A Review of Deep Learning Strategies for Enhancing Cybersecurity in Networks

Abstract

Rapid technological improvements have brought significant hazards to sensitive data and information. Cyberspace has connected various data structures, ranging from private communications/transactions to government activities. Cyberattacks are growing more complex which emphasizes the need to improve cybersecurity. Cyber security is more crucial as everything becomes more digital and as the number of connected devices keeps increasing. Cyber security techniques are used to keep networks, applications, and devices safe from intruders. Cloud and IoT technologies have expanded the complexity of computing, communication, and networking infrastructures, making cybercrime prevention more difficult. It takes a long time to develop threat recognition algorithms by the existing methods. Innovative strategies, like employing deep learning tools for cybersecurity, are anticipated to provide a solution to the issue. Deep learning approaches have many benefits which include the ability to solve complex problems quickly, high levels of automation, the best use of informal information, the capacity to generate excellent results at a lower cost, and the ability to recognize complex interactions. A diverse range of applications can be employed in deep learning models to make decisions based on predictions in the daily routine. The significant benefits of deep learning-enabled cyber security have improved security and reduced risks. The intensity of this systematic study provides consolidated knowledge about recent trends and serves as a foundation for future research in Deep learning-enabled Cybersecurity. This paper highlights the potential challenges and current cybersecurity issues with cutting-edge Deep Learning technologies.