Leveraging Artificial Intelligence for Predictive Cyber Threat Intelligence

Main Article Content

Dr. Vinod Varma Vegesna
Ashwin Adepu

Abstract

As cyber threats become increasingly complex and dynamic, traditional reactive security measures are proving inadequate. This paper proposes a predictive cyber threat intelligence (CTI) framework powered by artificial intelligence (AI). By leveraging machine learning algorithms and natural language processing (NLP), our framework analyzes vast amounts of data from open-source intelligence (OSINT) and dark web sources to predict potential threats. We present a detailed evaluation of our system, highlighting its accuracy in threat prediction and its ability to provide actionable insights. Our research underscores the importance of AI in developing proactive security measures and its potential to transform CTI practices.

Downloads

Download data is not yet available.

Article Details

How to Cite
Leveraging Artificial Intelligence for Predictive Cyber Threat Intelligence (D. V. V. Vegesna & A. Adepu , Trans.). (2024). International Journal of Creative Research In Computer Technology and Design, 6(6), 1-19. https://jrctd.in/index.php/IJRCTD/article/view/64
Section
Articles

How to Cite

Leveraging Artificial Intelligence for Predictive Cyber Threat Intelligence (D. V. V. Vegesna & A. Adepu , Trans.). (2024). International Journal of Creative Research In Computer Technology and Design, 6(6), 1-19. https://jrctd.in/index.php/IJRCTD/article/view/64

References

Feurer, M., Klein, A., Eggensperger, K., Springenberg, J. T., Blum, M., & Hutter, F. (2015). Efficient and robust automated machine learning. In Advances in neural information processing systems (pp. 2962-2970).

Ribeiro, M. T., Singh, S., & Guestrin, C. (2019). "Why Should I Trust You?" Explaining the Predictions of Any Classifier. arXiv preprint arXiv:1602.04938.

Lantz, E., Vukadinovic Greetham, D., Akerkar, R., & Duesing, N. (2020). Scalable Automated Machine Learning with H2O. In International Conference on Intelligent Data Engineering and Automated Learning (pp. 348-358). Springer, Cham.

Hutter, F., Kotthoff, L., & Vanschoren, J. (2019). Automated machine learning: Methods, systems, challenges. Springer.

Friedman, J. H. (2019). Data mining and statistics: what's the connection?. In Proceedings of the fifteenth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 3-3).

Chen, T., Li, M., Li, Y., Lin, M., Wang, N., Wang, M., ... & Guo, Y. (2020). Feature-engine: A python library to automate feature engineering. Journal of Open Source Software, 5(47), 2035.

Agarwal, R., Doppa, J. R., & Fern, A. (2018). MACHIDA: A meta-learning based method for automated algorithm selection and hyperparameter tuning. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 32, No. 1).

Smith, L. N. (2017). Cyclical learning rates for training neural networks. In 2017 IEEE Winter Conference on Applications of Computer Vision (WACV) (pp. 464-472). IEEE.

Brownlee, J. (2017). Deep learning for time series forecasting: Predict the Future with MLPs, CNNs and LSTMs in Python. Machine Learning Mastery.

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., ... & Vanderplas, J. (2011). Scikit-learn: Machine learning in Python. Journal of machine learning research, 12(Oct), 2825-2830.

Hinton, G., Srivastava, N., & Swersky, K. (2012). Lecture 6a Overview of mini-batch gradient descent. Coursera: Neural networks for machine learning, 4(2), 14.

Bengio, Y., Courville, A., & Vincent, P. (2013). Representation learning: A review and new perspectives. IEEE transactions on pattern analysis and machine intelligence, 35(8), 1798-1828.

Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.

Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: A simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research, 15(1), 1929-1958.

LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436-444.

Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780.

Chollet, F., & Allaire, J. J. (2018). Deep learning with R. Manning Publications Co..

Goodfellow, I., Bengio, Y., Courville, A., & Bengio, Y. (2016). Deep learning (Vol. 1). MIT press Cambridge.

Zeiler, M. D. (2012). ADADELTA: An adaptive learning rate method. arXiv preprint arXiv:1212.5701.

Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 779-788).

Bishop, C. M. (2006). Pattern recognition and machine learning. springer.

