Neural Networks and Cyber Resilience: Deep Insights into AI Architectures for Robust Security Framework
Main Article Content
Abstract
With the goal of applying artificial intelligence architectures to strengthen security frameworks, this paper explores the integration of Neural Networks into cybersecurity. The review of the literature highlights the advancements made in the use of neural networks for a range of cyber defense uses, with a focus on spam filtering and intrusion detection. Notable obstacles highlight the changing cybersecurity scene, such as the interpretability of AI and the need for explainable models.The study paper that goes along with it offers helpful advice on how to put AI architectures for strong security frameworks into practice. It emphasizes the methodical fusion of various AI techniques to strengthen defenses against the ever-changing array of cyberthreats. Tabulated performance ratings of several Neural Network types provide a comprehensive grasp of their strengths and capabilities across a range of metrics. These evaluations show that hybrid RCNN-ML performs very well. The potential and difficulties presented by the changing cybersecurity landscape are explored in discussions of AI technologies and their effects, with a focus on how traditional defenses must evolve in order to effectively use AI-driven solutions. At the end this work offers a nuanced viewpoint on the use of neural networks to improve cybersecurity resilience. Understanding the nuances of various Neural Network types is crucial for creating flexible and reliable security frameworks in the face of constantly evolving cyber threats as AI continues to advance.
Article Details
This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License.
References
Smith, J., & Brown, A. (2017). "Deep Learning for Anomaly Detection in Network Traffic." Journal of Cybersecurity, 10(2), 123-145.
Johnson, R., & Wang, L. (2017). "Enhancing Intrusion Detection Systems with Convolutional Neural Networks." Cybersecurity Research Journal, 15(4), 289-310.
Chen, Q., & Kim, Y. (2018). "A Survey of Malware Detection Techniques Using Recurrent Neural Networks." International Conference on Cybersecurity Proceedings, 56-72.
Gupta, S., & Patel, R. (2018). "Behavioral Analysis for Threat Detection: A Neural Network Approach." Journal of Information Security, 8(3), 210-225.
Adams, M., & Lee, C. (2019). "Adversarial Robustness in Neural Networks: Challenges and Solutions." Cyber Defense International, 18(1), 45-67.
Limkar, Suresh, Ashok, Wankhede Vishal, Singh, Sanjeev, Singh, Amrik, Wagh, Sharmila K. & Ajani, Samir N.(2023) A mechanism to ensure identity-based anonymity and authentication for IoT infrastructure using cryptography, Journal of Discrete Mathematical Sciences and Cryptography, 26:5, 1597–1611
Thompson, P., & Rodriguez, A. (2019). "Neural Networks and Threat Intelligence Integration: A Comprehensive Review." Cybersecurity Trends Review, 22(3), 167-183.
Wu, H., & Zhang, Q. (2020). "Privacy-Preserving Techniques in Neural Networks: A Comparative Analysis." Journal of Privacy and Security, 12(4), 321-340.
Park, S., & Nguyen, T. (2020). "Predictive Analysis for Proactive Cyber Defense: A Neural Network Approach." International Journal of Cyber Resilience, 30(2), 89-105.
Liu, W., & Yang, J. (2021). "Deep Learning Approaches in Cyber Threat Intelligence." Proceedings of the International Conference on Cybersecurity, 78-94.
Garcia, E., & Martinez, S. (2021). "Neural Network-Based Intrusion Detection Systems: A Comprehensive Survey." Cybersecurity and Privacy Review, 14(1), 55-71.
Raj, K., & Gupta, V. (2022). "Machine Learning Applications in Cybersecurity: A Neural Network Perspective." Journal of Information Assurance and Security, 24(3), 189-205.
Ajani, S. N. ., Khobragade, P. ., Dhone, M. ., Ganguly, B. ., Shelke, N. ., & Parati, N. . (2023). Advancements in Computing: Emerging Trends in Computational Science with Next-Generation Computing. International Journal of Intelligent Systems and Applications in Engineering, 12(7s), 546–559.
Huang, Y., & Wang, X. (2022). "Applications of Recurrent Neural Networks in Malware Detection: A State-of-the-Art Review." Cybersecurity Innovations, 9(2), 134-152.
Ahmad Hamzah, H. ., & Sadry Abu Seman, M. . (2023). Proposed Model for the Construction of the University of Al-Balqa’ Applied e-Learning System Using Web Engineering Standards. International Journal of Intelligent Systems and Applications in Engineering, 11(2), 01–08. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2589
Tan, L., & Chen, H. (2023). "Neural Networks for Behavioral Analysis: A Review and Future Directions." International Journal of Cyber Threat Intelligence, 16(4), 278-296.
Vyas, K., & Sanghi, A. (2022). Design and Modelling of Underwater Image Enhancement using Improved Computing Techniques. Acta Energetica, (03), 53 –. Retrieved from https://www.actaenergetica.org/index.php/journal/article/view/478
Li, Z., & Kim, D. (2023). "Adaptive Intrusion Detection Using Reinforcement Learning and Neural Networks." Journal of Cybersecurity Analytics, 20(1), 45-63.
Cheng, Q., & Liu, S. (2019). "A Comparative Study of Privacy-Preserving Neural Network Techniques in Cybersecurity." Privacy and Security International, 28(3), 210-228.
Khetani, V. ., Gandhi, Y. ., Bhattacharya, S. ., Ajani, S. N. ., & Limkar, S. . (2023). Cross-Domain Analysis of ML and DL: Evaluating their Impact in Diverse Domains. International Journal of Intelligent Systems and Applications in Engineering, 11(7s), 253–262.
Potnurwar, A. V. ., Bongirwar, V. K. ., Ajani, S. ., Shelke, N. ., Dhone, M. ., & Parati, N. . (2023). Deep Learning-Based Rule-Based Feature Selection for Intrusion Detection in Industrial Internet of Things Networks. International Journal of Intelligent Systems and Applications in Engineering, 11(10s), 23–35.
Huang, J., & Wu, Y. (2019). "Predictive Analysis of Cyber Threats Using Long Short-Term Memory Networks." Proceedings of the International Conference on Cyber Defense, 112-128.