Securing Sensor Networks Based on Optimization of Weighted Confidence

In this paper, an optimized rmlicious nodes detection algorithm, based on Weighted Confidence Filter （WCF）, is proposed to protect sensor networks from attacks. In this algorithm, each cluster head in a cluster-based hierarchical network figures out an average confidence degree by means of messages from its child nodes. The cluster head only accepts a message from the child node whose confidence degree is higher than the average. Meanwhile, it updates the confidence degrees for each of its child nodes by comparing the aggregation value and the received messages, and regards them as the weight of exactness of messages from nodes. A sensor node is judged to be rmlicious if its weight value is lower than the predefined threshold. Comparative simulation results verify that the proposed WCF algorithm is better than the Weighted Trust Evaluation （WTE） in terms of the detection ratio and the false alarm ratio. More specifically, with the WCF, the detection ratio is significantly improved and the false alarm ratio is observably reduced, especially when the malicious node ratio is 0.25 or greater. When 40% of 100 sensors are malicious, the detection accuracy is above 90% and the false alarm ratio is nearly only 1.8%.

Denial of Service Due to Direct and Indirect ARP Storm Attacks in LAN Environment

ARP-based Distributed Denial of Service (DDoS) attacks due to ARP-storms can happen in local area networks where many computer systems are infected by worms such as Code Red or by DDoS agents. In ARP attack, the DDoS agents constantly send a barrage of ARP requests to the gateway, or to a victim computer within the same sub-network, and tie up the resource of attacked gateway or host. In this paper, we set to measure the impact of ARP-attack on resource exhaustion of computers in a local area network. Based on attack experiments, we measure the exhaustion of processing and memory resources of a victim computer and also other computers, which are located on the same network as the victim computer. Interestingly enough, it is observed that an ARP-attack not only exhausts resource of the victim computer but also significantly exhausts processing resource of other non-victim computers, which happen to be located on the same local area network as the victim computer.

SAC-TA: A Secure Area Based Clustering for Data Aggregation Using Traffic Analysis in WSN

Clustering is the most significant task characterized in Wireless Sensor Networks (WSN) by data aggregation through each Cluster Head (CH). This leads to the reduction in the traffic cost. Due to the deployment of the WSN in the remote and hostile environments for the transmission of the sensitive information, the sensor nodes are more prone to the false data injection attacks. To overcome these existing issues and enhance the network security, this paper proposes a Secure Area based Clustering approach for data aggregation using Traffic Analysis (SAC-TA) in WSN. Here, the sensor network is clustered into small clusters, such that each cluster has a CH to manage and gather the information from the normal sensor nodes. The CH is selected based on the predefined time slot, cluster center, and highest residual energy. The gathered data are validated based on the traffic analysis and One-time Key Generation procedures to identify the malicious nodes on the route. It helps to provide a secure data gathering process with improved energy efficiency. The performance of the proposed approach is compared with the existing Secure Data Aggregation Technique (SDAT). The proposed SAC-TA yields lower average energy consumption rate, lower end-to-end delay, higher average residual energy, higher data aggregation accuracy and false data detection rate than the existing technique.

Adversarial attacks against dynamic graph neural networks via node injection

Dynamic graph neural networks(DGNNs)have demonstrated their extraordinary value in many practical applications.Nevertheless,the vulnerability of DNNs is a serious hidden danger as a small disturbance added to the model can markedly reduce its performance.At the same time,current adversarial attack schemes are implemented on static graphs,and the variability of attack models prevents these schemes from transferring to dynamic graphs.In this paper,we use the diffused attack of node injection to attack the DGNNs,and first propose the node injection attack based on structural fragility against DGNNs,named Structural Fragility-based Dynamic Graph Node Injection Attack(SFIA).SFIA firstly determines the target time based on the period weight.Then,it introduces a structural fragile edge selection strategy to establish the target nodes set and link them with the malicious node using serial inject.Finally,an optimization function is designed to generate adversarial features for malicious nodes.Experiments on datasets from four different fields show that SFIA is significantly superior to many comparative approaches.When the graph is injected with 1%of the original total number of nodes through SFIA,the link prediction Recall and MRR of the target DGNN link decrease by 17.4%and 14.3%respectively,and the accuracy of node classification decreases by 8.7%.

Security Analysis of Application Layer Protocols on Wireless Local Area Networks

This paper aims at analyzing the security issues that lie in the application layer (AL) protocols when users connect to the Internet via a wireless local area network (WLAN) through an access point. When adversaries launch deauthentication flood attacks cutting users' connection, the connection managers will automatically research the last access point's extended service set identifier (ESSID) and then re-establish connection. However, such re-connection can lead the users to a fake access point with the same ESSID set by attackers. As the attackers hide behind users' access points, they can pass AL's authentication and security schemes, e.g. secure socket layer (SSL). We have proved that they can even spy on users' account details, passwords, data and privacy.

