DDoS attacks have become one of the most severe cybersecurity threats, especially in application-layer attacks. With the emergence of Programmable Data Planes (PDPs), it has become possible to maintain line-rate throughput while achieving high detection rates, making them crucial in addressing DDoS challenges. However, due to the complexity of DDoS attacks, detection remains resource-intensive and overall network defense effectiveness is limited. This limitation becomes particularly pronounced in clustered environments, where coordinated defense is essential. This paper presents SISTAR, an innovative framework for efficient DDoS detection and mitigation using PDP. SISTAR integrates an improved Decision Tree - Constrained Threshold Segmentation (DT-CTS) model to achieve high detection accuracy while minimizing hardware resource usage. Through distributed deployment across multiple switches, SISTAR enhances network resilience by enabling rapid detection and coordinated response to DDoS attacks. We implement a prototype of SISTAR and evaluate its performance in a realistic testbed, the experimental results show that SISTAR surpasses existing models in terms of detection accuracy and resource efficiency. When combined with its alert pushback mechanism, SISTAR can effectively reduce network resource consumption caused by DDoS attacks.

A robotic locker system is needed where automated storage and retrieval of items are required without the need for staff presence. For example, a robot can provide 7/24 available services of medical items pick-up and return, during the COVID-19 pandemic (or under other emergencies). A robotic locker system is usually equipped with a user-friendly intuitive interface (e.g., a touchscreen); meanwhile, the robot desires a multitask agent that can observe, understand, and operate the locker’s interface to complete many tasks of storing/accessing/shipping items. In this article, we study building a multitask agent for interacting with robotic locker systems, called AgentMario. Without human intervention for a specific task, AgentMario decomposes solving a task into learning basic skills (states or user interfaces) and planning over the skills (finding the next state/interface). When the agent is solving a task, our search algorithm walks on the finite state machine graph and generates the proper plans (operation sequence) for the agent. In experiments, our method accomplishes four diverse tasks of picking-up/storing/dropping-off/shipping items. By employing image recognition and mechanical automation technologies, we implement AgentMario with a robot arm to enable contactless operation over the locker’s interface. Experimental results show that our method outperforms baselines in most tasks by a large margin.

The healthcare landscape is increasingly challenged by the need for efficient management of medical resources amidst advancing medical technology and aging populations. Traditional medical resource management systems often suffer from limitations in data collection and system stability, hindering decision-making and impacting service quality. However, the advent of IoT (Internet of Things) and cloud computing technologies has revolutionized medical resource management by enabling seamless data integration and storage across multiple layers. This paper introduces, MedSync, a multi-layered medical resource management system with Cloud-Edge Synergy by simultaneously leveraging IoT and cloud computing technologies. By efficiently coordinating data service and storage across four layers, a.k.a., the smart terminal layer, the hospital center layer, the SPD (Supply Processing and Distribution) edge layer, and the SPD cloud layer, the system provides hospitals with a comprehensive, intelligent solution for resource management. Through collaborative operation, hospitals can better understand patients’ needs, adjust medical plans, and enhance service effectiveness and satisfaction. Moreover, the system enhances management and decision support, thus improving operational efficiency and service quality. Through detailed system design, implementation, and efficacy evaluation, this paper offers novel insights and approaches for medical resource management, aiming to elevate service standards and patient experiences.