Artificial Intelligence in Cybersecurity: Advancing Intelligent Threat Detection, Prevention, and Automated Response

The digital world is expanding fast, but so are the cracks in its armor. Traditional security tools are failing to keep up with the sheer volume of modern cyber threats. This paper takes a hard look at how Artificial Intelligence (AI) is stepping in to fix this mess. We're shifting from a reactive defense strategy to one that predicts attacks before they land. We explore how AI is being used to detect intrusions, classify malware, and stop phishing, while also being honest about the risks, like attackers using AI against us and the problem of opaque algorithms we can't explain. The bottom line? AI isn't a magic fix, but it’s the only way we stand a chance against the speed of modern cybercrime.

Computer Science, Cybersecurity, Artificial Intelligence

1. Alotaibi, A., & Rassam, M. A. (2023). Adversarial Machine Learning Attacks against Intrusion Detection Systems: A Survey on Strategies and Defense. *Future Internet*. [Google Scholar] [Crossref]

2. Chaulagain, B. R., Pandey, A., Basnet, S. R., & Shakya, S. (2021). Hybrid Malware Detection Using Deep Learning and Data Fusion. [Google Scholar] [Crossref]

3. Foley, M., O'Reilly, P., & O'Sullivan, D. (2022). Autonomous Network Defence using Reinforcement Learning. *Proceedings of the 2022 ACM on Asia Conference on Computer and Communications Security*. [Google Scholar] [Crossref]

4. Gu, J., & Lu, S. (2021). An Effective Intrusion Detection Approach Using SVM with Naïve Bayes Feature Embedding. *Computers & Security*. [Google Scholar] [Crossref]

5. Hindy, H., Brosset, D., Bayne, E., Seeam, A., Tachtatzis, C., Atkinson, R., & Bellekens, X. (2020). A Taxonomy of Network Threats and the Effect of Current Datasets on Intrusion Detection Systems. *IEEE Access*. [Google Scholar] [Crossref]

6. Karki, M., & Nasoz, F. (2022). Comparative Analysis of BERT, RoBERTa, and DistilBERT for Phishing Email Detection. *IEEE Access*. [Google Scholar] [Crossref]

7. Sahingoz, O. K., Buber, E., Demir, O., & Diri, B. (2019). Machine Learning Based Phishing Detection from URLs. *Expert Systems with Applications*. [Google Scholar] [Crossref]

8. Sarker, I. H. (2021). As models get smarter, they get darker. We can't see inside them. This "Black Box" issue is a dealbreaker for critical infrastructure. And it gets worse: "Adversarial AI" is real. Attackers are using their own AI to generate. [Google Scholar] [Crossref]

9. . *SN Computer Science*. [Google Scholar] [Crossref]

10. Shelar, P., & Rao, S. (2021). Enhanced Capsule Network-based Executable Files Malware Detection and Classification. *Concurrency and Computation: Practice and Experience*. [Google Scholar] [Crossref]

11. Zhang, Y., & Wang, Q. (2024). Explainable Artificial Intelligence (XAI) for Cybersecurity: A Survey of Recent Trends. *Journal of Network and Computer Applications*. [Google Scholar] [Crossref]

• What the Desert Fathers Teach Data Scientists: Ancient Ascetic Principles for Ethical Machine-Learning Practice
• Comparative Analysis of Some Machine Learning Algorithms for the Classification of Ransomware
• Comparative Performance Analysis of Some Priority Queue Variants in Dijkstra’s Algorithm
• Transfer Learning in Detecting E-Assessment Malpractice from a Proctored Video Recordings.
• Dual-Modal Detection of Parkinson’s Disease: A Clinical Framework and Deep Learning Approach Using NeuroParkNet