Machine Learning for Enhanced Cyber Security

Cyber security is a big issue in current society since exploiting computer network vulnerabilities has become simple thanks to technological advances and human talents.Currently, several types of assaults are occurring, such as DOS attacks, probing, R2U, R2L viruses, port scanning, buffer overflow, CGI attacks, and floods, among others. A strong foundation is required to build a system for detecting and preventing these threats. The majority of the most recent ways for implementing IDS for computer security are covered in this article. Intrusion Detection Systems are the best answer for cyber-attacks. In a continuously changing environment, machine learning-based intrusion detection systems exhibit excellent accuracy. This study provide a broad overview of machine-learning algorithms, focusing on how they may be used for sophisticated data processing and automation in cybersecurity, with a particular focus on their ability to extract useful insights from cyber data. In addition to, this study investigates a variety of practical applications wherein information knowledge, mechanization, and decision-making might be used to provide proactive, next-generation cyber security. Our paper concludes by emphasizing the potential of computer vision in security in the future and pointing the way toward further research. Ultimately, this study intends to focus on how the status of learning algorithms and related approaches might inform future advancements in the field of cybersecurity.