# Artificial Intelligence in Cybersecurity: A Review and a Case Study
## 2 Phishing Attacks  
Phishing attacks are the most common cybercrime recently. They use fake emails that appear to be from someone they trust. These emails try to steal valuable information. The recipient is tricked into giving away personal information. This information can include sensitive data like passwords and credit card details. The attackers act like fishermen, using a tempting facade to lure unsuspecting victims into their trap. Stolen information can fuel financial crimes or malicious acts, but user awareness and strong online security are our best defenses against these evolving threats. Phishing attacks are not limited to email. Phishers use a scattered approach, employing misleading messages across various communication channels. This includes instant messaging, online forums, and social media. These messages often contain a deceptive link leading to a fake website designed to steal your information. This widespread method significantly increases the chances of a user clicking and unknowingly logging in with their username and password on the fake website. This is how phishing attacks steal login credentials. The malicious intent behind phishing is often cleverly disguised. Therefore, caution online is crucial. By acquiring stolen login credentials, phishers gain the potential to launch a variety of cybercrimes, all stemming from a single unsuspecting click. Machine learning (ML) and deep learning (DL) can be powerful tools in identifying patterns that reveal malicious intent in these attacks. These techniques analyze vast amounts of data to diagnose phishing attempts in real time. Similar to automated intelligence, ML and DL act as powerful decision-making tools within management information systems. ML and DM act as powerful tools for cybersecurity. They analyze vast amounts of data to uncover hidden attacking patterns. So, they can provide better planning and mitigation strategies. By employing these strategies, we can empower organizations with a robust toolkit for data analysis, encompassing threat identification through email content examination, historical malicious activity recognition, and threat classification for enhanced investigation. Phishing detection can be addressed through a technique called classification. Like a detective, this method sorts websites into categories. These categories include legitimate, suspicious, and phishy. By classifying websites, it improves cybersecurity decisions [3; 4]. In the study [4], the authors analyzed various website characteristics to predict their type. They built a training dataset by pairing these characteristics with known website classifications. The objective is to create a classifier. The proposal suggests a new system. This system works like an automated detective. It examines websites at first. It finds hidden patterns in data used for training. Based on these patterns, it can identify the type of website. A website classifier's effectiveness hinges on the strength of its feature-to-classification linkages, with accuracy measured by the alignment between predicted and real-world website types. Effective research is being conducted on phishing detection. A key study by [PERSON] et al. explores how selecting appropriate features can enhance ML-based phishing detection [5]. The authors investigate the impact of classifier type on accuracy and employ various methods to optimize detection performance. They analyze the results of using different features and observe the influence of diverse phishing attacks. To achieve this,they created a new dataset and tested various website classification methods. They experimented with different feature sets for each classification method, evaluating each method's effectiveness based on accuracy, true positive rate (catching phishing attempts), false positive rate (flagging safe sites), and processing speed. Their findings suggest that features based on URLs and HTTP protocols yield superior performance, indicating that focusing on these specific aspects can improve phishing detection accuracy.