RAPID: AI-driven Innovations for COVID-19 Themed Malware Detection

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The fast evolving and deadly outbreak of coronavirus disease (COVID-19) has posed grand challenges to human society. In the fight against the global pandemic, many social activities have moved online. Society's unprecedented reliance on the complex cyberspace makes its security more important than ever. Unfortunately, utilizing both fear and financial incentives, cyber threat actors are using COVID-19 or coronavirus as a lure all over the spectrum of sophistication to spread malware (i.e., software that deliberately fulfills the harmful intent to legitimate users) to gain profits from the pandemic. The malware with a COVID-19 theme (e.g., CovidLock, COVID-19 Banking Trojans) have become more and more sophisticated and resilient by using various tactics to fool the defenders and bypass their detection. This points to an imminent need for innovative techniques to combat the exponential growth of increasingly sophisticated COVID-19 themed malware so that users can be better protected in the cyberspace. By advancing capabilities of artificial intelligence (AI), the goal of this project is to develop innovative links between AI and security to design and develop an integrated framework for COVID-19 themed malware detection to help mitigate its negative effects on public health, society, and the economy. The outcomes of this project (including open-source codes and generated benchmarks) will be made publicly available. The project integrates research with education through innovative curriculum development, student mentoring activities, and broadening participation of underrepresented groups.The research has three key components. First, in addition to using content-based features, the project will develop a novel heterogeneous information network to characterize and represent applications (apps) and their complex social relations within the new ecosystem in a comprehensive manner. Second, the team will develop an innovative adversarial disentangler to separate the distinct, informative factors of variations hidden in the app representations needed for large-scale COVID-19 themed malware detection. Third, the team will design and develop a deep learning based classifier with interpretability enhancement for the detection and understanding of how malware spread. The developed framework for the understanding of how malware spread will facilitate a predictive understanding of the spread of coronavirus. By providing the system for COVID-19 themed malware detection to reduce mental anguish and financial loss for users, the planned work will help mitigate the negative effects of COVID-19 on public health, society, and the economy. The proposed research will be beneficial to multidisciplinary areas, including phishing fraud detection, spam filtering, and other domains such as data mining and machine learning where multiple data sources are involved.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.