# Artificial Intelligence in Cybersecurity: A Review and a Case Study
## 1 Introduction  
Artificial intelligence (AI) is an emerging tool offering promising solutions to the complex problems of cybersecurity. This manuscript delves into their applications in this critical field. We explore the current state-of-the-art advancements in these fields, focusing on four key subcategories: phishing, social engineering, ransomware, and malware. For each category, we present a focused analysis exploring the specific techniques and methodologies that leverage AI to address these prevalent threats. This work employs a comparative approach utilizing descriptive analysis and in-depth discussions to illuminate the strengths, weaknesses, and opportunities for further research within AI-powered cybersecurity solutions. The past decade (2013-2023) has been marked by a surge in complex and financially damaging cybersecurity threats. These major incidents, often exceeding a million dollars in financial impact, pose a critical challenge to both global security and economic stability. Consequently, understanding the evolution and patterns of these attacks over the past decade is crucial for researchers in the field. In their study, the authors of [1] identified a significant escalation in the frequency and complexity of cyberattacks. DDoS incidents surged in 2022, while malware attacks steadily increased, culminating in a peak in 2023. This trend underscores the growing sophistication of threat actors and the vulnerability of digital infrastructures. Furthermore, the combined impact of other attack methods, including phishing and zero-day exploits, surpassed that of DDoS and malware, revealing the diverse nature of cyber threats [1]. Secure communication is an essential pillar of cybersecurity in the IT domain. However, wireless communication presents a unique challenge due to its inherent vulnerability. Unlike wired connections, wireless data travel through the airwaves, essentially broadcasting information. Because the data are broadcast, they can be intercepted. Attackers withmalicious intent can intercept this broadcast communication. We have addressed them by providing a concise review of the latest applications of AI in cybersecurity, focusing on prevalent threats like phishing, social engineering, ransomware, and malware. To bridge the gap between theory and practice, we showcase a specific case study. This case study explores how a genetic algorithm (GA), a subfield of AI, secures communication within IEEE 802.15.4 networks. We chose this specific example because it highlights a critical gap in securing low-power, resource-constrained networks like those used in the ever-growing IoT and wireless sensor networks (WSNs). Encryption, which scrambles data using cryptographic algorithms to ensure only authorized users can access it, plays a critical role in securing communication on wireless networks. This protects sensitive information. It does this while the information is being sent. This works even on open wireless networks. The IEEE 802.15.4 protocol is a popular choice for wireless communication in industrial and home appliance applications due to its suitability for low-power, low-data-rate sensor networks and Internet of Things (IoT) devices. IEEE 802.15.4 offers good performance in noisy environments. However, it is more susceptible to cyber attacks. This is because it uses less complex encryption algorithms compared to protocols like IEEE 802.11. To exemplify the potential of AI in wireless communication, we present a case study involving the development of an anonymous encryption methodology for IEEE 802.15.4 networks. Our case study includes GA application. GAs are a type of AI. We use GAs for anonymous communication. At the same time, we analyze how network performance can be kept optimal. Such a proposal departs from traditional hash functions and register-based encryption mechanisms. It is based on deriving a genetically derived pseudo-random noise (PN) sequence. A comparison of the IEEE 802.15.4 PN sequence and our proposal is presented in throughput analyses.