โครงการวิจัยนี้มุ่งเน้นการตรวจจับมัลแวร์ที่ฝังตัวกับแอปพลิเคชั่นปกติ ซึ่งจะไม่แสดงพฤติกรรมที่น่าสงสัยและเป็นอันตรายในตอนต้น และเพิ่มประสิทธิภาพของการตรวจจับโค้ดอันตรายบนอุปกรณ์เคลื่อนที่
Research Project on Cybersecurity
1. Detection and monitoring malicious applications on android mobile devices (funding by the national broadcasting and telecommunication commission : NBTC during 2016-2017)
AUTHORS: Chonthorn Ariyapitipan and Kingkarn Sookhanaphibarn
ABSTRACT: This paper presents a systematic approach for video-audience-watching content analysis. Our analysis technique is based on audience preference that they will click Like while watching a video clip. The principal component analysis is applied to extract the content structure of a video clip. The hierarchical clustering technique also is used to segment a video content as time slots. The analysis and clustering are based on the audience opinion. In our experiment, the approach is set to teaching video content for 22 4-year students in Bachelor degree. The analysis results show the three principal structures that can represent three principal video-audience-watching preference styles.
AUTHORS: Worawat Choensawat and Kingkarn Sookhanaphibarn
ABSTRACT: Visual aircraft recognition (VACR) is a visual skill taught to military personnel to recognize the external appear-ance of the aircraft, both friendly and hostile, most likely to be encountered. It is important for air defense and military intelligence gathering. In training, many media are used such as scale models, printed silhouette charts, slide projectors, and computer-aided instruction. However, none of the above media allows practitioners to experience real environment-liked such as visibility on rainy days, cloudy days, nighttime and the actual flight characteristics of aircraft. This paper proposed a simulation system based on virtual reality for VACR training that allows training practitioners in realistic virtual environments and able to evaluate the effectiveness of the training. The system consists of the various types of aircraft models, AI modules for weather simulation and flight patterns according to the characteristics of each type of aircraft, terrain modeling, and the evaluating system.
AUTHORS: Tossaporn Santad, Piyarat Silapasupphakornwong, Worawat Choensawat, and Kingkarn Sookhanaphibarn
ABSTRACT: We proposed an abandoned-baggage detection system that the baggage was left in public places for security reasons, i.e., subway stations. The proposed system applied the YOLO deep learning model for object detection, and presented a GUI for supporting a parameter setting. With this GUI, the detection system will be invariant to lighting and camera position.