# Cybersecurity Threats and Their Mitigation Approaches Using Machine Learning--A Review
## 1 Introduction  
With the rapidly increasing prominence of information technology in recent decades, various types of security incidents, such as unauthorized access [1], denial of service (DoS) [2], malware attacks [3], zero-day attacks [4], data breaches [5], social engineering or phishing [6], etc., have increased at an exponential rate in the last decade. In 2010, the security community documented less than 50 million distinct malware executables. In the year 2012, this reported number doubled to around 100 million. From the record according to AV-TEST statistics, the security industry detected over 900 million malicious miscutables in 2019, and this number is rising [7]. Cybercrime and network attacks can result in significant financial losses for businesses and people. For example, according to estimates, an average data breach costs USD 3.9 million in the United States and USD 8.19 million globally [8], and cybercrime costs the world economy USD 400 billion per year [9]. The security community estimates [10], over the next five years, that the number of records broken will nearly quadruple. As a result, to minimizeIt is also dependent on businesses that give access to their employees, who possess the capacity and knowledge to identify such cyber-threats quickly and effectively. As a result, the primary concern that must be addressed immediately is to intelligently identify various cyber occurrences, whether previously known or unseen, and safeguard critical systems from such cyber-attacks adequately. Cybersecurity refers to technologies and techniques that protect programs, networks, computers and data from being damaged, attacked or accessed by unauthorized people [12]. Cybersecurity covers various situations, from corporate to mobile computing, and can be divided into several areas. These are: (i) network security, which focuses on preventing cyber-attackers or intruders from gaining access to a computer network; (ii) application security, which considers keeping devices and software free of risks or cyber-threats; (iii) information security, which primarily considers the security and privacy of relevant data; and (iv) operational security refers to the procedures for handling and safeguarding data assets. Traditional cybersecurity solutions include a firewall, antivirus software or an intrusion detection system in network and computer security systems. Data science is driving the transformation, where machine learning, an essential aspect of \"Artificial Intelligence\", can play a vital role in discovering hidden patterns from data. Data science is pioneering a new scientific paradigm, and machine learning has substantially impacted the cybersecurity landscape [13, 14]. As discussed in the article [15], with the advancement of technologies pertinent to launching cyber threats, attackers are becoming more efficient, giving rise to an increasing number of connected technologies. The graph in Figure 1 depicts timestamp data in terms of a specific date, with the \(x\)-axis representing the matching popularity and the \(y\)-axis representing the corresponding popularity in the range of 0 (minimum) to 100 (maximum). It is observed that the popularity values of cybersecurity and machine learning areas were less than 30 in 2015, and they exceeded 70 in 2022, i.e., more than double in terms of increased popularity. In this study, we focus on machine learning in cybersecurity, which is closely related to these areas in terms of security, intelligent decision making and the data processing techniques to deploy in real-world applications. Overall, this research is concerned with security data, using machine learning algorithms to estimate cyber-hazards and optimize cybersecurity processes. This project is also useful for academic and industrial researchers interested in studying and developing data-driven smart cybersecurity models using machine learning approaches. Figure 1: Google Trend for machine learning vs. data science vs. cybersecurity from 2015 to present.