Deep Learning Techniques for Cybersecurity Applications

In the contemporary world, Deep Learning performed incredible execution in various zones such as pattern detection, image recognition, and network protection like cybersecurity. Deep learning (DL) has various focal points, including quick tackling, complex issues, enormous computerization, most extreme utilization of unstructured information, capacity to give high caliber results, a decrease of significant expenses, no requirement for data naming, and identification of operations. This can remove ideal element portrayal from crude information tests. This determines different types of cyber use cases, which includes the discovery of intrusions, a grouping of malware, analysis of android malware, identifying scams, recognizing phishing methods, and detection of binary processes. In this chapter, the current study focuses on the deep learning task for different digital protection or cybersecurity use cases. Here, several methodologies of deep learning (DL) online protection are given and organized through an outline of the numerous cyberattack discovery techniques. These are directed to cyberattack recognition strategies dependent on DL classifiers. In particular, we have mainly summarized the core issues of cybersecurity organizations and presented some related practical applications using the deep learning framework. We considered attack recognition strategies based on various designs like a recurrent neural network, auto encoder's generative adversarial network, and convolution neural network based on deep learning techniques. At long last, we conclude specific approaches to improve the execution of cybersecurity recognition under considerations of using deep learning structures. In the contemporary world, Deep Learning performed incredible execution in various zones such as pattern detection, image recognition, and network protection like cybersecurity. Deep learning (DL) has various focal points, including quick tackling, complex issues, enormous computerization, most extreme utilization of unstructured information, capacity to give high caliber results, a decrease of significant expenses, no requirement for data naming, and identification of operations. This chapter focuses on the deep learning task for different digital protection or cybersecurity use cases. The most annoying test in cybersecurity is easily the steadily boosting nature of security hazards. Generally, associations or the public authority could have zeroed in a more significant part of t