A Machine Learning-Based Attack Detection and Prevention System in Vehicular Named Data Networking

Named Data Networking(NDN)is gaining a significant attention in Vehicular Ad-hoc Networks(VANET)due to its in-network content caching,name-based routing,and mobility-supporting characteristics.Nevertheless,existing NDN faces three significant challenges,including security,privacy,and routing.In particular,security attacks,such as Content Poisoning Attacks(CPA),can jeopardize legitimate vehicles with malicious content.For instance,attacker host vehicles can serve consumers with invalid information,which has dire consequences,including road accidents.In such a situation,trust in the content-providing vehicles brings a new challenge.On the other hand,ensuring privacy and preventing unauthorized access in vehicular(VNDN)is another challenge.Moreover,NDN’s pull-based content retrieval mechanism is inefficient for delivering emergency messages in VNDN.In this connection,our contribution is threefold.Unlike existing rule-based reputation evaluation,we propose a Machine Learning(ML)-based reputation evaluation mechanism that identifies CPA attackers and legitimate nodes.Based on ML evaluation results,vehicles accept or discard served content.Secondly,we exploit a decentralized blockchain system to ensure vehicles’privacy by maintaining their information in a secure digital ledger.Finally,we improve the default routing mechanism of VNDN from pull to a push-based content dissemination using Publish-Subscribe(Pub-Sub)approach.We implemented and evaluated our ML-based classification model on a publicly accessible BurST-Asutralian dataset for Misbehavior Detection(BurST-ADMA).We used five(05)hybrid ML classifiers,including Logistic Regression,Decision Tree,K-Nearest Neighbors,Random Forest,and Gaussian Naive Bayes.The qualitative results indicate that Random Forest has achieved the highest average accuracy rate of 100%.Our proposed research offers the most accurate solution to detect CPA in VNDN for safe,secure,and reliable vehicle communication.

Push-Based Content Dissemination and Machine Learning-Oriented Illusion Attack Detection in Vehicular Named Data Networking

Recent advancements in the Vehicular Ad-hoc Network(VANET)have tremendously addressed road-related challenges.Specifically,Named Data Networking(NDN)in VANET has emerged as a vital technology due to its outstanding features.However,the NDN communication framework fails to address two important issues.The current NDN employs a pull-based content retrieval network,which is inefficient in disseminating crucial content in Vehicular Named Data Networking(VNDN).Additionally,VNDN is vulnerable to illusion attackers due to the administrative-less network of autonomous vehicles.Although various solutions have been proposed for detecting vehicles’behavior,they inadequately addressed the challenges specific to VNDN.To deal with these two issues,we propose a novel push-based crucial content dissemination scheme that extends the scope of VNDN from pullbased content retrieval to a push-based content forwarding mechanism.In addition,we exploitMachine Learning(ML)techniques within VNDN to detect the behavior of vehicles and classify them as attackers or legitimate.We trained and tested our system on the publicly accessible dataset Vehicular Reference Misbehavior(VeReMi).We employed fiveML classification algorithms and constructed the bestmodel for illusion attack detection.Our results indicate that RandomForest(RF)achieved excellent accuracy in detecting all illusion attack types in VeReMi,with an accuracy rate of 100%for type 1 and type 2,96%for type 4 and type 16,and 95%for type 8.Thus,RF can effectively evaluate the behavior of vehicles and identify attacker vehicles with high accuracy.The ultimate goal of our research is to improve content exchange and secureVNDNfromattackers.Thus,ourML-based attack detection and preventionmechanismensures trustworthy content dissemination and prevents attacker vehicles from sharing misleading information in VNDN.

