Unleashing the Full Potential of Artificial Intelligence and Machine Learning in Cybersecurity Vulnerability Management.

This research paper investigates the role of Artificial Intelligence (AI) and Machine Learning (ML) in Cybersecurity Threat Detection. With the increasing frequency and complexity of cyber-attacks, cybersecurity has become a major concern for individuals and organizations alike. AI and ML offer promising solutions to this problem by providing advanced analytics, automation, and decision-making capabilities to improve cybersecurity threat detection. In this paper, we examine various AI and ML techniques for threat detection, vulnerability management, password cracking, drones and robots, text, and Natural Language Processing (NLP). Furthermore, we present a case study on fake news prediction, using a dataset consisting of fake and real news, and propose a classifier model based on different ML algorithms, namely Multinomial Naive Bayes, Bernoulli Naive Bayes, Gaussian Naive Bayes, and Logistic Regression. The study shows that the proposed model can effectively predict fake news, achieving an accuracy of 0.99 %. Overall, this research sheds light on the potential of AI and ML in cybersecurity and demonstrates how these technologies can be used to detect cyber threats and enhance cybersecurity.