Malware outperforms conventional signature-based techniques by posing a dynamic and varied threat to digital environments. In cybersecurity, machine learning has become a potent device, providing flexible and data-driven models for malware identification. The significance of choosing the optimal method for this purpose is emphasized in this review paper. Assembling various datasets comprising benign and malicious samples is the first step in the research process. Important data pretreatment procedures like feature extraction and dimensionality reduction are also included. Machine learning techniques, ranging from decision trees to deep learning models, are evaluated based on metrics like as accuracy, precision, recall, F1-score, and ROC-AUC, which determine how well they distinguish dangerous software from benign applications. A thorough examination of numerous studies shows that the Random Forest algorithm is the most effective in identifying malware. Because Random Forest can handle complex and dynamic malware so well, it performs very well in batch and real-time scenarios. It also performs exceptionally well in static and dynamic analysis circumstances. This study emphasizes how important machine learning is, and how Random Forest is the basis for creating robust malware detection. Its effectiveness, scalability, and adaptability make it a crucial tool for businesses and individuals looking to protect sensitive data and digital assets. In conclusion, by highlighting the value of machine learning and establishing Random Forest as the best-in-class method for malware detection, this review paper advances the subject of cybersecurity. Ethical and privacy concerns reinforce the necessity for responsible implementation and continuous research to tackle the changing malware landscape.

The popularity of mobile games has expanded among individuals of all ages, and the mobile gaming businesses are quickly expanding day by day. The Google Play Store, one of the most well-known platforms for the distribution of Android applications and games, sees a daily influx of thousands of new mobile games. One of the biggest problems in the gaming industry is predicting a mobile game's performance. Every day, thousands of new games are released. But just a couple of them are successful, while most of them fail. The study was done with the intention of analyzing any relationship between a mobile game's success and its distinctive features. Many of the mobile game developers work independently or work in the mobile game industries to make their games successful on the digital market. Before they are released, game makers can increase the quality of their games if they are confident in their products' commercial viability. For that reason, more than 17,000 games were taken into consideration. We show that the success of a mobile game is clearly influenced by its category, number of supported languages, developer profile, and release month. Furthermore, we show that specific aesthetic features of game symbols are more frequently linked to higher rating counts. We analyzed Google Play Store mobile games data and used a variety of machine learning algorithms for predicting the performance of mobile games based on the total number of downloads and the total user rating.