In the field of state-of-the-art object detection, the task of object localization is typically accomplished through a dedicated subnet that emphasizes bounding box regression. This subnet traditionally predicts the object's position by regressing the box's center position and scaling factors. Despite the widespread adoption of this approach, we have observed that the localization results often suffer from defects, leading to unsatisfactory detector performance. In this paper, we address the shortcomings of previous methods through theoretical analysis and experimental verification and present an innovative solution for precise object detection. Instead of solely focusing on the object's center and size, our approach enhances the accuracy of bounding box localization by refining the box edges based on the estimated distribution at the object's boundary. Experimental results demonstrate the potential and generalizability of our proposed method.

The Modbus protocol serves as a fundamental element in modern computer network systems, with Modbus Transmission Control Protocol (TCP) being particularly vital in the realms of edge computing and industrial computing. Although User Datagram Protocol (UDP) is frequently acknowledged for its superior transmission speed relative to TCP, it is deficient in the reliability that TCP offers. Modbus utilizes the attributes of TCP to ensure accurate data transmission; however, it exhibits inherent limitations when managing large data volumes, which negatively impacts the performance of the communication link. To address this challenge, we propose an innovative approach referred to as Modbus UDP over Time-Sensitive Networking (TSN). This method not only significantly improves transmission performance, but also leverages the benefits of TSN to rectify the reliability shortcomings associated with UDP. Experimental results obtained from the testing platform indicate that this approach can markedly enhance the capacity for lossless data transmission.

Compared with traditional environments, the cloud environment exposes online services to additional vulnerabilities and threats of cyber attacks, and the cyber security of cloud platforms is becoming increasingly prominent. A piece of code, known as a Webshell, is usually uploaded to the target servers to achieve multiple attacks. Preventing Webshell attacks has become a hot spot in current research. Moreover, the traditional Webshell detectors are not built for the cloud, making it highly difficult to play a defensive role in the cloud environment. SmartEagleEye, a Webshell detection system based on deep learning that is successfully applied in various scenarios, is proposed in this paper. This system contains two important components: gray-box and neural network analyzers. The gray-box analyzer defines a series of rules and algorithms for extracting static and dynamic behaviors from the code to make the decision jointly. The neural network analyzer transforms suspicious code into Operation Code (OPCODE) sequences, turning the detection task into a classification problem. Comprehensive experiment results show that SmartEagleEye achieves an encouraging high detection rate and an acceptable false-positive rate, which indicate its capability to provide good protection for the cloud environment.