# Cybersecurity Threats and Their Mitigation Approaches Using Machine Learning--A Review
## 1 Introduction
Preventing cybersecurity attacks beyond a set of fundamental functional needs and knowledge about risks, threats or vulnerabilities requires analyzing cybersecurity data and building the right tools to process them successfully. Several machine learning techniques, which include but are not limited to feature reduction, regression analysis, unsupervised learning, finding associations or neural network-focused deep learning techniques, can be used to effectively extract the insights or patterns of security incidents. This is briefly discussed in the \"Machine learning techniques in cybersecurity\" section. These learning techniques can detect anomalies or malicious conduct and data-driven patterns of related security issues and make intelligent judgments to avert cyber-assaults. Machine learning is a partial but significant departure from traditional well-known security solutions, including user authentication and access control, firewalls and cryptography systems, which may or may not be effective in meeting today's cyber business needs [16, 17, 18]. The critical difficulty is that domain experts and security analysts fix these manually in situations where ad hoc data management is required [19]. However, as a growing number of cybersecurity incidents in various formats are emerging over time, traditional solutions have proven ineffective in managing these cyber-hazards. As a result, a slew of new, complex attacks emerges and spreads rapidly over the network. Thus, several academics apply diverse data analytic and knowledge extraction models to create cybersecurity models, which are covered in the section \"Machine learning techniques in cybersecurity\", based on the efficient identification of security insights and the most recent security trends that may be more relevant. According to research, addressing the cyber problem necessitates the development of more flexible and efficient security systems that can adapt to attacks and update security policies to eradicate them on a timely basis intelligently. To do this, a huge amount of relevant cybersecurity data collected from different sources, such as network and system sources, must be analyzed. Moreover, these techniques should be implemented in a way that increases automation, with minimal to no human intervention. The discussions of this study are listed below. * To comprehend the applicability of data-driven decision making, a report on the existing idea of cybersecurity protection plans and associated approaches is presented first. To do this, several machine learning techniques used in cybersecurity have been discussed and numerous cybersecurity datasets, emphasizing their importance and applicability in this domain, have been presented. * In addition, an examination of several related research challenges and future objectives in the field of cybersecurity machine learning approaches has been presented. * Finally, the most common issues in applying machine learning algorithms on cybersecurity datasets have been explored within the scope of improvements to build a robust system. The rest of this research is structured as follows. Section 2 describes the motivation for our research and provides an overview of cybersecurity technologies. Next, it defines and briefly covers numerous types of cybersecurity data, including cyber incident data. A brief discussion of different categories of machine learning techniques and their relationships with various cybersecurity tasks is presented in Section 3. The section also summarizes a number of the most effective machine learning algorithms for cybersecurity models in this domain. Section 4 briefly covers and emphasizes different research concerns and future directions in cybersecurity. Finally, an emphasis on some crucial elements of our findings has been presented in Section 5.