| Title | Cybersecurity Threat Detection Using Deep Learning Techniques in Networked Systems |
| Research Area | Cybersecurity, Deep Learning, Intrusion Detection System, Malware Detection, Network Security, Anomaly Detection |
| Abstract | The rapid growth of digital networks, cloud computing, and Internet-based services has significantly increased exposure to cyber threats such as malware, phishing, denial-of-service attacks, and network intrusions. Traditional rule-based and signature-based security mechanisms are increasingly ineffective against sophisticated and evolving attack patterns. Deep learning techniques provide an intelligent and adaptive approach for detecting cyber threats by learning complex patterns from large volumes of network data. This paper presents a comprehensive study of deep learning-based cybersecurity threat detection models, including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and Autoencoders. Experimental evaluation demonstrates that deep learning models achieve high detection accuracy and significantly reduce false positives compared to conventional intrusion detection systems. The study also discusses challenges related to scalability, real-time deployment, and data imbalance in cybersecurity analytics. |
| Keywords | Cybersecurity, Deep Learning, Intrusion Detection System, Malware Detection, Network Security, Anomaly Detection |
| Paper Status | Published |
| Volume | 1 |
| Issue | 6 |
| Published On | 08/12/2025 |
| Published File |
IJSRTD_4864.pdf
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