Cyber Attacks Detection in Network Using Deep Learning Techniques
D. Sameera, Gaddala Koushik, Imran Ahmed Shaik, Micheal Richard · 2024
In today’s digitally saturated era, cybersecurity emerges as a paramount concern, given the looming Specter of cyber-attacks. This paper ventures into the realm where cybersecurity intersects with deep learning methodologies, with a focal point on the pioneering approach introduced by Cybernet in identifying and neutralizing cyber threats within network infrastructures. Commencing with an overview of cybersecurity and the dynamic landscape of cyber-attacks, the paper accentuates the deficiencies inherent in traditional security protocols and underscores the imperative for progressive solutions. It subsequently delves into the pivotal role of deep learning within the realm [2] of cybersecurity, elucidating its myriad applications encompassing anomaly detection, malware scrutiny, and intrusion detection. The deployment of Cybernet is scrutinized, spanning from the intricacies of data aggregation and preprocessing to the intricacies of real-time surveillance and response mechanisms. Through illuminative case studies, the paper showcases the effectiveness of Cybernet in thwarting cyber intrusions, while also providing insights into forthcoming trends and challenges that underscore the perpetual evolution of deep learning in the cybersecurity domain. Ultimately, the paper underscores the pivotal role played by deep learning in fortifying cybersecurity resilience and shielding digital assets against the emergent threats of tomorrow.