An AI-driven Security Information and Event Management system for enhancing the security of e-Government services

Project Leader: Assoc. Prof. Tran Quang Duc

Date: October 2017 – December 2019

E-Government refers to the use of IT by government agencies who are responsible for transforming information between people, businesses, and all other governmental stakeholders. The objectives of e-government include better delivery of public services to people, enhancing business and industry collaborations, citizen empowerment through access to information, or more effective governance. For e-Government services, cyber security presents a unique problem due to the numerous threats that the government agencies must face on daily basis and the scale of the consequences if the threats are not properly handled.

In this research project, we develop an AI-driven security information and event management system. Such a system provides automated, continuous analysis and correlation of all activities collected within the e-Government services.In this research project, we develop an AI-driven security information and event management system. Such a system provides automated, continuous analysis and correlation of all activities collected within the e-Government services.

Publications

• Tran, H. Mac, V. Tong, H.A. Tran, L.G. Nguyen, “A LSTM based Framework for Handling Multiclass Imbalance in DGA Botnet Detection,” Neurocomputing (275), January 2018.
• Hiếu Đình Mạc, Tùng Trọng Bùi, Đức Trần Quang, Giang Nguyễn Linh, Phương pháp phát hiện DGA Botnet dựa trên CNN và Bidirectional LSTM, Tạp chí Thông tin và Truyền thông, 551 (741), Tháng 12, Năm 2017 (Giải thưởng bài báo xuất sắc nhất tại Hội thảo SoIS 2017).
• Tong, H.A. Tran, S. Souihi, and A. MELLOUK, “A novel QUIC traffic Classifier based on Convolutional Neural Networks,” IEEE Global Communications Conference (Globecom), Abu Dhabi, UAE, 2018.
• Tong, H.A. Tran, S. Souihi, and A. MELLOUK, “Empirical study for Dynamic Adaptive Video Streaming Service based on Google Transport QUIC protocol,” The 43rd IEEE Conference on Local Computer Networks (LCN), Chicago, USA, 2018.
• H. Du, H.C. Nguyen, K.K. Nguyen, N.H. Nguyen, “An Efficient Parallel Algorithm for Computing the Closeness Centrality in Social Networks,” The 9th International Symposium on Information and Communication Technology (SoICT 2018), Da Nang, Vietnam, December 2018.
• G. Le, H.T. Nguyen, D.P. Pham, V.O. Phung, N.H. Nguyen, “GuruWS: A Hybrid Platform for Detecting Malicious Web Shells and Web Application Vulnerabilities,” Transactions on Computational Collective Intelligence XXXII, pp. 184-208, Springer, Berlin, Heidelberg.
• Tống Văn Vạn, Mạc Đình Hiếu, Bùi Trọng Tùng, Trần Quang Đức, Nguyễn Linh Giang, Phương pháp cải tiến LSTM dựa trên đặc trưng thống kê trong phát hiện DGA Botnet, Tạp chí Công nghệ thông tin và Truyền thông (Chuyên san: Các công trình nghiên cứu, phát triển và ứng dụng CNTT-TT), vol. 3, no. 40, pp. 33-42 (ISSN: 1859-3526), 2019.
• Tran, H. Mac, L.G. Nguyen, “A method for detecting DGA-malware infected machines,” VN Patent, Patent No. 10031187, 2022.