Threshold-Based Software-Defined Networking(SDN)Solution for Healthcare Systems against Intrusion Attacks

The healthcare sector holds valuable and sensitive data.The amount of this data and the need to handle,exchange,and protect it,has been increasing at a fast pace.Due to their nature,software-defined networks(SDNs)are widely used in healthcare systems,as they ensure effective resource utilization,safety,great network management,and monitoring.In this sector,due to the value of thedata,SDNs faceamajor challengeposed byawide range of attacks,such as distributed denial of service(DDoS)and probe attacks.These attacks reduce network performance,causing the degradation of different key performance indicators(KPIs)or,in the worst cases,a network failure which can threaten human lives.This can be significant,especially with the current expansion of portable healthcare that supports mobile and wireless devices for what is called mobile health,or m-health.In this study,we examine the effectiveness of using SDNs for defense against DDoS,as well as their effects on different network KPIs under various scenarios.We propose a threshold-based DDoS classifier(TBDC)technique to classify DDoS attacks in healthcare SDNs,aiming to block traffic considered a hazard in the form of a DDoS attack.We then evaluate the accuracy and performance of the proposed TBDC approach.Our technique shows outstanding performance,increasing the mean throughput by 190.3%,reducing the mean delay by 95%,and reducing packet loss by 99.7%relative to normal,with DDoS attack traffic.

Comparison of Using an Electronic System and Conventional Monitoring Method for Monitoring the Quality of Drinking Water and Defects Discovery in Rural Area Water Distribution Network of Abarkouh, Iran

The used water for human consumption must be free of microorganisms and chemicals that cause risk in the human health. In this study, water quality of 18 rural area of Abarkouh was determined and compared the conventional monitoring method (According to ISIR (Institute of Standards and Industrial Research of Iran), 1053 and 4208) and use of electronic system method (Patent in industrial property general office of Iran, 77815). Free chlorine monitoring and pH test done by health workers in the conventional method and the results will be sent to the Health Network monthly. Sampling for microbiological testing is done monthly based on population (According to ISIR, 4208). On the electronic system, the procedure is also done by health workers, but the result will be sent to the receiver device by using a cell phone. According to the chlorine test results if the free chlorine residual reported zero, microbiological sampling was done by a health expert. Finally, the number of chlorine test and microbiological sampling and the results of these experiments collected in the both methods and recorded in SPSS 22 then were analyzed by using chi-square test and Fisher exact test. The result of microbiological experiments shows that the sampling rate decreased 29% in using of electronic system method in comparison to the conventional monitoring method while the number of microbial defect detection increased 19% in drinking water networks monitoring by electronic system. Using of electronic system monitoring can reduce the rate and cost of microbiological sampling and its experiments and increase accuracy of these tests, in this way it will increase the quality and safety of drinking water in distribution network in small and dispersed rural communities.

Weighted average consensus problem in networks of agents with diverse time-delays

This paper studies the weighted average consensus problem for networks of agents with fixed directed asymmetric unbalance information exchange topology. We suppose that the classical distributed consensus protocol is destroyed by diverse time-delays which include communication time-delay and self time-delay. Based on the generalized Nyquist stability criterion and the Gerschgorin disk theorem, some sufficient conditions for the consensus of multi-agent systems are obtained. And we give the expression of the weighted average consensus value for our consensus protocol. Finally, numerical examples are presented to illustrate the theoretical results.

Contrastive Analysis of Software Networks Based on Different Coupling Relationships

Several software network models are constructed based on the relationships between classes in the object-oriented software systems.Then,a variety of well-known open source software applications are statistically analyzed by using these models.The results show that: (1) Dependency network does play a key role in software architecture;(2) The exponents of in-degree and total-degree distribution functions of different networks differ slightly,while the exponent of out-degree varies obviously;(3) Weak-coupling relationships have greater impact on software architecture than strong-coupling relationships.Finally,a theoretically analysis on these statistical phenomena is proposed from the perspectives of software develop technology,develop process and developer’s habits,respectively.

