Vol. 4 No. 2 (2024): Journal of AI-Assisted Scientific Discovery
Articles

Deep Learning Techniques for Intrusion Detection Systems: A Comparative Study of Accuracy and Efficiency

Dr. Emily Richards
Associate Professor, Department of Computer Science, University of Melbourne, Melbourne, Australia
Cover

Published 15-10-2024

Keywords

  • intrusion detection systems,
  • deep learning,
  • CNN,
  • RNN,
  • autoencoders

How to Cite

[1]
Dr. Emily Richards, “Deep Learning Techniques for Intrusion Detection Systems: A Comparative Study of Accuracy and Efficiency”, Journal of AI-Assisted Scientific Discovery, vol. 4, no. 2, pp. 87–93, Oct. 2024, Accessed: Nov. 21, 2024. [Online]. Available: https://scienceacadpress.com/index.php/jaasd/article/view/174

Abstract

Intrusion detection systems (IDS) are vital for safeguarding large-scale networks from cyber threats. Traditional IDS approaches often struggle to balance accuracy, detection time, and resource efficiency, especially in complex environments. Recent advances in deep learning have shown promise in improving these metrics. This paper provides a comparative study of various deep learning techniques, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), autoencoders, and hybrid models. It assesses their performance in terms of detection accuracy, computational efficiency, and suitability for real-time applications. The findings suggest that while CNNs excel in processing large amounts of network traffic data, RNNs are better suited for temporal sequence analysis. Autoencoders, on the other hand, demonstrate efficiency in anomaly detection with minimal resource consumption. The paper concludes with insights into the practical implementation of these models and discusses future directions for enhancing IDS performance through deep learning.

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References

  1. Vangoor, Vinay Kumar Reddy, et al. "Zero Trust Architecture: Implementing Microsegmentation in Enterprise Networks." Journal of Artificial Intelligence Research and Applications 4.1 (2024): 512-538.
  2. Gayam, Swaroop Reddy. "Artificial Intelligence in E-Commerce: Advanced Techniques for Personalized Recommendations, Customer Segmentation, and Dynamic Pricing." Journal of Bioinformatics and Artificial Intelligence 1.1 (2021): 105-150.
  3. Nimmagadda, Venkata Siva Prakash. "Artificial Intelligence for Predictive Maintenance of Banking IT Infrastructure: Advanced Techniques, Applications, and Real-World Case Studies." Journal of Deep Learning in Genomic Data Analysis 2.1 (2022): 86-122.
  4. Putha, Sudharshan. "AI-Driven Predictive Analytics for Maintenance and Reliability Engineering in Manufacturing." Journal of AI in Healthcare and Medicine 2.1 (2022): 383-417.
  5. Sahu, Mohit Kumar. "Machine Learning for Personalized Marketing and Customer Engagement in Retail: Techniques, Models, and Real-World Applications." Journal of Artificial Intelligence Research and Applications 2.1 (2022): 219-254.
  6. Kasaraneni, Bhavani Prasad. "AI-Driven Policy Administration in Life Insurance: Enhancing Efficiency, Accuracy, and Customer Experience." Journal of Artificial Intelligence Research and Applications 1.1 (2021): 407-458.
  7. Kondapaka, Krishna Kanth. "AI-Driven Demand Sensing and Response Strategies in Retail Supply Chains: Advanced Models, Techniques, and Real-World Applications." Journal of Artificial Intelligence Research and Applications 1.1 (2021): 459-487.
  8. Kasaraneni, Ramana Kumar. "AI-Enhanced Process Optimization in Manufacturing: Leveraging Data Analytics for Continuous Improvement." Journal of Artificial Intelligence Research and Applications 1.1 (2021): 488-530.
  9. Pattyam, Sandeep Pushyamitra. "AI-Enhanced Natural Language Processing: Techniques for Automated Text Analysis, Sentiment Detection, and Conversational Agents." Journal of Artificial Intelligence Research and Applications 1.1 (2021): 371-406.
  10. Kuna, Siva Sarana. "The Role of Natural Language Processing in Enhancing Insurance Document Processing." Journal of Bioinformatics and Artificial Intelligence 3.1 (2023): 289-335.
  11. George, Jabin Geevarghese, et al. "AI-Driven Sentiment Analysis for Enhanced Predictive Maintenance and Customer Insights in Enterprise Systems." Nanotechnology Perceptions (2024): 1018-1034.
  12. P. Katari, V. Rama Raju Alluri, A. K. P. Venkata, L. Gudala, and S. Ganesh Reddy, “Quantum-Resistant Cryptography: Practical Implementations for Post-Quantum Security”, Asian J. Multi. Res. Rev., vol. 1, no. 2, pp. 283–307, Dec. 2020
  13. Karunakaran, Arun Rasika. "Maximizing Efficiency: Leveraging AI for Macro Space Optimization in Various Grocery Retail Formats." Journal of AI-Assisted Scientific Discovery 2.2 (2022): 151-188.
  14. Sengottaiyan, Krishnamoorthy, and Manojdeep Singh Jasrotia. "Relocation of Manufacturing Lines-A Structured Approach for Success." International Journal of Science and Research (IJSR) 13.6 (2024): 1176-1181.
  15. Paul, Debasish, Gunaseelan Namperumal, and Yeswanth Surampudi. "Optimizing LLM Training for Financial Services: Best Practices for Model Accuracy, Risk Management, and Compliance in AI-Powered Financial Applications." Journal of Artificial Intelligence Research and Applications 3.2 (2023): 550-588.
  16. Namperumal, Gunaseelan, Akila Selvaraj, and Yeswanth Surampudi. "Synthetic Data Generation for Credit Scoring Models: Leveraging AI and Machine Learning to Improve Predictive Accuracy and Reduce Bias in Financial Services." Journal of Artificial Intelligence Research 2.1 (2022): 168-204.
  17. Soundarapandiyan, Rajalakshmi, Praveen Sivathapandi, and Yeswanth Surampudi. "Enhancing Algorithmic Trading Strategies with Synthetic Market Data: AI/ML Approaches for Simulating High-Frequency Trading Environments." Journal of Artificial Intelligence Research and Applications 2.1 (2022): 333-373.
  18. Pradeep Manivannan, Amsa Selvaraj, and Jim Todd Sunder Singh. “Strategic Development of Innovative MarTech Roadmaps for Enhanced System Capabilities and Dependency Reduction”. Journal of Science & Technology, vol. 3, no. 3, May 2022, pp. 243-85
  19. Yellepeddi, Sai Manoj, et al. "Federated Learning for Collaborative Threat Intelligence Sharing: A Practical Approach." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 146-167.