## 1 Introduction  
Cybersecurity is an emerging field within computer science that protects computer networks, information systems, and data from cybercriminals, hackers, and data breaches. Cyber threats increase due to the progression of the latest technologies, combined with the extended adoption of integrated applications. The growing frequency of such incidents and their level of complexity make the traditional protection methods insufficient, which is why new, more effective, and predictive measures are required [1]. This is where artificial intelligence (AI) is a pioneering force in revolutionising cybersecurity technologies. Over the years, AI and ML have emerged as significant contributors to today's cybersecurity strategies [2]. These technologies utilise data analytical models to enhance threat identification and mitigation more efficiently, coupled with accuracy, than traditional methods [3]. AI is the branch of computer science which replicates human cognitive abilities,particularly in making decisions and solving problems [4]. AI is further divided into ML, in general, and DL, in particular, which incorporates complex neural networks to analyse large chunks of data, seeking to identify one previously unknown pattern typical for a cyber threat [5]. [PERSON], [PERSON] [6] stated that intrusion detection, anomaly detection, and malware classification are some tasks in which ML techniques have been applied to achieve better results in cybersecurity. ML algorithms can, for instance, learn from network traffic to detect outliers or learn from malware features. In contrast, DL excels at more sophisticated threat detection tasks, including the categorisation of zero-day vulnerabilities or being shielded against particularly sophisticated phishing methods [7]. According to the type of learning, ML models can be supervised, unsupervised and semi-supervised. Supervised ML models learn from labelled data; unsupervised ML models learn from unlabelled data; and semi-supervised ML models learn from a combination of both [8]. Apart from the core of ML, ML's subset DL uses neural networks with several layers (hence, 'deep') for working with large datasets to understand complex and large patterns like images, sound, and sequential data [9]. A notable use case for DL in cybersecurity is forming advanced threat detection systems, which can decipher novel cyber-attack types by studying large amounts of unstructured data in network traffic, system logs, and other such data [10]. ML models need to analyse vast data to identify complex cyber threats, which requires substantial computational power. For example, IDS and malware analysis require massive data processing and robust decision making in real time [11]. This high computational demand emerged when processing the multi-dimensional network traffic data, analysing patterns from the data and implementing the complex learning models are needed. The computational complexity of some of the most used ML algorithms is presented in Table 1.  
ML-based cybersecurity solutions comprised human resources, the regulatory framework, and cryptography, underscoring the importance of multidisciplinary interconnection [13]. The cybersecurity regulations, along with the legal limitations, ensure data privacy and compliance. At the same time, cryptography techniques support data encryption for securing information. Finally, human awareness, training, and explainable AI are crucial in shaping security measures for an enhanced secure environment against attacks. ML and DL have been growing across a wide array of cybersecurity domains. End-point security leverages ML to identify malware by analysing the behaviour of files and