A review on AI-based techniques for malware identification and prevention Available to Purchase

As a result of the surge in malware activity that threatens the security and safety of computer systems as well as stakeholders, security has grown significantly in importance with the quick development of technology. Preserving the data from fraudulent attempts is one of the most important things to do in order to ensure the security of stakeholders, especially end users. Malware is a collection of invasive software, scripts, malicious programming code, or active material that is meant to damage computer systems, software, mobile apps, and online apps. A study found that unsuspecting users are unable to tell the difference between safe and harmful apps. As a result, in order to safeguard the stakeholders, computer systems and mobile applications should be built to recognize malicious activity. Numerous techniques that make use of cutting-edge ideas like artificial intelligence, machine learning, and deep learning are available to detect malware activity. In this work, we focus on methods for identifying and stopping malware activity that are based on artificial intelligence (AI). We provide an in-depth analysis of the weaknesses of the most recent malware detection methods as well as recommendations for enhancing their effectiveness. According to our analysis, there will be major benefits to developing malware detection apps using futuristic techniques. Understanding this synthesis will aid future research on AI-based malware detection and prevention.