Secure vehicular data communication in Named Data Networking

Vehicular data misuse may lead to traffic accidents and even loss of life,so it is crucial to achieve secure vehicular data communications.This paper focuses on secure vehicular data communications in the Named Data Networking(NDN).In NDN,names,provider IDs and data are transmitted in plaintext,which exposes vehicular data to security threats and leads to considerable data communication costs and failure rates.This paper proposes a Secure vehicular Data Communication(SDC)approach in NDN to supress data communication costs and failure rates.SCD constructs a vehicular backbone to reduce the number of authenticated nodes involved in reverse paths.Only the ciphtertext of the name and data is included in the signed Interest and Data and transmitted along the backbone,so the secure data communications are achieved.SCD is evaluated,and the data results demonstrate that SCD achieves the above objectives.

Preventing“Bad”Content Dispersal in Named Data Networking

Named Data Networking(NDN)improves the data delivery efficiency by caching contents in routers. To prevent corrupted and faked contents be spread in the network,NDN routers should verify the digital signature of each published content. Since the verification scheme in NDN applies the asymmetric encryption algorithm to sign contents,the content verification overhead is too high to satisfy wire-speed packet forwarding. In this paper, we propose two schemes to improve the verification performance of NDN routers to prevent content poisoning. The first content verification scheme, called "user-assisted",leads to the best performance, but can be bypassed if the clients and the content producer collude. A second scheme, named ``RouterCooperation ‘', prevents the aforementioned collusion attack by making edge routers verify the contents independently without the assistance of users and the core routers no longer verify the contents. The Router-Cooperation verification scheme reduces the computing complexity of cryptographic operation by replacing the asymmetric encryption algorithm with symmetric encryption algorithm.The simulation results demonstrate that this Router-Cooperation scheme can speed up18.85 times of the original content verification scheme with merely extra 80 Bytes transmission overhead.

Energy-Aware Traffic Routing with Named Data Networking

Greening Internet is an important issue now, which studies the way to reduce the increasing energy expenditure. Our work focuses on the network infrastructure and considers its energy awareness in traffic routing. We formulate the model by traffic engineering to achieve link rate adaption, and also predict traffic matrices to preserve network stability. However, we realize that there is a tradeoff between network performance and energy efficiency, which is an obvious issue as Internet grows larger and larger. An essential cause is the huge traffic, and thus we try to find its solution from a novel architecture called Named Data Networking (NDN) which can flexibly cache content in edge routers and decrease the backbone traffic. We combine our methods with NDN, and finally improve both the network performance and the energy efficiency. Our work shows that it is effective, necessary and feasible to consider greening idea in the design of future Internet.

A Cache Replacement Policy Based on Multi-Factors for Named Data Networking

Named Data Networking(NDN)is one of the most excellent future Internet architectures and every router in NDN has the capacity of caching contents passing by.It greatly reduces network traffic and improves the speed of content distribution and retrieval.In order to make full use of the limited caching space in routers,it is an urgent challenge to make an efficient cache replacement policy.However,the existing cache replacement policies only consider very few factors that affect the cache performance.In this paper,we present a cache replacement policy based on multi-factors for NDN(CRPM),in which the content with the least cache value is evicted from the caching space.CRPM fully analyzes multi-factors that affect the caching performance,puts forward the corresponding calculation methods,and utilize the multi-factors to measure the cache value of contents.Furthermore,a new cache value function is constructed,which makes the content with high value be stored in the router as long as possible,so as to ensure the efficient use of cache resources.The simulation results show that CPRM can effectively improve cache hit ratio,enhance cache resource utilization,reduce energy consumption and decrease hit distance of content acquisition.

SAFT-VNDN: A Socially-Aware Forwarding Technique in Vehicular Named Data Detworking

Vehicular Social Networks(VSNs)is the bridge of social networks and Vehicular Ad-Hoc Networks(VANETs).VSNs are promising as they allow the exchange of various types of contents in large-scale through Vehicle-to-Vehicle(V2V)and Vehicle-to-Infrastructure(V2I)communication protocols.Vehicular Named Data Networking(VNDN)is an auspicious communication paradigm for the challenging VSN environment since it can optimize content dissemination by decoupling contents from their physical locations.However,content dissemination and caching represent crucial challenges in VSNs due to short link lifetime and intermittent connectivity caused by vehicles’high mobility.Our aim with this paper is to improve content delivery and cache hit ratio,as well as decrease the transmission delay between end-users.In this regard,we propose a novel hybrid VNDN-VSN forwarding technique based on social communities,which allows requester vehicles to easily find the most suitable forwarder or producer among the community members in their neighborhood area.Furthermore,we introduce an effective caching mechanism by dividing the content store into two parts,one for community private contents and the second one for public contents.Simulation results show that our proposed forwarding technique can achieve a favorable performance compared with traditional VNDN,in terms of data delivery ratio,average data delivery delay,and cache hit ratio.

