Abstract
AI in Cybersecurity Market scheme helps organizations in observance, detecting, reporting, and countering cyber threats to keep up information confidentiality. The increasing awareness among folks, advancements in info technology, up-gradation of intelligence and police work solutions, and in
Highlights
Cybersecurity characterized as a collection of processes that help protect electronic data, human activity, and systems
Artificial Intelligence or AI is the development of complex computer systems with the aid of human mentality and able to perform its function like a normal human being, for example, it can recognize the voice and processes it into different languages as a human being
Tagarev et al [24] continued digital transformation requires substantial investment and innovation in order to ensure cyber safety, a variety of critical infrastructures and essential services which rely more and more on digital infrastructure, as well as to increase resilience in malicious use of cyberspace by organizations, societies, sectors, nations and Alliances. This volume includes 28 papers that will be presented at the DIGILIENCE 2019 conference, covering the cyber information sharing and situational awareness, the advantages and challenges of emerging technologies such as artificial intelligence, the human factor, cyber security education and training and cyber resilience, and the need to include cyber safety efforts in the search for DIGILIENCE Conference series would encourage the exchange of information and expertise in IT management, cyber security and resilience, facilitating the dissemination of good practice
Summary
Cybersecurity characterized as a collection of processes that help protect electronic data, human activity, and systems. Donald Hebb introduced this model in 1949 [5]–[8] Supervised learning This type of learning is responsible for a pattern to implement a machine learning algorithm. After getting emails and labels, these systems taught how to wipe out spam these spam emails can de track the user by giving them innovative tags Unsupervised learning This type of learning is contradictory to supervised learning. There are four types of machine learning algorithms: supervised, semi-supervised, and unsupervised and reinforcement This allows the system to make complex predictions when treated with the massive datasets. These algorithms functions on the population of the chromosomes that evolve based on certain operators.
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