Deep learning offering resilience from trending cyber-attacks, a review

ABSTRACT

During the Covid-19 pandemic world has witnessed the rise of cyber-attacks, especially during the Lockdown time course announced by the countries throughout the world, when almost every aspect of life changed the routine from offline to online. Protecting and securing information resources during pandemics has been a top priority for the modern computing world, with databases, banking, E-commerce and mailing services, etc. being the eye-catching credentials to the attackers. Apart from cryptography, machine learning and deep learning can offer an enormous amount of help in testing, training, and extracting negligible information from the data sets. Deep learning and machine learning have many methods and models in the account to detect and classify the different versions of cyber-attacks occasionally, from the datasets. Some of the most common deep learning methods inspired by the neural networks are Recurrent Neural Networks, Convolutional Neural Networks, Deep Belief Networks, Deep Boltzman Networks, Autoencoders, and Stacked Auto-encoders. Also counting machine learning algorithms into the account, there is a vast variety of algorithms that are meant to perform classification and regression. The survey will provide some of the most important deep learning and machine learning architectures used for Cyber-security and can offer protective services against cyber-attacks. The paper is a survey about various categories of cyber-attacks with a timeline of different attacks that took place in India and some of the other countries in the world. The final section of the report is about what deep learning methods can offer for developing and improving the security policies and examining vulnerabilities of an information system. © 2021 IEEE.