Visible to the public Biblio

Filters: Author is Gao, X.  [Clear All Filters]
2018-04-11
Zeng, H., Wang, B., Deng, W., Gao, X..  2017.  CENTRA: CENtrally Trusted Routing vAlidation for IGP. 2017 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC). :21–24.

Trusted routing is a hot spot in network security. Lots of efforts have been made on trusted routing validation for Interior Gateway Protocols (IGP), e.g., using Public Key Infrastructure (PKI) to enhance the security of protocols, or routing monitoring systems. However, the former is limited by further deployment in the practical Internet, the latter depends on a complete, accurate, and fresh knowledge base-this is still a big challenge (Internet Service Providers (ISPs) are not willing to leak their routing policies). In this paper, inspired by the idea of centrally controlling in Software Defined Network (SDN), we propose a CENtrally Trusted Routing vAlidation framework, named CENTRA, which can automated collect routing information, centrally detect anomaly and deliver secure routing policy. We implement the proposed framework using NETCONF as the communication protocol and YANG as the data model. The experimental results reveal that CENTRA can detect and block anomalous routing in real time. Comparing to existing secure routing mechanism, CENTRA improves the detection efficiency and real-time significantly.

2019-01-16
Bai, X., Niu, W., Liu, J., Gao, X., Xiang, Y., Liu, J..  2018.  Adversarial Examples Construction Towards White-Box Q Table Variation in DQN Pathfinding Training. 2018 IEEE Third International Conference on Data Science in Cyberspace (DSC). :781–787.

As a new research hotspot in the field of artificial intelligence, deep reinforcement learning (DRL) has achieved certain success in various fields such as robot control, computer vision, natural language processing and so on. At the same time, the possibility of its application being attacked and whether it have a strong resistance to strike has also become a hot topic in recent years. Therefore, we select the representative Deep Q Network (DQN) algorithm in deep reinforcement learning, and use the robotic automatic pathfinding application as a countermeasure application scenario for the first time, and attack DQN algorithm against the vulnerability of the adversarial samples. In this paper, we first use DQN to find the optimal path, and analyze the rules of DQN pathfinding. Then, we propose a method that can effectively find vulnerable points towards White-Box Q table variation in DQN pathfinding training. Finally, we build a simulation environment as a basic experimental platform to test our method, through multiple experiments, we can successfully find the adversarial examples and the experimental results show that the supervised method we proposed is effective.

2020-11-23
Wu, K., Gao, X., Liu, Y..  2018.  Web server security evaluation method based on multi-source data. 2018 International Conference on Cloud Computing, Big Data and Blockchain (ICCBB). :1–6.
Traditional web security assessments are evaluated using a single data source, and the results of the calculations from different data sources are different. Based on multi-source data, this paper uses Analytic Hierarchy Process to construct an evaluation model, calculates the weight of each level of indicators in the web security evaluation model, analyzes and processes the data, calculates the host security threat assessment values at various levels, and visualizes the evaluation results through ECharts+WebGL technology.
2020-12-17
Gao, X., Fu, X..  2020.  Miniature Water Surface Garbage Cleaning Robot. 2020 International Conference on Computer Engineering and Application (ICCEA). :806—810.

In light of the problem for garbage cleaning in small water area, an intelligent miniature water surface garbage cleaning robot with unmanned driving and convenient operation is designed. Based on STC12C5A60S2 as the main controller in the design, power module, transmission module and cleaning module are controlled together to realize the function of cleaning and transporting garbage, intelligent remote control of miniature water surface garbage cleaning robot is realized by the WiFi module. Then the prototype is developed and tested, which will verify the rationality of the design. Compared with the traditional manual driving water surface cleaning devices, the designed robot realizes the intelligent control of unmanned driving, and achieves the purpose of saving human resources and reducing labor intensity, and the system operates security and stability, which has certain practical value.

2021-01-11
Liu, X., Gao, W., Feng, D., Gao, X..  2020.  Abnormal Traffic Congestion Recognition Based on Video Analysis. 2020 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR). :39—42.

The incidence of abnormal road traffic events, especially abnormal traffic congestion, is becoming more and more prominent in daily traffic management in China. It has become the main research work of urban traffic management to detect and identify traffic congestion incidents in time. Efficient and accurate detection of traffic congestion incidents can provide a good strategy for traffic management. At present, the detection and recognition of traffic congestion events mainly rely on the integration of road traffic flow data and the passing data collected by electronic police or devices of checkpoint, and then estimating and forecasting road conditions through the method of big data analysis; Such methods often have some disadvantages such as low time-effect, low precision and small prediction range. Therefore, with the help of the current large and medium cities in the public security, traffic police have built video surveillance equipment, through computer vision technology to analyze the traffic flow from video monitoring, in this paper, the motion state and the changing trend of vehicle flow are obtained by using the technology of vehicle detection from video and multi-target tracking based on deep learning, so as to realize the perception and recognition of traffic congestion. The method achieves the recognition accuracy of less than 60 seconds in real-time, more than 80% in detection rate of congestion event and more than 82.5% in accuracy of detection. At the same time, it breaks through the restriction of traditional big data prediction, such as traffic flow data, truck pass data and GPS floating car data, and enlarges the scene and scope of detection.