SEM-Based Method for Performance Evaluation of Wired LANs

For wired local area networks(LANs),their effectiveness and invulnerability are very critical.It is extraordinarily significant to evaluate the network performance effectively in the design of a reasonable network topology and the performance improvement of the networks.However,there are many factors affecting the performance of the networks,and the relation among them is also complicated.How to evaluate the performance of the wired LANs more accurately has been a heavy challenge in the network research.In order to solve the problem,this paper presents a performance evaluation method that evaluates the effectiveness and invulnerability of the wired LANs.Compared to traditional statistical methods,it has the distinct advantage of being able to handle several dependent variables simultaneously and tolerates the measurement errors among these independent variables and dependent variables.Finally,the rationality and validity of this method are verified by the extensive experimental simulation.

Scheme of Rogue AP detection in managed WLAN based on AP's location

A scheme of rogue access point(Rogue AP)detection based on AP's localization is proposed.Global position system(GPS)information and received signal strength(RSS)information are used to get the location of AP in a smartphone,which is compared with the database located in a remote server.The proposed scheme can detect not only fake access point(Fake AP)but also Evil Twin AP.It can be a user-oriented solution to detecting Rogue AP threats,and users can use it flexibly.

基于组合加权k近邻分类的无线传感网络节点复制攻击检测方法

无线传感网络节点体积小,隐蔽性强,节点复制攻击检测的难度较大,为此提出一种基于组合加权k近邻分类的无线传感网络节点复制攻击检测方法。通过信标节点的空间位置数据与相距跳数得出各节点之间的相似程度,结合高斯径向基核函数求解未知节点的横轴、纵轴的空间坐标,确定各网络节点的空间位置;根据网络节点的属性特征与投票机制建立节点复制攻击模型,凭借组合加权k近邻分类法划分节点类型,并将结果传送至簇头节点,由簇头节点做出最后的仲裁,识别出节点复制攻击行为。仿真结果表明,所提方法的节点复制攻击检测率最大值为99.5%,最小值为97.9%,对节点复制攻击检测的耗时为5.41 s,通信开销数据包数量最大值为209个,最小值为81个。

基于改进FastICA算法的工控网络恶意节点攻击检测系统设计

为了确保工控系统的安全和可靠性,基于改进FastICA算法,设计工控网络恶意节点攻击检测系统。在工控网络服务器端集群中,从网络服务器端、攻击行为排重模块、恶意节点辨识模块和Fabric攻击行为检测组件等4部分设计工控网络恶意节点攻击检测系统的硬件模块。在此基础上计算Fast初始值,统计ICA单位的不确定性,改进FastICA算法。由此,联合相关攻击行为信息,推导恶意节点的攻击分离条件。实验结果表明,按照1∶1的负荷标准分配待测数据,所设计系统可以有效检测恶意节点攻击行为,得到的恶意节点实时检测数量值与额定值的拟合度接近100%,能够较好维护网络体系的运行稳定性。

恶意攻击背景下电力信息网络节点重要度评估

电力信息网络中攻击事件频发,关键节点遭受攻击的概率增大,为迅速确定关键节点,解决其故障问题,设计一种恶意攻击背景下的电力信息网络节点重要度评估方法。抽象处理电力信息网络,通过Prim算法与社团分区方法相结合的分区方法进行网络区域划分,再构建电力信息网络模型,进行电力信息网络的区间连锁故障建模,计算恶意攻击下连锁故障过程中电力信息网络的负荷损失。基于HITs算法设计网络节点重要度评估算法,实现节点重要度的评估。测试结果表明,设计方法对垃圾节点的识别抵御率始终高于94%,证明该方法在实践中表现良好。

传感器网络节点定位系统安全性分析

正确的节点位置信息是传感器网络构建和维护、监测事件定位、目标跟踪等模块实现的前提和基础.节点的定位过程极易受到各种攻击,在资源受限的传感器网络中,如何安全、有效地获取节点位置信息,是一个极具挑战性的安全问题.着重分析了不同类型的传感器网络节点定位系统所面临的安全攻击,讨论了近年来该领域具有代表性的安全措施的原理、特点和局限,并简要介绍了谈领域今后的研究热点.

信标节点漂移情况下的无线传感器网络节点定位机制

针对信标节点漂移情况下的节点定位问题,提出了一种分布式的信标节点漂移检测方法,采用节点自评分和协商机制,自动寻找可能发生了漂移的信标节点,同时针对大量信标节点发生漂移后的定位覆盖率下降问题,构建普通节点的定位可信度模型,并在定位盲区内使用一些较为可靠的普通节点作为临时信标节点进行定位。仿真实验表明,该算法在误检测、定位误差方面性能优于传统算法,具有较低的通信开销、较高的实用性和灵活性。

