CYBERATTACK DETECTION USING DEEP LEARNING TECHNIQUES
https://doi.org/10.5281/zenodo.17506217
Keywords:
Cybersecurity, Deep Learning, Intrusion Detection, Neural Networks, Artificial IntelligenceAbstract
The increasing frequency and sophistication of cyberattacks have made traditional rule-based security systems inadequate. Deep learning (DL), a subfield of artificial intelligence (AI), provides a powerful approach for detecting and mitigating cyber threats by automatically learning complex data patterns. This study explores deep learning-based techniques for cyberattack detection, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and autoencoders. Experimental results from recent studies highlight that DL models outperform traditional machine learning algorithms in accuracy, adaptability, and feature extraction. The paper concludes with recommendations for integrating DL systems into real-world cybersecurity frameworks.
