Advancing Cybersecurity with Deep Learning: Innovative Approaches and Real-Time Applications

Advancing Cybersecurity with Deep Learning: Innovative Approaches and Real-Time Applications

Publication Date : 2024-09-11
Author(s) :

Sandeep Kosuri, Saranya Eeday

           
Article Name :

Advancing Cybersecurity with Deep Learning: Innovative Approaches and Real-Time Applications

Abstract :

Sophisticated security measures are required to safeguard vital infrastructure and sensitive data due to the ever changing nature of cyber threats. Integrating deep learning techniques into cybersecurity is the subject of this study, which aims to improve threat detection, response, and prevention using novel methodologies. We compare the performance of different deep learning architectures, such as RNNs and CNNs, in detecting patterns that can indicate cyberattacks, using tools like malware detectors and intrusion detection systems (IDS). By utilizing massive datasets and real time analytics, these models outperform conventional methods in terms of speed and accuracy. Moreover, we offer real world examples of deep learning in action, including automated threat intelligence analysis and anomaly detection in network data. Strong training processes and model optimization are necessary, as the results show that cybersecurity frameworks must constantly learn and adapt. Also covered are issues with data privacy and the need for computational resources, two of the many obstacles to using deep learning solutions in cybersecurity. By expanding our knowledge of how deep learning can strengthen cybersecurity measures, this research ultimately leads to more resilient systems that can withstand emerging cyber threats.

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