A Survey on Cybersecurity Techniques Toward Convolutional Neural Network

Between cyber security and deep learning, there are numerous multidisciplinary intersections. One of the deep learning methods such as convolutional neural network (CNN) can be used to build smart models for cyber anomalies, fraud detection, malware detection, and threat intelligence sensing in cyber security. Convolutional neural networks are a type of neural network that has been shown to be particularly successful in image recognition, video recognition and classification. Data and computing resources connected on a network usually are protected by cybersecurity. Throughout the attack cycle, its purpose is to secure these resources from all attackers. CNN have proved successful at recognizing faces, objects, traffic signs and so much more. Convolutional neural network is vulnerable to a variety of cyberattacks such threats, malwares, frauds and more. However, in this paper we have summarized a comprehension of convolutional neural networks. In addition, we have represent that the Distributed Denial of Service (DDoS) attacks is one of the important attacks over the last years in CNN. Finally, we have seen different detection techniques against threats toward CNN. In this paper, we provide a comprehensive survey that have been carried recent CNN techniques in cybersecurity.