Abstract:
"Cybersecurity has become an ever-evolving challenge, as the digital landscape becomes increasingly intertwined with daily life. Distributed Denial of Service (DDoS) is a type of cyber- attack that threatens the availability of online resources. Organizations without proper protection face an average expense of $200,000 for each DDoS attack irrespective of how often, how long, or how extensive the attacks are. Even small-scale businesses bear a significant impact because of DDoS attacks, incurring an average recovery cost of $120,000.
The study explores the landscape of DDoS attacks, sheds light on the evolving nature and the impact of such attacks. Furthermore, by drawing inspiration from remarkable capabilities of deep learning, this research proposes a novel solution for DDoS attack detection. By using the capabilities such as adaptability and pattern recognition prowess of deep learning models, the aim is to improve the accuracy and precision of network intrusion detection systems.
In conclusion, the suggested deep learning technique shows promise as a way to strengthen cyber defences and provide a proactive method of detecting DDoS attacks. In the end, the research's findings promote a safer and more secure digital world by adding to the continuing conversation on cybersecurity resilience in the face of new threats."
