Deep Learning for Anomaly Detection in High-Dimensional Data: Applications in Cybersecurity

Authors

  • Jane Smith PhD, Senior Researcher, Department of Computer Science, University of California, Berkeley, CA, USA Author

Keywords:

deep learning, anomaly detection

Abstract

The advent of big data has posed significant challenges in various fields, particularly in cybersecurity, where the volume and complexity of data make it increasingly difficult to identify anomalies indicative of potential threats. This paper explores the application of deep learning techniques for anomaly detection in high-dimensional datasets, focusing on their effectiveness in real-time threat identification and breach prevention in cybersecurity. We discuss various deep learning architectures, including autoencoders, convolutional neural networks (CNNs), and recurrent neural networks (RNNs), that have shown promise in handling high-dimensional data. The paper further examines the limitations of traditional methods and highlights how deep learning approaches can enhance detection accuracy. By reviewing recent studies and case applications, this research aims to provide insights into the evolving landscape of cybersecurity and the critical role of deep learning in safeguarding sensitive information.

Downloads

Download data is not yet available.

References

Gayam, Swaroop Reddy. "Deep Learning for Predictive Maintenance: Advanced Techniques for Fault Detection, Prognostics, and Maintenance Scheduling in Industrial Systems." Journal of Deep Learning in Genomic Data Analysis 2.1 (2022): 53-85.

Venkata, Ashok Kumar Pamidi, et al. "Reinforcement Learning for Autonomous Systems: Practical Implementations in Robotics." Distributed Learning and Broad Applications in Scientific Research 4 (2018): 146-157.

Nimmagadda, Venkata Siva Prakash. "Artificial Intelligence for Supply Chain Visibility and Transparency in Retail: Advanced Techniques, Models, and Real-World Case Studies." Journal of Machine Learning in Pharmaceutical Research 3.1 (2023): 87-120.

Putha, Sudharshan. "AI-Driven Predictive Maintenance for Smart Manufacturing: Enhancing Equipment Reliability and Reducing Downtime." Journal of Deep Learning in Genomic Data Analysis 2.1 (2022): 160-203.

Sahu, Mohit Kumar. "Advanced AI Techniques for Predictive Maintenance in Autonomous Vehicles: Enhancing Reliability and Safety." Journal of AI in Healthcare and Medicine 2.1 (2022): 263-304.

Kondapaka, Krishna Kanth. "AI-Driven Predictive Maintenance for Insured Assets: Advanced Techniques, Applications, and Real-World Case Studies." Journal of AI in Healthcare and Medicine 1.2 (2021): 146-187.

Kasaraneni, Ramana Kumar. "AI-Enhanced Telematics Systems for Fleet Management: Optimizing Route Planning and Resource Allocation." Journal of AI in Healthcare and Medicine 1.2 (2021): 187-222.

Pattyam, Sandeep Pushyamitra. "Artificial Intelligence in Cybersecurity: Advanced Methods for Threat Detection, Risk Assessment, and Incident Response." Journal of AI in Healthcare and Medicine 1.2 (2021): 83-108.

Ahmad, Tanzeem, et al. "Explainable AI: Interpreting Deep Learning Models for Decision Support." Advances in Deep Learning Techniques 4.1 (2024): 80-108.

Y. LeCun, Y. Bengio, and G. Hinton, "Deep learning," Nature, vol. 521, pp. 436-444, May 2015.

I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. Cambridge, MA, USA: MIT Press, 2016.

K. Murphy, Machine Learning: A Probabilistic Perspective. Cambridge, MA, USA: MIT Press, 2012.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, "ImageNet classification with deep convolutional neural networks," in Proc. 25th Int. Conf. Neural Inf. Process. Syst., 2012, pp. 1097–1105.

J. Schmidhuber, "Deep learning in neural networks: An overview," Neural Networks, vol. 61, pp. 85-117, Jan. 2015.

S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 4th ed. Upper Saddle River, NJ, USA: Prentice Hall, 2020.

M. Mohri, A. Rostamizadeh, and A. Talwalkar, Foundations of Machine Learning, 2nd ed. Cambridge, MA, USA: MIT Press, 2018.

G. James, D. Witten, T. Hastie, and R. Tibshirani, An Introduction to Statistical Learning: With Applications in R, 2nd ed. New York, NY, USA: Springer, 2021.

C. M. Bishop, Pattern Recognition and Machine Learning. New York, NY, USA: Springer, 2006.

R. D. Luque, M. Carrión, and C. L. Castillo, "Ethics in artificial intelligence: An overview of ethical theories and models," Int. J. Interact. Multimedia Artif. Intell., vol. 5, no. 5, pp. 4-14, Jan. 2019.

S. J. Pan and Q. Yang, "A survey on transfer learning," IEEE Trans. Knowl. Data Eng., vol. 22, no. 10, pp. 1345-1359, Oct. 2010.

Y. Bengio, "Learning deep architectures for AI," Found. Trends Mach. Learn., vol. 2, no. 1, pp. 1-127, 2009.

A. Géron, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd ed. Sebastopol, CA, USA: O'Reilly Media, 2019.

A. Holzinger, P. Kieseberg, A. Weippl, and E. Tjoa, "Current advances, trends and challenges of machine learning and knowledge extraction: From machine learning to explainable AI," in Lecture Notes in Computer Science, vol. 11015. Cham, Switzerland: Springer, 2018, pp. 1-8.

Downloads

Published

01-12-2023

How to Cite

[1]
J. Smith, “Deep Learning for Anomaly Detection in High-Dimensional Data: Applications in Cybersecurity”, J. of Artificial Int. Research and App., vol. 3, no. 2, pp. 685–692, Dec. 2023, Accessed: Nov. 07, 2024. [Online]. Available: https://aimlstudies.co.uk/index.php/jaira/article/view/249

Similar Articles

11-20 of 147

You may also start an advanced similarity search for this article.