# Artificial Intelligence in Cybersecurity: A Review and a Case Study
## 1 Introduction
Finally, the paper concludes by summarizing our key findings in cybersecurity. The methodology of our work explores the potential AI applications to enhance cybersecurity through a two-pronged approach: a literature review and a case study. To contextualize our work within the existing body of knowledge, a concise review of recent research is presented in Sections 2 and 5. This section adheres to the established data collection and inclusion criteria such as:  
Data Collection: To explore the current state of AI use in cybersecurity in a concise way, we performed a focused review of recent academic publications. This involved searching reputable academic databases and publications for relevant research focusing on AI for threat prevention. Inclusion Criteria: To capture the rapidly evolving field of AI technologies, our selection process prioritized the most recent publications. We specifically focused on studies that addressed preventative measures against common threats like phishing, social engineering, ransomware, and malware. To illustrate the practical applications of the AI concepts, Section 6 presents a case study. This case study delves into the use of GA as a sub-class of AI to secure communication within IEEE 802.15.4 networks. By examining this specific example, we aim to showcase both the real-world benefits of AI in cybersecurity and any potential limitations associated with these technologies. The following considerations guided the development of our case study:  
Addressing a Gap: We identified a critical security vulnerability in communication protocols used by resource-constrained IoT and WSN networks adhering to the IEEE 802.15.4 standard. This standard is not compatible with traditional encryption methods used in Wi-Fi (WEP, TKIP, CCMP) due to their high processing power and energy consumption requirements. Proposed Solution: To address this gap, we propose a novel encryption methodology specifically designed for IEEE 802.15.4 communication. This methodology leverages a GA, which is a popular sub-class of AI algorithms, to generate a unique symbol-to-chip sequence table, essentially acting as a cipher for data transmission. Unlike the standard protocol's publicly available table, our solution ensures anonymity and renders data unreadable for passive eavesdroppers on the communication channel. Evaluation: To assess the effectiveness of our proposed encryption method under realistic conditions, we employed an experimental platform. This platform simulated varying chip error rates (\(\rho\)) ranging from 0% to 3%, representing low-noise environments like an office space. We maintained the default IEEE 802.15.4 protocol parameters and eliminated potential collisions to isolate the impact of error-causing factors on communication throughput. The following sections explore the potential of AI in combating cybersecurity threats. Section 2 reviews recent studies on applying AI to identify and prevent phishing attacks. Section 3 focuses on their role in mitigating social engineering tactics. Sections 4 and 5 delve into the use of AI against ransomware and malware, respectively. Section 6 presents a case study to showcase a specific application of AI in cybersecurity. Finally, Section 7 concludes the paper by summarizing the key findings and highlighting future directions.