Vol. 4 No. 1 (2024): Journal of AI-Assisted Scientific Discovery
Articles

AI-Powered Intrusion Detection Systems: Real-World Performance Analysis

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  • Sai Manoj Yellepeddi,
  • Chetan Sasidhar Ravi,
  • Vinay Kumar Reddy Vangoor,
  • Subrahmanyasarma Chitta
Sai Manoj Yellepeddi
Senior Technical Advisor and Independent Researcher, Redmond, USA
Chetan Sasidhar Ravi
Mulesoft Developer, Zurich American Insurance, Illinois, USA
Vinay Kumar Reddy Vangoor
Engineer II, MetaSoftTech Solutions LLC, Arizona, USA
Subrahmanyasarma Chitta
Software Engineer, Access2Care LLC, Colorado, USA
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Published 11-01-2024

Keywords

  • AI-powered IDS,
  • machine learning,
  • deep learning,
  • cybersecurity,
  • intrusion detection,
  • performance analysis,
  • scalability,
  • implementation challenges,
  • attack scenarios
    • ...More
    Less
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

How to Cite

[1]
S. Manoj Yellepeddi, C. Sasidhar Ravi, V. Kumar Reddy Vangoor, and S. Chitta, “AI-Powered Intrusion Detection Systems: Real-World Performance Analysis”, Journal of AI-Assisted Scientific Discovery, vol. 4, no. 1, pp. 279–289, Jan. 2024, Accessed: Sep. 19, 2024. [Online]. Available: https://scienceacadpress.com/index.php/jaasd/article/view/135

Abstract

The advent of artificial intelligence (AI) has significantly influenced various domains of cybersecurity, particularly in the realm of Intrusion Detection Systems (IDS). This paper presents a comprehensive analysis of AI-powered IDS, focusing on their real-world performance relative to traditional IDS methodologies. As cyber threats continue to evolve in complexity and sophistication, the need for advanced detection mechanisms has become paramount. AI-powered IDS leverage machine learning (ML) and deep learning (DL) techniques to enhance detection accuracy, reduce false positives, and improve response times.

Machine learning algorithms, such as decision trees, support vector machines, and ensemble methods, have been extensively employed in IDS to learn from historical data and identify patterns indicative of potential intrusions. These algorithms enable IDS to adapt to new and emerging threats by continuously refining their detection models based on evolving data. Deep learning techniques, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have further advanced the field by enabling the analysis of complex, high-dimensional data, such as network traffic and system logs, with greater accuracy.

The paper provides a detailed comparative performance analysis of AI-powered IDS against traditional signature-based and anomaly-based IDS approaches. Traditional IDS systems rely heavily on predefined signatures and heuristics to detect known threats. While these systems are effective at identifying known attack patterns, they often fall short in detecting novel or sophisticated threats. In contrast, AI-powered IDS utilize advanced algorithms capable of identifying previously unseen attack vectors by learning from vast amounts of data.

Real-world performance analysis is conducted using a variety of datasets and attack scenarios. This includes examining the efficacy of AI-powered IDS in detecting various types of attacks, such as distributed denial of service (DDoS) attacks, malware infections, and insider threats. The analysis encompasses metrics such as detection accuracy, false positive rates, and response times, providing a nuanced understanding of the strengths and limitations of AI-powered systems.

Implementation challenges are a critical aspect of integrating AI into IDS. The paper explores issues related to the training and validation of AI models, including the need for large, representative datasets and the risks of overfitting. Scalability concerns are also addressed, as the deployment of AI-powered IDS in large-scale networks may require significant computational resources and infrastructure. Additionally, the paper discusses the implications of AI model interpretability and transparency, which are crucial for ensuring trust and accountability in cybersecurity applications.

The future of AI in IDS is examined, with a focus on emerging trends and technologies. The paper highlights the potential of hybrid approaches that combine AI techniques with traditional IDS methods to enhance overall effectiveness. It also considers the role of explainable AI (XAI) in improving the interpretability of AI-powered IDS and fostering greater adoption in enterprise environments.

This paper provides an in-depth analysis of AI-powered IDS, offering valuable insights into their performance, implementation challenges, and future prospects. The findings underscore the transformative potential of AI in enhancing cybersecurity defenses, while also identifying areas for further research and development. By addressing the limitations of traditional IDS and leveraging the capabilities of advanced AI techniques, AI-powered IDS represent a significant advancement in the field of cybersecurity.