Research on the Method of Implementing Named Data Network Interconnection Based on IP Network

In order to extend the application scope of NDN and realize the transmission of different NDNs across IP networks,a method for interconnecting NDN networks distributed in different areas with IP networks is proposed.Firstly,the NDN data resource is located by means of the DNS mechanism,and the gateway IP address of the NDN network where the data resource is located is found.Then,the transmission between different NDNs across the IP network is implemented based on the tunnel technology.In addition,in order to achieve efficient and fast NDN data forwarding,we have added a small number of NDN service nodes in the IP network,and proposed an adaptive probabilistic forwarding strategy and a link cost function-based forwarding strategy to make NDN data obtaining the cache service provided by the NDN service node as much as possible.The results of analysis and simulation experiments show that,the interconnectionmethod of NDN across IP network proposed is generally effective and feasible,and the link cost function forwarding strategy is better than the adaptive probability forwarding strategy.

IS: Interest Set to Enhance Flow Transmission in Named-Data Networking

Named-data Networking(NDN) is a promising future Internet architecture, which introduces some evolutionary elements into layer-3, e.g., consumer-driven communication, soft state on data forwarding plane and hop-byhop traffic control. And those elements ensure data holders to solely return the requested data within the lifetime of the request, instead of pushing data whenever needed and whatever it is. Despite the dispute on the advantages and their prices, this pattern requires data consumers to keep sending requests at the right moments for continuous data transmission, resulting in significant forwarding cost and sophisticated application design. In this paper, we propose Interest Set(IS) mechanism, which compresses a set of similar Interests into one request, and maintains a relative long-term data returning path with soft state and continuous feedback from upstream. In this way, IS relaxes the above requirement, and scales NDN data forwarding by reducing forwarded requests and soft states that are needed to retrieve a given set of data.

A Hierarchal Clustered Based Proactive Caching in NDN-Based Vehicular Network

An Information-Centric Network(ICN)provides a promising paradigm for the upcoming internet architecture,which will struggle with steady growth in data and changes in accessmodels.Various ICN architectures have been designed,including Named Data Networking(NDN),which is designed around content delivery instead of hosts.As data is the central part of the network.Therefore,NDN was developed to get rid of the dependency on IP addresses and provide content effectively.Mobility is one of the major research dimensions for this upcoming internet architecture.Some research has been carried out to solve the mobility issues,but it still has problems like handover delay and packet loss ratio during real-time video streaming in the case of consumer and producer mobility.To solve this issue,an efficient hierarchical Cluster Base Proactive Caching for Device Mobility Management(CB-PC-DMM)in NDN Vehicular Networks(NDN-VN)is proposed,through which the consumer receives the contents proactively after handover during the mobility of the consumer.When a consumer moves to the next destination,a handover interest is sent to the connected router,then the router multicasts the consumer’s desired data packet to the next hop of neighboring routers.Thus,once the handover process is completed,consumers can easily get the content to the newly connected router.A CB-PCDMM in NDN-VN is proposed that improves the packet delivery ratio and reduces the handover delay aswell as cluster overhead.Moreover,the intra and inter-domain handover handling procedures in CB-PC-DMM for NDN-VN have been described.For the validation of our proposed scheme,MATLAB simulations are conducted.The simulation results show that our proposed scheme reduces the handover delay and increases the consumer’s interest satisfaction ratio.The proposed scheme is compared with the existing stateof-the-art schemes,and the total percentage of handover delays is decreased by up to 0.1632%,0.3267%,2.3437%,2.3255%,and 3.7313%at the mobility speeds of 5 m/s,10 m/s,15 m/s,20 m/s,and 25 m/s,and the efficiency of the packet delivery ratio is improved by up to 1.2048%,5.0632%,6.4935%,6.943%,and 8.4507%.Furthermore,the simulation results of our proposed scheme show better efficiency in terms of Packet Delivery Ratio(PDR)from 0.071 to 0.077 and a decrease in the handover delay from 0.1334 to 0.129.