Chollet, F. (2017). Xception: Deep learning with depthwise separable convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1251-1258).

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need. In Advances in neural information processing systems (pp. 5998-6008).

Bojarski, M., Del Testa, D., Dworakowski, D., Firner, B., Flepp, B., Goyal, P., ... & Zhang, X. (2016). End to end learning for self-driving cars. arXiv preprint arXiv:1604.07316.

Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780.

Whig, P., Silva, N., Elngar, A. A., Aneja, N., & Sharma, P. (Eds.). (2023). Sustainable Development through Machine Learning, AI and IoT: First International Conference, ICSD 2023, Delhi, India, July 15–16, 2023, Revised Selected Papers. Springer Nature.

Channa, A., Sharma, A., Singh, M., Malhotra, P., Bajpai, A., & Whig, P. (2024). Original Research Article Revolutionizing filmmaking: A comparative analysis of conventional and AI-generated film production in the era of virtual reality. Journal of Autonomous Intelligence, 7(4).

Jain, A., Kamat, S., Saini, V., Singh, A., & Whig, P. (2024). Agile Leadership: Navigating Challenges and Maximizing Success. In Practical Approaches to Agile Project Management (pp. 32-47). IGI Global.

Whig, P., Remala, R., Mudunuru, K. R., & Quraishi, S. J. (2024). Integrating AI and Quantum Technologies for Sustainable Supply Chain Management. In Quantum Computing and Supply Chain Management: A New Era of Optimization (pp. 267-283). IGI Global.

Mittal, S., Koushik, P., Batra, I., & Whig, P. (2024). AI-Driven Inventory Management for Optimizing Operations With Quantum Computing. In Quantum Computing and Supply Chain Management: A New Era of Optimization (pp. 125-140). IGI Global.

Whig, P., Mudunuru, K. R., & Remala, R. (2024). Quantum-Inspired Data-Driven Decision Making for Supply Chain Logistics. In Quantum Computing and Supply Chain Management: A New Era of Optimization (pp. 85-98). IGI Global.

Sehrawat, S. K., Dutta, P. K., Bhatia, A. B., & Whig, P. (2024). Predicting Demand in Supply Chain Networks With Quantum Machine Learning Approach. In Quantum Computing and Supply Chain Management: A New Era of Optimization (pp. 33-47). IGI Global.

Whig, P., Kasula, B. Y., Yathiraju, N., Jain, A., & Sharma, S. (2024). Transforming Aviation: The Role of Artificial Intelligence in Air Traffic Management. In New Innovations in AI, Aviation, and Air Traffic Technology (pp. 60-75). IGI Global.

Kasula, B. Y., Whig, P., Vegesna, V. V., & Yathiraju, N. (2024). Unleashing Exponential Intelligence: Transforming Businesses through Advanced Technologies. International Journal of Sustainable Development Through AI, ML and IoT, 3(1), 1-18.

Whig, P., Bhatia, A. B., Nadikatu, R. R., Alkali, Y., & Sharma, P. (2024). 3 Security Issues in. Software-Defined Network Frameworks: Security Issues and Use Cases, 34.

Pansara, R. R., Mourya, A. K., Alam, S. I., Alam, N., Yathiraju, N., & Whig, P. (2024, May). Synergistic Integration of Master Data Management and Expert System for Maximizing Knowledge Efficiency and Decision-Making Capabilities. In 2024 2nd International Conference on Advancement in Computation & Computer Technologies (InCACCT) (pp. 13-16). IEEE.

Whig, P., & Kautish, S. (2024). VUCA Leadership Strategies Models for Pre-and Post-pandemic Scenario. In VUCA and Other Analytics in Business Resilience, Part B (pp. 127-152). Emerald Publishing Limited.

Whig, P., Bhatia, A. B., Nadikatu, R. R., Alkali, Y., & Sharma, P. (2024). GIS and Remote Sensing Application for Vegetation Mapping. In Geo-Environmental Hazards using AI-enabled Geospatial Techniques and Earth Observation Systems (pp. 17-39). Cham: Springer Nature Switzerland.

Most read articles by the same author(s)

<< < 1 2 3 4 5