Application of Machine Learning in Cybersecurity: A Technological Perceptive

Cybersecurity is defined as a comprehensive toolkit and that are developed to safeguard the data of computer and stop the entry of malicious software. The innovative strategies are needed to provide protection to the crucial system that handles sensitive data. The chapter discusses the different application of machine learning algorithm that is applied to the field of cybersecurity. We have covered key areas such as analysis of malware, anomaly detection, and covering bolstering intelligence. Machine learning techniques are renowned for their adaptability and capacity to learn. While they excel at detecting, containing, and thwarting known malware attacks, certain types of attacks may surpass current cybersecurity solutions. Falling under the umbrella of artificial intelligence, machine learning employs a series of arithmetical operations to excerpt and analyze pertinent data, uncover novel features, and help in decision-making processes. This chapter covers how different machine learning algorithm can adapt to continuously changing threats and adapting itself to handle such situations. We aim to elucidate the complexities that are involved in this area by covering the contribution of machine learning algorithm to strengthen the cybersecurity. Hence, the field of threat detection has evolved significantly with the advent of machine learning, a data-driven approach that places a strong emphasis on understanding the underlying data. In this research paradigm, we delve into machine learning applications to intrusion systems, focusing on different data types such as packets, flows, sessions, and logs.