| Autor: |
Faruk, Md Jobair Hossain, Shahriar, Hossain, Valero, Maria, Barsha, Farhat Lamia, Sobhan, Shahriar, Khan, Md Abdullah, Whitman, Michael, Cuzzocreak, Alfredo, Lo, Dan, Rahman, Akond, Wu, Fan |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
2021 IEEE International Conference on Big Data (Big Data) |
| Druh dokumentu: |
Working Paper |
| DOI: |
10.1109/BigData52589.2021.9671434 |
| Popis: |
With the rapid technological advancement, security has become a major issue due to the increase in malware activity that poses a serious threat to the security and safety of both computer systems and stakeholders. To maintain stakeholders, particularly, end users security, protecting the data from fraudulent efforts is one of the most pressing concerns. A set of malicious programming code, scripts, active content, or intrusive software that is designed to destroy intended computer systems and programs or mobile and web applications is referred to as malware. According to a study, naive users are unable to distinguish between malicious and benign applications. Thus, computer systems and mobile applications should be designed to detect malicious activities towards protecting the stakeholders. A number of algorithms are available to detect malware activities by utilizing novel concepts including Artificial Intelligence, Machine Learning, and Deep Learning. In this study, we emphasize Artificial Intelligence (AI) based techniques for detecting and preventing malware activity. We present a detailed review of current malware detection technologies, their shortcomings, and ways to improve efficiency. Our study shows that adopting futuristic approaches for the development of malware detection applications shall provide significant advantages. The comprehension of this synthesis shall help researchers for further research on malware detection and prevention using AI. |
| Databáze: |
arXiv |
| Externí odkaz: |
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