# Artificial Intelligence in Cybersecurity: A Review and a Case Study
## 5 Defending Against Malware  
Malicious software, also known as malware, poses a significant threat to the IT industry. The recent surge in malware attacks has become a major challenge. Malware can infiltrate computer systems without authorization, leading to a variety of harmful consequences. These consequences often include data theft and system corruption. The increasing popularity of mobile devices, particularly those running Android, necessitates the development of robust security solutions. However, this widespread adoption also creates a larger target for mobile malware infections. To address this critical issue, [PERSON] et al. [24] propose an ML-based approach for anomaly detection on Android devices. Their system utilizes the power of three machine learning algorithms, including K-nearest neighbors (KNN), naive Bayes, and decision tree. They analyzed mobile application behavior and identified potential malware vulnerabilities. As demonstrated in this study, ML methods offer a powerful approach to combating the growing malware threat. These methods provide a means to analyze and classify large amounts of data. They allow malware to be identified. Their successes hold true even when using obfuscation techniques to evade traditional signature-based detection. One of ML approaches is proposed by [PERSON] et al. [25]. Their approach is based on a classification technique for classifying Windows PE files. This technique is trained on a substantial dataset of roughly 100,000 Brazilian malware samples. Each sample is characterized by 57 features. The authors explore various machine learning models, achieving the highest accuracy of 99.7% with a random forest model. This result shows the effectiveness of the random forest model in differentiating between benign and malicious files, suggesting its potential as a valuable tool for system security. Polymorphic malware is a new and highly adaptable form of malicious software. It poses a significant challenge to traditional signature-based detection methods. This type of malware constantly modifies its code to evade identification. This renders signature-based approaches ineffective. To address this growing threat, [PERSON] et al. [26] propose an ML approach for such types of malware attacks. Their approach utilizes various algorithms. These algorithms include naive Bayes, support vector machines (SVMs), J48, random forest (RF), and their own proposed method. They employed them on a large dataset. They chose the model with the highest accuracy and lowest error rate based on their analysis of detection rate and false positive/negative rates. These rates are measured by the confusion matrix. This analysis provides effective differentiation between benign and malicious traffic on computer networks. The analysis focuses on the difference in correlation symmetry integrals and demonstrates the effectiveness of ML in detecting highly adaptable malware. Contemporary cybersecurity methods are increasingly burdened by sophisticated malware. This malware is typically characterized by rapid spread, self-propagation, and advanced evasion tactics. These characteristics allow malware to evade near real-time detection and forensic analysis. AI presents itself as a potential solution to address this growing cybersecurity challenge. In a recent study ([27]), the authors propose a novel systematic approach for identifying modern malware families. This approach utilizes a combination of dynamic DL methods and heuristic techniques to achieve classification and detection of different malware types. Their research explores the application of symmetry analysis within the context of malware detection. This application aims to improve detection capability, analysis performance, and mitigation strategies. Ultimately, their research strives for the development of more resilient cyber-systems against evolving threats.