Approaches of Deep Learning Used in Cyber Security and Cyber-Crime

Deep Learning (DL) is an area of artificial intelligence (AI), wherein machine learning (ML) using convolutional neural networks to detect patterns in data and forecast its worth. It excels in tough settings like climate change and can handle sophisticated jobs such as image and speech recognition, natural language processing, and others. Deep learning models have found applications in a variety of fields due to their ability to reflect complicated patterns in data. Deep learning applications have the capacity to analyze and automatically classify large volumes of Internet traffic. Deep learning-based solutions that automate the detection of attacks and address complicated cybersecurity issues are gaining popularity. This study extensively describes the promising uses of deep learning, which is based on many layers of artificial neural networks, in a wide range of security challenges. Before critically and comparatively reviewing state-of-the-art solutions from the literature, we will highlight the main properties of typical deep learning architectures used in cybersecurity applications. Deep learning highlights developing concepts and provides an overview of required resources, such as a general framework and appropriate datasets. The limits of the evaluated works are highlighted, as well as a vision of the present challenges in the field, providing useful insights and best practices for researchers and developers working on related problems.