Smart Contract Based DDoS Attack Traceability Audit Mechanism in Intelligent IoT

In this paper,we focus on providing data provenance auditing schemes for distributed denial of service(DDoS)defense in intelligent internet of things(IoT).To achieve effective DDoS defense,we introduce a two-layer collaborative blockchain framework to support data auditing.Specifically,using data scattered among intelligent IoT devices,switch gateways self-assemble a layer of blockchain in the local autonomous system(AS),and the main chain with controller participation can be aggregated by its associated layer of blocks once a cycle,to obtain a global security model.To optimize the processing delay of the security model,we propose a process of data pre-validation with the goal of ensuring data consistency while satisfying overhead requirements.Since the flood of identity spoofing packets,it is difficult to solve the identity consistency of data with traditional detection methods,and accountability cannot be pursued afterwards.Thus,we proposed a Packet Traceback Telemetry(PTT)scheme,based on in-band telemetry,to solve the problem.Specifically,the PTT scheme is executed on the distributed switch side,the controller to schedule and select routing policies.Moreover,a tracing probabilistic optimization is embedded into the PTT scheme to accelerate path reconstruction and save device resources.Simulation results show that the PTT scheme can reconstruct address spoofing packet forward path,reduce the resource consumption compared with existing tracing scheme.Data tracing audit method has fine-grained detection and feasible performance.

ReLFA:Resist Link Flooding Attacks via Renyi Entropy and Deep Reinforcement Learning in SDN-IoT

Link flooding attack(LFA)is a fresh distributed denial of service attack(DDoS).Attackers can cut off the critical links,making the services in the target area unavailable.LFA manipulates legal lowspeed flow to flood critical links,so traditional technologies are difficult to resist such attack.Meanwhile,LFA is also one of the most important threats to Internet of things(IoT)devices.The introduction of software defined network(SDN)effectively solves the security problem of the IoT.Aiming at the LFA in the software defined Internet of things(SDN-IoT),this paper proposes a new LFA mitigation scheme ReLFA.Renyi entropy is to locate the congested link in the data plane in our scheme,and determines the target links according to the alarm threshold.When LFA is detected on the target links,the control plane uses the method based on deep reinforcement learning(DRL)to carry out traffic engineering.Simulation results show that ReLFA can effectively alleviate the impact of LFA in SDN IoT.In addition,the rerouting time of ReLFA is superior to other latest schemes.

Application Study of Greenhouse Environment Monitoring System Based on ZigBee Technology

Against the demand of intelligent greenhouse construction of facility agriculture,greenhouse environment monitoring system is developed.The system contains three-layer architecture: sensor network layer at the bottom,data transmission convergence layer in the middle and monitoring application layer on the top,which is different from design idea of the existing system architecture.The bottom layer uses ZigBee wireless communication technology to construct wireless sensor network,and node type contains coordinator,router and acquisition terminal.Acquisition terminal is distributed in each greenhouse to collect data and play the role of wireless transmission,and router plays the function of data forwarding as the bridge of acquisition terminal and coordinator.Middle layer is composed of monitoring software developed by Lab VIEW software of NI Company and coordinator,which is used to gather data from the bottom layer.The top layer is comprehensive monitoring platform developed by Java language,which is used to gather greenhouse data of all plantation bases in one region,thereby providing comprehensive information service for government,enterprise and farmer.Greenhouse environment monitoring system realizes data collection and sharing of greenhouse environment information(air temperature,air humidity,light intensity and carbon dioxide concentration).Via test verification,the system's operation is stable,with certain application value.

利用和频振动光谱探索蛋白质与多肽界面结构的最新研究进展

蛋白质和多肽的界面结构在各种生物过程和技术发展中发挥着重要作用。对这些结构的深入了解对于疾病诊断、药物输送和生物材料的研究至关重要.然而,这些体系的复杂性以及分析方法的局限性限制了其深入研究.尽管X射线晶体学和低温电子显微镜等先进技术在分析蛋白质结构方面做出了重大贡献,但对于实际条件下表面结合蛋白质结构的理解仍然有限,这也为该领域带来了挑战。和频振动光谱技术已经成为分析蛋白质界面分子结构的一种强有力的手段.本文简要介绍和频振动光谱学的基本原理,探讨其在相位测量方面的研究进展,并概述了近三年(2021~2023)的研究成果,展示了和频振动光谱在揭示多肽和蛋白质界面分子结构方面的潜力:本文为进一步利用和频振动光谱探索不同类型的多肽和蛋白质在界面上的结构和功能提供了有利的支持。