# Artificial Intelligence in Cybersecurity: A Review and a Case Study
## 1 Introduction
A recent study [2] proposes using multiple PN codes (S3, S5, S7, and S9) with different spreading factors. These spreading factors are found using GA. The goal is to improve throughput in channels with different chip error rates. The primary distinction between the work presented in [2] and this study lies in the utilization of spreading factors and PN codes, as well as the operational methodology for ensuring secure communication. While ref. [2] prioritizes achieving higher throughput under varying channel conditions, our research focuses on enhancing the level of secure communication while maintaining throughput levels comparable to generic IEEE 802.15.4. The authors of [2] proposed publicly available codes (S3, S5, S7, and S9) that operate at different spreading factors than the standard IEEE 802.15.4 PN codes. Their proposed mechanism switches between spreading factors based on changing channel conditions, adapting to varying chip error rates. In contrast, our approach maintains a fixed spreading factor throughout the communication process, resembling the standard IEEE 802.15.4 PN operation. Furthermore, the PN codes employed in [2] are pre-generated offline and used for subsequent operations. Our proposal, however, allows users to generate codes dynamically, even during runtime, in a public-blind manner, thereby bolstering the overall security of the system. The key findings of our proposals are as follows:  
Effective PN Sequence Discovery: We implemented a GA as a method for generating sequences. We used this method to create a new PN sequence. This PN sequence is for IEEE 802.15.4 networks. Our new sequence offers a viable alternative to the standard's default set, enhancing communication security. Preserved Noise Characteristics: The newly discovered PN sequence exhibits pseudorandom noise characteristics comparable to the default sequences defined in the IEEE 802.15.4 standard, ensuring compatibility and functionality within the network. Maintained Throughput: The GA-derived sequence is good at handling data. It works just as well as the default PN sequence set. This means that adding security features does not slow down how fast data travels. Re-discoverable Sequence for Anonymity: The GA scheme allows for the re-discovery of the similar default PN sequence using the same GA parameters. This re-discoverability feature provides anonymity in the sequence set, further bolstering communication security. Enhanced Security through Lack of Production Mechanism: Unlike traditional methods that rely on shift registers or hash functions for sequence generation, the GA-derived sequence lacks a predefined production mechanism. Not knowing how messages are created on-channel plays an important role. This makes it harder to understand what is being communicated. It also adds another layer of anonymity for the people using this channel. Hardware-Level Security: The GA-discovered sequence can be embedded directly into the wireless transceiver chip. Because it is performed on hardware, not software, we can make security stronger and harder to break. Our research utilizes a genetic algorithm (GA) to generate a secure pseudo-random noise (PN) sequence for IEEE 802.15.4 networks. This approach achieves performance comparable to the existing standard while offering superior security due to the anonymity and unknown nature of the generation mechanism. Additionally, hardware-level implementation holds promise for a more dependable and tamper-proof security solution. Our paper examines the intersection of AI in cybersecurity. Following this section, we delve into a focused examination of current AI applications used for cybersecurity purposes. This review will focus on four prevalent cyber threats: phishing, social engineering, ransomware, and malware. Subsequently, we propose a novel method for information encryption that leverages the power of a GA. We will meticulously dissect the proposed GA model, unveiling its intricate details and functionalities. Furthermore, a rigorous evaluation of the model's performance will be presented.