Journal Articles - Computer and Information Technology - 2022

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  • Publication
    Research and Develop the Apt Defense from Attack System Using Sandbox Technique
    ( 2022)
    Lý Hải Sơn
    ;
    Đỗ Thanh Tùng
    ;
    Trần Thị Yến Nhi
    Advanced Persistent Threat is an insidious, persistent, and with a specific aim attack into a target system. According to statistics in Vietnam and the world, many APT attacks cause significant negative effects. Therefore it is crucial to find a solution to combat these attacks. This thesis will give a comprehensive introduction about APT attacks; their specific features; preventative measures; malware analysis and its technique, static analysis technique; sandboxing technique; automatic analysis and report about malware behaviors without human intervention; and suggest an integrated model solution that detects and prevents APT using sandboxing technique. Through installation trials, the thesis proves that the sandboxing technique can analyze and detect malware behaviors on the system.
  • Publication
    On Performance of Downlink Non Orthogonal Multiple Access Wireless System Relying on UAV
    ( 2022)
    Nguyễn Đức Nhân
    Wireless communications relying on Unmanned Aerial Vehicles (UAVs) is a promising candidate in future communication systems. Compared with conventional terrestrial communications, UAV networks has some significant benefits including cost-effectiveness, mobility, fast deployment, higher probability of Line-of-Sight (LoS) links between UAV and ground terminals. Based on ideal conditions of high Signal-to- Noise Ratio (SNR), Non-Orthogonal Multiple Access (NOMA) with fixed power allocation factor is adopted to indicate performance of two users. These ground user from UAV is independent of channel and trajectory parameters is further evaluated under impact of imperfect channel state information (CSI). We drive the closed-form formula of outage probability for two ground users. Simulation results provide impacts of system parameters on outage behavior.
  • Publication
    Filtering and Tracking of Spreading of Information on Social Networks Using a Combination of LDA, SVM, and Naive Bayes Models
    ( 2022)
    Trần Thị Yến Nhi
    ;
    Nguyễn Chí Toàn
    ;
    Nguyễn Hoàng Trung
    Collecting and analyzing big amounts of data to extract useful data is a big challenge that we face in modern society. This presents great opportunities and challenges in the field of computer science research. If successfully analyzed and made sense of, data can help determine market trends, growth trends of an organization or stop the spread of information on social media. In this paper, the authors will conduct research on the theories of the Latent Dirichlet Allocation model (LDA), algorithmic Gibbs sampling, Support Vector Machine (SVM), Naive Bayes theorem, and the Waikato Environment for Knowledge Analysis (Weka). The authors also analyze and design the research system. This research will construct an empirical system to aid in the qualification and control of information on social media, detect implicit themes and potentially negative messages, trace the spreader of this news, and determine the speed of this news spreading. We aim to finalize a support system to aid with decision-making in the research that focuses on hot topics and development trends in the future and stop the spread of negative information and fix it.