Artificial intelligence

AI-Based Intrusion Detection: Enhancing Cybersecurity Defense

By: Avadhesh Kumar Gupta, Unitedworld School of Computational Intelligence , Karnavati University, India, Email: dr.avadheshgupta@gmail.com

Abstract

Intrusion detection is an essential component of modern cybersecurity defense. Traditional intrusion detection systems rely on rule-based approaches that require manual updates and can struggle to detect new or advanced threats. Artificial intelligence (AI)-based intrusion detection systems, on the other hand, have shown promising results in detecting and preventing cyberattacks. This blog post will explore the benefits of AI-based intrusion detection and its potential to enhance cybersecurity defense.

What is AI-Based Intrusion Detection?

AI-based intrusion detection systems use machine learning algorithms to analyze network traffic and identify anomalous patterns [1,2,3]. These algorithms can learn from large datasets of normal and abnormal traffic to detect even subtle deviations from expected behavior [4,5,6]. AI-based intrusion detection systems can identify new and unknown threats and adapt to evolving attack methods, making them more effective than rule-based approaches [7,8,9].

Benefits of AI-Based Intrusion Detection

These are some of the benefits of AI-based intrusion detection [10,11,12]:

  1. Early Detection of Advanced Threats: AI-based intrusion detection systems can identify new and unknown threats, including those that are designed to evade traditional security measures.
  2. Real-Time Response: AI-based intrusion detection systems can respond to threats in real-time, allowing for rapid containment and mitigation of the attack.
  3. Reduced False Positives: AI-based intrusion detection systems can reduce false positives by analyzing network traffic in context, considering user behavior and network topology factors.
  4. Adaptability: AI-based intrusion detection systems can adapt to evolving threats and attack methods, making them more effective over time.

Challenges of AI-Based Intrusion Detection

While AI-based intrusion detection has many benefits, there are also some challenges to consider, such as [13,14,15]:

  1. Data Quality: AI-based intrusion detection systems require large amounts of high-quality data to be effective.
  2. Explainability: AI-based intrusion detection systems can be difficult to interpret and explain, making understanding how they arrive at their decisions is challenging.
  3. Cost: AI-based intrusion detection systems can be expensive to develop, implement, and maintain.

Conclusion

AI-based intrusion detection has the potential to enhance cybersecurity defense by providing early detection of advanced threats, real-time response, reduced false positives, and adaptability. However, there are also challenges to consider, such as data quality, explainability, and cost. Organizations should carefully evaluate their cybersecurity needs and resources to determine whether AI-based intrusion detection is appropriate for their environment. With the proper implementation and maintenance, AI-based intrusion detection can be a valuable tool in enhancing cybersecurity defense.

References

  1. Fatemidokht, H., et al., (2021). Efficient and secure routing protocol based on artificial intelligence algorithms with UAV-assisted for vehicular ad hoc networks in intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems, 22(7), 4757-4769.
  2. Gupta, B. B., Gupta, S., & Chaudhary, P. (2017). Enhancing the browser-side context-aware sanitization of suspicious HTML5 code for halting the DOM-based XSS vulnerabilities in cloud. International Journal of Cloud Applications and Computing (IJCAC), 7(1), 1-31.
  3. Rao, B. V., Sharma, V., Rathore, N., Prasad, D., Anandaram, H., & Soni, G. (2023). A Secure Framework to Prevent Three-Tier Cloud Architecture From Malicious Malware Injection Attacks. , 13(1), 1-22. http://doi.org/10.4018/IJCAC.317220
  4. Gupta, B. B., Li, K. C., Leung, V. C., Psannis, K. E., & Yamaguchi, S. (2021). Blockchain-assisted secure fine-grained searchable encryption for a cloud-based healthcare cyber-physical system. IEEE/CAA Journal of Automatica Sinica, 8(12), 1877-1890.
  5. Sandeep Kumar (2021) Artificial Intelligence and Machine learning for Smart and Secure Healthcare System, Insights2Techinfo, pp.1
  6. Akilandeswari J., Jothi G., Dhanasekaran K., Kousalya K., & Sathiyamoorthi V. (2022). Hybrid Firefly-Ontology-Based Clustering Algorithm for Analyzing Tweets to Extract Causal Factors. International Journal on Semantic Web and Information Systems (IJSWIS), 18(1), 1-27. http://doi.org/10.4018/IJSWIS.295550
  7. Cvitić, I., Peraković, D., Periša, M., & Gupta, B. (2021). Ensemble machine learning approach for classification of IoT devices in smart home. International Journal of Machine Learning and Cybernetics, 12(11), 3179-3202.
  8. Ling, Z. & Hao, Z. J. (2022). Intrusion Detection Using Normalized Mutual Information Feature Selection and Parallel Quantum Genetic Algorithm. International Journal on Semantic Web and Information Systems (IJSWIS), 18(1), 1-24. http://doi.org/10.4018/IJSWIS.307324
  9. Gopal Mengi; Sudhakar Kumar (2022) Artificial Intelligence and Machine Learning in Healthcare, Insights2Tecinfo, pp. 1
  10. Nguyen, G. N., Le Viet, N. H., Elhoseny, M., Shankar, K., Gupta, B. B., & Abd El-Latif, A. A. (2021). Secure blockchain enabled Cyber–physical systems in healthcare using deep belief network with ResNet model. Journal of parallel and distributed computing, 153, 150-160.
  11. Mishra, A., et al., (2021, January). Classification based machine learning for detection of ddos attack in cloud computing. In 2021 IEEE International Conference on Consumer Electronics (ICCE) (pp. 1-4). IEEE.
  12. Kinyanjui P.W., Rawal B.S. (2022) Opportunities in 5G Edge Computing & Security Challenges. Cyber Security Insights Magazine, Insights2Techinfo, Volume 1, pp. 20-27. 2022.
  13. Yamaguchi, S., et al., (2021). Malware threat in Internet of Things and its mitigation analysis. In Research Anthology on Combating Denial-of-Service Attacks (pp. 371-387). IGI Global.
  14. Chattopadhyay S., Banerjee S., Pal A., Adi N. S., Rahaman M., (2022) Secure smart socket system for energy monitoring embedded with IoT, Cyber Security Insights Magazine, Insights2Techinfo, Volume 3, pp. 1-5.
  15. Lee, M. T., & Suh, I. (2022). Understanding the effects of Environment, Social, and Governance conduct on financial performance: Arguments for a process and integrated modelling approach. Sustainable Technology and Entrepreneurship, 1(1), 100004.
  16. Gupta, B. B., Agrawal, P. K., Mishra, A., & Pattanshetti, M. K. (2011). On estimating strength of a DDoS attack using polynomial regression model. In Advances in Computing and Communications: First International Conference, ACC 2011, Kochi, India, July 22-24, 2011, Proceedings, Part IV 1 (pp. 244-249). Springer Berlin Heidelberg.

Cite As:

A.K. Gupta (2023) AI-Based Intrusion Detection: Enhancing Cybersecurity Defense, Insights2Techinfo, pp.1

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