NDN-AdHoc网络中基于多属性决策的可靠转发策略

为了应对传统自组织网络高度动态拓扑、短寿命连接等特性导致的传输失效问题,基于命名数据网络(NDN)架构,设计了一种联合链路稳定性和内容可达性的可靠转发(RF-LSCA)策略。该策略基于多属性决策方法,综合考虑链路持续时间、节点间距离和内容可达性3个关键属性,从而更加精确地选择最优转发节点,提升了内容传输性能;此外,鉴于单一最优路径可能存在的不稳定性,该策略进一步将决策模型与概率转发思想相结合,设计了兼具低开销与高可靠性的冗余转发机制。这一设计充分利用了NDN的多路径特性,以保障移动环境下的内容传输性能。通过仿真实验验证,所提RF-LSCA在内容获取平均时延、平均跳数、内容满足率等多个关键性能指标上优于现有策略,充分证明了其在提高NDN-AdHoc网络传输效率方面的优越性。

一种基于蝙蝠算法的NDN网络路径选择方法

针对命名数据网络(named data networking,NDN)路径选择过程中传输速度慢的问题,采用蝙蝠算法优化NDN网络的路径选择。为了证明蝙蝠算法在路径选择上的优势,将蝙蝠算法、遗传算法和粒子群算法进行对比,得出蝙蝠算法求得最优解的概率更高、收敛速度更快,为此提出了一种基于蝙蝠算法的NDN网络路径选择(bat algorithm-path selection in NDN,BA-PSNDN)方法。对传输过程中的节点进行实时更新并计算,通过迭代选出最优路径进行数据传输。使用ndnSIM2.7软件进行仿真,通过在兴趣包和数据包中加入自定义段,保存数据包传输过程中的信息并进行路径选择,仿真出最优路径后输出时延信息。结果表明,BA-PSNDN方法在减少网络传输时延方面更优。

Proposed Caching Scheme for Optimizing Trade-off between Freshness and Energy Consumption in Name Data Networking Based IoT

Over the last few years, the Internet of Things (IoT) has become an omnipresent term. The IoT expands the existing common concepts, anytime and anyplace to the connectivity for anything. The proliferation in IoT offers opportunities but may also bear risks. A hitherto neglected aspect is the possible increase in power consumption as smart devices in IoT applications are expected to be reachable by other devices at all times. This implies that the device is consuming electrical energy even when it is not in use for its primary function. Many researchers’ communities have started addressing storage ability like cache memory of smart devices using the concept called—Named Data Networking (NDN) to achieve better energy efficient communication model. In NDN, memory or buffer overflow is the common challenge especially when internal memory of node exceeds its limit and data with highest degree of freshness may not be accommodated and entire scenarios behaves like a traditional network. In such case, Data Caching is not performed by intermediate nodes to guarantee highest degree of freshness. On the periodical updates sent from data producers, it is exceedingly demanded that data consumers must get up to date information at cost of lease energy. Consequently, there is challenge in maintaining tradeoff between freshness energy consumption during Publisher-Subscriber interaction. In our work, we proposed the architecture to overcome cache strategy issue by Smart Caching Algorithm for improvement in memory management and data freshness. The smart caching strategy updates the data at precise interval by keeping garbage data into consideration. It is also observed from experiment that data redundancy can be easily obtained by ignoring/dropping data packets for the information which is not of interest by other participating nodes in network, ultimately leading to optimizing tradeoff between freshness and energy required.