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References

  1. H. Zhang, J. Zhao, and G. Wu, “A survey on network anomaly detection with machine learning algorithms,” IEEE Access, vol. 8, pp. 105683–105700, 2020.
  2. C. C. Ko, J. H. Lin, and C. H. Liu, “Anomaly detection in computer networks based on deep learning techniques,” IEEE Transactions on Network and Service Management, vol. 17, no. 1, pp. 214–227, Mar. 2020.
  3. A. P. G. Armitage and N. H. F. Jones, “Machine learning for intrusion detection: An overview,” IEEE Transactions on Information Forensics and Security, vol. 16, pp. 2678–2692, 2021.
  4. L. Zhao, K. Wang, and Y. Liu, “A deep learning-based approach for anomaly detection in network traffic,” IEEE Transactions on Network and Service Management, vol. 18, no. 3, pp. 2910–2922, Sep. 2021.
  5. M. A. Ahmed, M. M. H. Kamel, and J. M. H. Iqbal, “Evaluation of machine learning techniques for network intrusion detection systems,” IEEE Access, vol. 9, pp. 14350–14361, 2021.
  6. J. Yang, W. Ma, and X. Zhao, “A survey of deep learning for network intrusion detection systems,” IEEE Access, vol. 8, pp. 105457–105473, 2020.
  7. M. A. K. P. Singh and J. L. Smith, “Hybrid intrusion detection systems: Combining signature and anomaly detection techniques,” IEEE Transactions on Dependable and Secure Computing, vol. 18, no. 1, pp. 45–58, Jan. 2021.
  8. R. M. A. Gomez and H. M. B. Fernandez, “Comparative study of machine learning algorithms for IDS performance evaluation,” IEEE Transactions on Network and Service Management, vol. 17, no. 2, pp. 1751–1762, Jun. 2020.
  9. L. Liu, Q. Zhang, and S. Yang, “Federated learning for privacy-preserving intrusion detection systems,” IEEE Transactions on Information Forensics and Security, vol. 16, pp. 2937–2948, 2021.
  10. J. A. Rodriguez and F. J. Castro, “Reinforcement learning for optimizing intrusion detection systems,” IEEE Transactions on Neural Networks and Learning Systems, vol. 31, no. 4, pp. 1104–1116, Apr. 2020.
  11. S. M. Patel, M. S. Verma, and V. B. Singh, “Deep convolutional neural networks for network intrusion detection,” IEEE Transactions on Cybernetics, vol. 50, no. 7, pp. 3122–3135, Jul. 2020.
  12. B. C. Wu, T. Z. Zheng, and J. H. Choi, “Explainable AI techniques for intrusion detection systems: A survey,” IEEE Access, vol. 9, pp. 18842–18856, 2021.
  13. P. P. Lee and M. B. Kim, “Scalable anomaly detection in network traffic using AI-powered IDS,” IEEE Transactions on Computers, vol. 70, no. 4, pp. 530–542, Apr. 2021.
  14. A. S. Kumar and D. R. K. Patel, “Performance evaluation of deep learning models for cybersecurity,” IEEE Transactions on Information Forensics and Security, vol. 15, pp. 1158–1170, 2020.
  15. T. J. Chen, H. L. Liu, and R. Y. Zhao, “The role of AI in enhancing traditional intrusion detection systems,” IEEE Transactions on Network and Service Management, vol. 18, no. 1, pp. 97–109, Mar. 2021.
  16. Y. M. Wang, Z. X. Liu, and M. J. Yang, “Adversarial attacks and defenses in deep learning for IDS,” IEEE Transactions on Knowledge and Data Engineering, vol. 33, no. 6, pp. 1254–1266, Jun. 2021.
  17. R. K. Sharma and J. L. Wang, “Challenges and solutions in scaling AI-powered IDS for large networks,” IEEE Transactions on Network and Service Management, vol. 18, no. 2, pp. 1122–1135, Jun. 2021.
  18. F. R. Gomez and C. Y. Zhou, “Adversarial training techniques for improving IDS robustness,” IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 2, pp. 298–310, Feb. 2021.
  19. N. A. Brown and H. M. Jones, “Hybrid AI models for intrusion detection systems: A survey,” IEEE Transactions on Information Forensics and Security, vol. 15, pp. 830–843, 2020.
  20. L. X. Yang, J. S. Li, and D. Q. Zhang, “The future of AI in cybersecurity: Emerging trends and research directions,” IEEE Access, vol. 9, pp. 19637–19652, 2021.

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