Impact of Artificial Intelligence and Cyber Security as Advanced Technologies on Bitcoin Industries

The proliferation of Internet usage and mobile device technology has resulted in an increase in the size of cyberspace. There is a growing likelihood that cyberspace will be the target of attacks that are fully automated and designed to continue for an extended period of time. Techniques used in cybersecurity make security processes more effective by enhancing their ability to identify and react to intrusions. The previously installed protection mechanisms are no longer sufficient since hackers are becoming more sophisticated and are able to circumvent established security measures. Conventional security solutions are often ineffective because they are unable to recognize previously undiscovered and polymorphic security attacks, which are two of the most common types of security breaches. Methods that make use of machine learning (ML) are crucial for a wide variety of applications pertaining to the field of cyber security. Despite the considerable advances that have been made, there are nonetheless a number of obstacles to overcome while attempting to guarantee the dependability of ML systems. This article presents in-depth analyses of the previous research that has been done on the use of machine learning (ML) techniques for network analysis and intrusion detection. In addition to this, a concise pedagogical explanation of each ML approach is provided. This article provides information on the datasets that are used in machine learning methods, which are the primary tools for analyzing network traffic and locating abnormalities.