; Stiller, B. An overview of IP flow-based intrusion detection. IEEE Commun. Surv. Tutor. 2010, 12, 343–356.
• Wu, X.; Banzhaf, W. The use of computational intelligence in intrusion detection systems: A review. Appl. Soft Comput. 2010, 10, 1–35. [CrossRef]
• Torres, J.M.; Comesaña, C.I.; García-Nieto, P.J. Machine learning techniques applied to cybersecurity. Int. J. Mach. Learn. 2019, 1–14.
• Xin, ; Kong, L.; Liu, Z.; Chen, Y.; Li, Y.; Zhu, H.; Gao, M.;

Hou, H.;Wang, C. Machine Learning and Deep Learning Methods for Cybersecurity. IEEE Access 2018, 6, 35365–

35381.

• Bharati, ; Podder, P.; Mondal, M.; Robel, M. and Alam, R.; Threats and countermeasures of cyber security in direct and remote vehiclecommunication systems. Journal of Information Assurance & Security. 2020, 15(4), 153-164. 2020.
• Al-Garadi, A.; Mohamed, A.; Al-Ali, A.; Du, X.; Guizani,

1807. A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security. arXiv 2018, arXiv:1807.11023.

• El Hihi, ; Bengio, Y. Hierarchical recurrent neural networks for long-term dependencies. In Advances in Neural Information Processing Systems; MIT Press: Cambridge, MA, USA, 1996;

499. 493–499.

• Sutskever, ; Vinyals, O.; Le, Q.V. Sequence to sequence learning with neural networks. In Advances in Neural Information Processing Systems; MIT Press: Cambridge, MA, USA, 2014; pp. 3104–3112.
• Berman, S.; Buczak, A.L.; Chavis, J.S.; Corbett, C.L. A Survey of Deep Learning Methods for Cyber Security. Information 2019, 10, 122.
• Hochreiter, ; Schmidhuber, J. Long short-term memory. Neural Comput. 1997, 9, 1735–1780.
• Sainath, N.; Mohamed, A.R.; Kingsbury, B.; Ramabhadran,

8618. Deep convolutional neural networks for LVCSR. In Proceedings of the 2013 IEEE International Conference Acoustics, Speech and Signal Processing (ICASSP), Vancouver, BC, Canada, 26–31 May 2013; pp. 8614–8618.

VLEARNY Journal of Business
2 (1) 2025, 33-41, https://vlearny.com/journal/
© VLERNY Technology LLP.