Currently, deep learning methods in the analysis and processing of Magnetic Resonance Imaging (MRI) images have found widespread use in medical research. Due to the highly uniform features of human organs or tissues in medical images, pathological conditions are often distinguished only by subtle differences. The manifestations of knee-joint edema are complex and varied, possibly caused by various factors including injury, inflammation, and degenerative diseases, among others. The diversity in edema characteristics in images increases the difficulty of classification. Therefore, the task of pathological identification of knee joint edema poses significant challenges. This paper constructs a neural network model for extracting features of the knee joint and establishes a corresponding classification model. We propose a novel Multi-dimensional knee-joint edema classification network based on attention mechanism (MKEA), which includes a one-dimensional feature extraction network, a two-dimensional feature extraction network, and a feature fusion classification network. In the 2D feature extraction network, a channel attention mechanism module is embedded. we integrate these components to develop a network model for classifying knee edema images from the NYU fastMRI Initiative database. By using well-designed attention models, the network can selectively focus on key regions in medical images, eliminating the need for expensive annotations like bounding boxes or partial labeling. Our method was compared with state-of-the-art approaches on the knee-joint edema dataset. Experimental results show that our approach surpasses the current state-of-the-art methods in terms of performance.

DDoS attacks have caused very serious damage to enterprise networks. Recently, a new kind of DDoS attack called link-flooding attack (LFA), has surfaced and is already being used by attackers to flood and congest network critical links. LFA is very difficult to detect since adversaries often utilize large-scale legitimate low-speed flows and rolls target links to isolate target areas for launching attacks. To address such a critical security problem, we design and implement a novel LFA defense system called LFADefender that leverages some key features, such as programmability, network-wide view, and flow traceability, of an emerging network technology, Software-Defined Networking (SDN), to effectively detect and migrate LFA. In LFADefender, we propose a LFA target link selection approach and design a LFA congestion monitoring mechanism to effectively detect LFA. In addition, we present a multiple optional paths rerouting method to temporarily mitigate links congestion caused by LFA. We further propose a malicious traffic blocking approach to radically mitigate LFA. Our evaluation results show that LFADefender can accurately detect and rapidly mitigate LFA, but only imposes minimal overhead in the communication channels between network controllers and data planes.