Identification of IoT vulnerabilities using DRL

Abstract

Security issues have been a threat to our modern life even after all the inventions in modern data networks. It has come to a stage where with great innovation comes greater risk. Recently, security issues in IoT devices are increasing day by day with the vast uses of IoT devices in our everyday life. In today's world, the most valuable thing is information, the more information you possess the richer and more powerful one is. Due to the increase in use, the online attackers have been recently targeting IoT devices to gain monetary benefit or acquiring different sensitive information from the crucial sources. Maximum IoT devices are very simple to get hands on illegally. Since IoT devices are not well equipped with proper security measures in order to keep them easily accessible and low cost, the hackers are easily accessing these devices. However, computer and digital devices often appear to become more unstable and vulnerable to malfunctions and vulnerabilities due to cyber attacks. Also, this is economically harmful which is quite frustrating. Securing these have been quite an important issue for many years. Thus, to make sure the information is safe and to overcome these vulnerabilities different measures are taking place to minimize these attacks. Several surveys are put forward to address these IoT based topics including intrusion detection, threat minimization and prevention of these attacks. It is not possible for the users to keep track of the intrusion every time manually. So an autonomous security measure should be taken to prevent the intrusion. In order to do this we are using Deep Reinforcement Learning technique to detect this intrusion and prevent them with the existing data and model as it is highly efficient in tackling complex, diverse and, in particular defense of highextent cyber attacks. Reinforcement learning (RL) operates in a rotation of sense action goals. Since reinforcement learning acquires information directly from the environment, it is distinct from supervised learning that learns from the examples given. We are trying to detect and prevent malware attacks like viruses, ransomware and everything. For that, patterns need to be found by using deep learning or deep reinforcement learning algorithms. Furthermore, the system will be analyzing multiple attacks from systems that are already infected to do so and finally we will be coming up with a pattern created out of data analysis to prevent further attacks.