一种基于分簇优化的VNDN数据转发方法

车辆命名数据网络(VNDN)是一种架构于NDN上的车辆自组织网络(VANET),是基于信息中心网络体系的未来车联网典型代表之一.在VNDN中,车辆的移动性会导致基于耦合路由的NDN数据交互出现通信链路断裂问题.鉴于此,提出了一种基于分簇的车辆移动预测及数据转发方法.利用卡尔曼滤波预测机制,对具有相似移动特征的车辆进行分簇,同时引入网关节点来合理控制簇的大小并保证相邻簇之间的通信.仿真结果表明:所提方法能够有效降低数据传输时延,提高数据交付成功率.

Enhancing Interest Forwarding for Fast Recovery from Unanticipated Data Access Failure in NDN

We show that an aggregated Interest in Named Data Networking (NDN) may fail to retrieve desired data since the Interest previously sent upstream for the same content is judged as a duplicate one and then dropped by an upstream node due to its multipath forwarding. Furthermore, we propose NDRUDAF, a NACK based mechanism that enhances the Interest forwarding and enables Detection and fast Recovery from such Unanticipated Data Access Failure. In the NDN enhanced with NDRUDAF, the router that aggregates the Interest detects such unanticipated data access failure based on a negative acknowledgement from the upstream node that judges the Interest as a duplicate one. Then the router retransmits the Interest as soon as possible on behalf of the requester whose Interest is aggregated to fast recover from the data access failure. We qualitatively and quantitatively analyze the performance of the NDN enhanced with our proposed NDRUDAF and compare it with that of the present NDN. Our experimental results validate that NDRUDAF improves the system performance in case of such unanticipated data access failure in terms of data access delay and network resource utilization efficiency at routers.

A Fast and Memory-Efficient Approach to NDN Name Lookup

For name-based routing/switching in NDN, the key challenges are to manage large-scale forwarding Tables, to lookup long names of variable lengths, and to deal with frequent updates. Hashing associated with proper length-detecting is a straightforward yet efficient solution. Binary search strategy can reduce the number of required hash detecting in the worst case. However, to assure the searching path correct in such a schema, either backtrack searching or redundantly storing some prefixes is required, leading to performance or memory issues as a result. In this paper, we make a deep study on the binary search, and propose a novel mechanism to ensure correct searching path without neither additional backtrack costs nor redundant memory consumptions. Along any binary search path, a bloom filter is employed at each branching point to verify whether a said prefix is present, instead of storing that prefix here. By this means, we can gain significantly optimization on memory efficiency, at the cost of bloom checking before each detecting. Our evaluation experiments on both real-world and randomly synthesized data sets demonstrate our superiorities

NDN Content Poisoning Mitigation Using Bird Swarm Optimization and Trust Value

Information-Centric Networking(ICN)is considered a viable strategy for regulating Internet consumption using the Internet’s underlying architecture.Although Named Data Networking(NDN)and its reference-based implementa-tion,the NDN Forwarding Daemon(NFD),are the most established ICN solu-tions,their vulnerability to the Content Poisoning Attack(CPA)is regarded as a severe threat that might dramatically impact this architecture.Content Poisoning can significantly minimize the impact of NDN’s universal data caching.Using verification signatures to protect against content poisoning attacks may be imprac-tical due to the associated costs and the volume of messages sent across the net-work,resulting in high computational costs.Therefore,in this research,we designed a method in NDN called Bird Swarm Optimization Algorithm-Based Content Poisoning Mitigation(BSO-Content Poisoning Mitigation Scheme).By aggregating the security information of entire routers along the full path,this sys-tem introduces the BSO to explore the secure transmission path and alter the con-tent retrieval procedure.Meanwhile,based on the determined trustworthy value of each node,the BSO-Content Poisoning Mitigation Scheme can bypass malicious routers,preventing them from disseminating illicit content in the future.Addition-ally,the suggested technique can minimize content poisoning utilizing removing erroneous Data packets from the cache-store during the pathfinding process.The proposed method has been subjected to extensive analysis compared with the ROM scheme and improved performance justified in several metrics.BSO-Con-tent Poisoning Mitigation Scheme is more efficient and faster than the ROM tech-nique in obtaining valid Data packets and resulting in a higher good cache hit ratio in a comparatively less amount of time.