The Optimization Study about Fault Self-Healing Restoration of Power Distribution Network Based on Multi-Agent Technology

In order to quickly and accurately locate the fault location of the distribution network and increase the stability of the distribution network,a fault recovery method based on multi-objective optimization algorithm is proposed.The optimization of the power distribution network fault system based on multiagent technology realizes fast recovery of multi-objective fault,solve the problem of network learning and parameter adjustment in the later stage of particle swarm optimization algorithm falling into the local extreme value dilemma,and realize the multi-dimensional nonlinear optimization of the main grid and the auxiliary grid.The system proposed in this study takes power distribution network as the goal,applies fuzzy probability algorithm,simplifies the calculation process,avoids local extreme value,and finally realizes the energy balance between each power grid.Simulation results show that the Multi-Agent Technology enjoys priority in restoring important load,shortening the recovery time of power grid balance,and reducing the overall line loss rate of power grid.Therefore,the power grid fault self-healing system can improve the safety and stability of the important power grid,and reduce the economic loss rate of the whole power grid.

The Distributed Network Monitoring Model with Bounded Delay Constraints

We address the problem of optimizing a distributed monitoring system and the goal of the optimization is to reduce the cost of deployment of the monitoring infrastructure by identifying a minimum aggregating set subject to delay constraint on the aggregating path. We show that this problem is NP-hard and propose approximation algorithm proving the approximation ratio with lnm+1, where is the number of monitoring nodes. At last we extend our modal with more constraint of bounded delay variation. Key words network - distributed monitoring - delay constraint - NP-hard CLC number TP 393 Foundation item: Supported by the National Natural Science Foundation of China (60373023)Biography: LIU Xiang-hui(1973-), male, Ph. D. candidate, research direction: algorithm complexity analysis, QoS in Internet.

A Hierarchy Distributed-Agents Model for Network Risk Evaluation Based on Deep Learning

Deep Learning presents a critical capability to be geared into environments being constantly changed and ongoing learning dynamic,which is especially relevant in Network Intrusion Detection.In this paper,as enlightened by the theory of Deep Learning Neural Networks,Hierarchy Distributed-Agents Model for Network Risk Evaluation,a newly developed model,is proposed.The architecture taken on by the distributed-agents model are given,as well as the approach of analyzing network intrusion detection using Deep Learning,the mechanism of sharing hyper-parameters to improve the efficiency of learning is presented,and the hierarchical evaluative framework for Network Risk Evaluation of the proposed model is built.Furthermore,to examine the proposed model,a series of experiments were conducted in terms of NSLKDD datasets.The proposed model was able to differentiate between normal and abnormal network activities with an accuracy of 97.60%on NSL-KDD datasets.As the results acquired from the experiment indicate,the model developed in this paper is characterized by high-speed and high-accuracy processing which shall offer a preferable solution with regard to the Risk Evaluation in Network.

Dynamic Threshold-Based Approach to Detect Low-Rate DDoS Attacks on Software-Defined Networking Controller

The emergence of a new network architecture,known as Software Defined Networking(SDN),in the last two decades has overcome some drawbacks of traditional networks in terms of performance,scalability,reliability,security,and network management.However,the SDN is vulnerable to security threats that target its controller,such as low-rate Distributed Denial of Service(DDoS)attacks,The low-rate DDoS attack is one of the most prevalent attacks that poses a severe threat to SDN network security because the controller is a vital architecture component.Therefore,there is an urgent need to propose a detection approach for this type of attack with a high detection rate and low false-positive rates.Thus,this paper proposes an approach to detect low-rate DDoS attacks on the SDN controller by adapting a dynamic threshold.The proposed approach has been evaluated using four simulation scenarios covering a combination of low-rate DDoS attacks against the SDN controller involving(i)a single host attack targeting a single victim;(ii)a single host attack targeting multiple victims;(iii)multiple hosts attack targeting a single victim;and(iv)multiple hosts attack targeting multiple victims.The proposed approach’s average detection rates are 96.65%,91.83%,96.17%,and 95.33%for the above scenarios,respectively;and its average false-positive rates are 3.33%,8.17%,3.83%,and 4.67%for similar scenarios,respectively.The comparison between the proposed approach and two existing approaches showed that it outperformed them in both categories.