基于多元分类的无线传感器网络恶意节点检测算法

无线传感器网络是一个暴露在开放环境中的分布式网络,各节点之间相互独立,缺乏中心节点和监控节点,极易受到恶意节点的攻击。为了对无线传感器网络中的大量传感器节点进行恶意节点检测,提出了一种基于多元分类的恶意节点检测方法。提出的检测方法是在已知少量传感器节点类型的前提下,抽取与已知恶意节点类型相关的传感器节点属性,建立关于全部参与网络活动的传感器节点的样本空间,通过多元分类算法对已知类型传感器节点的样本进行学习,生成分类器,然后对未知类型传感器节点进行分类。实验结果表明在传感器节点属性值以及活跃节点个数稳定的情况下,误检率能够稳定在0.5%以下。

泛洪攻击下机会网络典型路由算法健壮性分析

设计了评价方法及指标体系,通过对真实城市场景中带有智能蓝牙设备行人移动行为的仿真,从传输能力、传输效率和节点能耗三个方面定量分析了志愿节点的作用以及在有或没有志愿节点参与情况下泛洪攻击的效果,以此来评价Direct Deliv-ery、Epidemic、Spray and Wait、Prophet和MaxProp共五种机会网络典型路由算法在泛洪攻击下的健壮性。结果表明Direct De-livery算法健壮性最好,能完全抵御泛洪攻击,Spray and Wait算法的健壮性最差,在某些场景下性能会下降80%以上,其他三种算法在泛洪攻击下性能会显著下降。

Ad hoc网络中基于多径路由协议的信誉机制

基于多径路由协议,采用RS编码技术,设计了一种将数据信息编码后分片沿不同路径传输的方法,在目的节点对恶意篡改行为进行检测,确定恶意节点的具体位置或将恶意节点锁定在恶意节点组内。在此基础上提出了一个完整的抵制篡改数据攻击的信誉机制,充分利用了从数据传输和恶意节点检测过程中获得的信息,采用简单贝叶斯模型方法,对节点进行信誉评价,引入了恶意节点组的概念。数据在正常传输过程中无需重复加密和签名。实验表明,该信誉机制能够有机地与多径路由协议相结合,快速准确地孤立篡改数据的恶意节点。

高校数字图书馆网络安全风险分析与策略

从数字图书馆网络内部和使用服务的外部两方面,分析高校图书馆网络面临的硬件安全风险、软件安全风险、技术风险、管理风险以及外部攻击安全风险等主要风险。结合网络安全技术的发展,分别提出各自的应对策略。重点分析黑客攻击和网络病毒的控制方法,包括采用杀毒防护软件、运用防火墙技术、加密认证技术、入侵检测技术、VLAN技术、内容检查与监控技术以及虚拟蜜罐技术等等。图书馆网络系统只有实现了设备的本质安全化、人员网络技术的专业化以及管理的正规化,才能有效地规避各种网络安全风险和确保网络系统的正常运行,研究结果对如何预防与控制高校数字图书馆网络安全风险具有借鉴和指导作用。

基于垂直平分线的无线传感器网络定位改进算法

针对传统基于垂直平分线的区域定位算法定位精度低、迭代次数多等缺点,提出一种改进的垂直平分线算法IMBLA。根据未知节点接收到的两锚节点接收信号强度指示(RSSI)值的比值,移动两锚点的垂直平分线,再确定待定位节点与垂直平分线的位置关系。该算法应用基于参考锚节点的高斯校正模型进行RSSI测距,包括有障碍物时的模型,不但适合各种环境,还能有效防止恶意攻击。仿真结果表明,与MBLA和IPAIT算法相比,IMBLA算法的定位精度和网络覆盖率较高。

LSN DCI EVPN VxLAN组网架构研究及实现

虚拟化技术的兴起,打破了传统数据中心的架构,数据中心之间存在着大量互联场景,如异地双活、数据中心容灾、数据同步备份、数据迁移、集中式存储、共享虚拟化资源池、虚拟机漂移等,由此产生了大量的东西向流量,需要构建一个可靠、灵活、安全、易于部署的二、三层网络,以满足数据中心互联需求。研究了EVPN VxLAN组网架构,在业内尚无异构多厂商规模商用组网成功案例的情况下,结合中国电信股份有限公司上海分公司LSN数据中心现状,提出并实现了适用于现网的DCI组网。

传感器节点定位系统攻防机制研究

正确的节点位置信息是无线传感器网络许多功能模块实现的前提和基础,如网络构建和维护、监测事件定位、目标跟踪。在资源受限的传感器网络中,如何安全和有效地获取节点位置信息,是一个极具挑战性的安全问题。本文着重分析了不同的传感器节点定位系统所面临的各种攻击,分析了近年来该领域具有代表性的安全措施的原理、特点和局限,并简要介绍了该领域今后的研究热点。

无线传感器网络中基于RSSI一致性的安全定位方法

无线传感器网络通常部署在无人值守的敌对环境中,容易受到攻击节点的影响,给其应用带来较大挑战。针对该问题,利用移动信标节点,提出一种基于接收信号强度指示器(RSSI)一致性实现未知节点安全定位的方法。该方法通过计算未知节点接收到的数据包RSSI的方差,将方差较小的一组所对应的节点信息剔除,从而抑制虫洞攻击对定位的影响。仿真结果表明,该方法能有效消除虫洞攻击对节点定位的影响。