一种基于区域划分和内容流行度的NDN缓存策略

针对命名网络中路由器缓存能力有限的问题,提出了一种基于区域划分和内容流行度的NDN缓存策略(CCNCP).该策略根据拓扑结构划分区域,并综合衡量节点重要性选出局部中心节点和全局中心节点;引入分类的内容流行度,合理地将不同流行度的内容缓存在不同重要度的节点.首先采用经典的GN算法进行社区划分.定义了度中心性、紧密中心性、介数中心性等中心性度量指标.然后建立了系统模型,给出了缓存策略.最后对Zachary空手道俱乐部网络的社团结构进行改编,并引入真实拓扑GARR,使用Icarus模拟器进行仿真试验.结果表明:CCNCP在2个不同网络结构中表现得最好;当参数S等于0.25时,CCNCP和第2好缓存策略ProbCache的缓存命中率、延迟、路径拉伸比分别为30.9%和26.8%、52.9和56.2 ms、0.584和0.617,前者比后者分别提高4.1%、降低5.9%、降低5.3%,可见CCNCP在3个指标上与现有的典型策略相比都具有显著优势.

基于SDN具有缓存感知的NDN节点拥塞控制策略

命名数据网(named data networking,NDN)作为一种新型的互联网架构,旨在应对日益增长的数据流量.然而,随着用户需求进一步增长,拥塞控制对于多路径传输的命名数据网仍然是一个具有挑战性的问题,亟需一种能够快速地检测网络拥塞和有效地管理网络资源的拥塞控制机制.针对这一问题,提出了一种基于软件定义网络技术的、具有缓存感知功能的命名数据网节点拥塞控制策略,称BCMCC.首先,介绍了BCMCC的新型网络架构.利用软件定义网络控制平面与数据平面解耦合技术,该架构将拥塞控制功能集中于SDN控制器中,以实现集中式节点拥塞控制、降低节点运行负荷的目的.其次,基于新型网络架构,研究了BCMCC的缓存感知算法和多路径选择拥塞控制算法.其中,缓存感知算法实现了网络缓存内容的感知以及缓存内容全局流行度的计算,能够利用命名数据网的节点缓存特性,降低缓存内容对拥塞控制的影响;多路径选择拥塞控制算法协同节点更新转发端口信息以实现流量迁移、智能化管理多路径容量,达到提高网络资源利用率、有效避免和缓解网络拥塞的目的.最后,在ndnSIM仿真平台进行BCMCC的性能测试.实验结果表明,BCMCC在丢包量、网络吞吐量以及传输时延等方面优于Best-Route算法、NCC算法和Random算法.在网络流量相同的情况下,BCMCC平均传输时延比Random低14.16%,比NCC低22.68%,比Best-Route低17.24%,同时相比Best-Route具有更稳定的网络吞吐量.BCMCC可以有效解决命名数据网拥塞问题.

基于NDN架构的智能配电网感知数据收集协议

传统无线传感器网络(wireless sensor network,WSN)基于IP网络,难以发挥出智能配电网中多种接入网络的性能;同时,在气象灾害或巡检无人机等动态场景下难以获取节点IP地址,无法及时有效完成数据收集任务。命名数据网络(named data networking,NDN)的出现,为解决上述问题提供了可能。NDN以数据需求为驱动,符合WSN只关注数据收集任务本身的初衷。结合NDN新型网络架构对于底层网络协议透明的优势,提出了一种智能配电网中感知数据收集协议——T-ND-WSN。仿真实验表明,该协议结合网内缓存机制,在满足智能配电网数据通信时延上确界前提下,在数据传输服务质量上具有明显优势。