Distributed consensus algorithm for networked Euler-Lagrange systems with self-delays and uncertainties

A distributed coordinated consensus problem for multiple networked Euler-Lagrange systems is studied. The communication between agents is subject to time delays, unknown parameters and nonlinear inputs, but only with their states available for measurement. When the communication topology of the system is connected, an adaptive control algorithm with selfdelays and uncertainties is suggested to guarantee global full-state synchro-nization that the difference between the agent＇s positions and ve-locities asymptotically converges to zero. Moreover, the distributed sliding-mode law is given for chaotic systems with nonlinear inputs to compensate for the effects of nonlinearity. Finally, simulation results show the effectiveness of the proposed control algorithm.

Distributed Adaptive Learning Framework for Wide Area Monitoring of Power Systems Integrated with Distributed Generations

This paper presents a preliminary study of developing a novel distributed adaptive real-time learning framework for wide area monitoring of power systems integrated with distributed generations using synchrophasor technology. The framework comprises distributed agents (synchrophasors) for autonomous local condition monitoring and fault detection, and a central unit for generating global view for situation awareness and decision making. Key technologies that can be integrated into this hierarchical distributed learning scheme are discussed to enable real-time information extraction and knowledge discovery for decision making, without explicitly accumulating and storing all raw data by the central unit. Based on this, the configuration of a wide area monitoring system of power systems using synchrophasor technology, and the functionalities for locally installed open-phasor-measurement-units (OpenPMUs) and a central unit are presented. Initial results on anti-islanding protection using the proposed approach are given to illustrate the effectiveness.

A Multi-agent Architecture in CORBA Environment for Distributed Measurement System

The MACE - a Multi agent based distributed measurement architecture in CORBA environment used to develop intelligent distributed measurement system for remote control and monitoring of instruments over network such as Internet and Ethernet was proposed. The architecture is characterized by interoperability, collaboration and intelligence by means of CORBA and multi agent technologies. The architecture and exemplifies it by a common project was described.

A Mobile-Agent-Based Adaptive Data Fusion Algorithm for Multiple Signal Ensembles in Wireless Sensor Networks

Distributed Compressed Sensing (DCS) is an emerging field that exploits both intra- and inter-signal correlation structures and enables new distributed coding algorithms for multiple signal ensembles in wireless sensor networks. The DCS theory rests on the joint sparsity of a multi-signal ensemble. In this paper we propose a new mobile-agent-based Adaptive Data Fusion (ADF) algorithm to determine the minimum number of measurements each node required for perfectly joint reconstruction of multiple signal ensembles. We theoretically show that ADF provides the optimal strategy with as minimum total number of measurements as possible and hence reduces communication cost and network load. Simulation results indicate that ADF enjoys better performance than DCS and mobile-agent-based full data fusion algorithm including reconstruction performance and network energy efficiency.

Distributed and Redundant Design of Ship Monitoring and Control Network

The world trend in ship automation is to integrate the monitoring, intelligent control and systematic management of the instruments and equipments both on bridge and in engine room. The paper presents a design scheme of the ship integrated monitoring and operating system based on two layers distributed and redundant computer network. The lower layer network is the field bus network connected mainly by CAN bus; the upper one is the PC local network in TCP/IP protocol, which consisted of a database server, monitoring and operating computers, industrial computers and a set of switches. Distributed schemes are fully applied to both software and hardware. This paper specifically describes the composition, software distribution and redundant technology of the upper local network and gives some important sample codes for the implement of the redundant and distributed design. The technologies here have been proved in the many applications and it may be applied to other industrial fields.

Agent-Based Synthesis of Distributed Controllers for Discrete Manufacturing Systems

A method for designing real-time distributed controllers of discrete manufacturing systems is presented. The approach held is agent based;the controller strategy is distributed into several interacting agents that operate each one on a part of the manufacturing process;these agents may be distributed into several interconnected processors. The proposed method consists of a modelling methodology and software development framework that provides a generic agent architecture and communication facilities supporting the interaction among agents.

基于多Agent的含DG智能配电网差动保护自愈方法

含DG智能配电网中的潮流方向随分布式发电的变化而变化,导致配电网故障自愈的效果难以保障,因此,提出基于多Agent的含DG智能配电网差动保护自愈方法。通过构建基于多Agent的MAS体系结构,将其应用于含DG智能配电网差动保护和自愈的控制,在MAS体系结构的数据处理层完成差动保护,并在MAS体系结构的协调控制层实现故障自愈。结果表明,该设计方法不仅可以恢复配电网故障区域的电力,而且在操作次数、线损及功率因数方面可达到较理想的效果。

基于多Agent技术的配电网自适应故障定位方法研究

随着电力行业的快速发展,配电网的规模和复杂性也在不断提升。然而,配电网中的故障定位问题一直是困扰电力系统稳定运行的难题。传统的故障定位方法往往存在定位精度不高、响应速度慢等问题。因此,文章提出一种基于多Agent技术的配电网自适应故障定位方法,旨在提升故障定位的准确性和实时性。实验结果表明,基于多Agent技术的配电网自适应故障定位方法在复杂和动态配电网环境中具有显著优势。

基于概率的震级完备度方法评估云南地震台网的监测能力

为了科学评估云南地震台网的监测能力,分析云南地区的地震监测能力时空分布特征,采用基于概率的震级完备度(PMC)方法,利用2009-2023年云南地震台网地震目录、震相和台站信息等数据,计算了云南地震台网在云南地区的平均地震监测能力和在不同深度的监测能力,得到滇中、滇西局部地区的基于概率的完备性震级M_(P)=1.5,云南全境基本都能监测到2.5级地震。评估了研究时段内云南地震台网台站监测能力的演变,发现部分台站的监测能力持续下降。但自2018年增加20个台站后,云南2.0及2.5级地震的监测范围有一定的扩大,但微震监测能力提升不明显,可能是因为2018年以来云南一直处于小震弱活动状态。最后,对应用PMC方法规划提升台网监测能力进行了展望。

基于移动Agent的网格资源发现与监控技术的研究

网格资源监控对于网格这一分布式环境非常重要 ,通过监控可以获得网格环境中的各种大量异构资源和各个节点的状态 ,分析监控得到的数据可以在具体应用中为系统调整提供可靠的依据 .GridFerret系统是一种基于移动Agent技术的网格监控系统 ,将移动Agent技术与网格技术结合起来 ,充分发挥二者的优势 ,有效地减少网格资源发现和监控过程中的通讯代价 .介绍了网格技术和移动Agent技术 ,详细讨论了GridFerret系统的体系结构的设计和实现 .

基于多Agent联盟算法的含分布式电源配电网重构

提出基于多Agent的含分布式电源(DG)配电网控制新策略。新策略不仅具有良好的灵活性、扩展性和人机交互功能,而且利于Agent形成联盟,便于将多Agent联盟机制引入到含DG的配电网研究中,以配合相关算法解决特定配电网任务。以配电网络损耗为优化目标,利用求解多Agent联盟的蚁群算法求解最优Agent联盟,使形成的Agent联盟网损最小,对配电网进行重构。算例表明,采用的多Agent联盟算法和控制策略对求解配电网相关任务是有效的,具有很强的适应性和可操作性。