A Taxonomy on Misbehaving Nodes in Delay Tolerant Networks

Delay Tolerant Networks (DTNs) are type of Intermittently Connected Networks (ICNs) featured by long delay, intermittent connectivity, asymmetric data rates and high error rates. DTNs have been primarily developed for InterPlanetary Networks (IPNs), however, have shown promising potential in challenged networks i.e. DakNet, ZebraNet, KioskNet and WiderNet. Due to unique nature of intermittent connectivity and long delay, DTNs face challenges in routing, key management, privacy, fragmentation and misbehaving nodes. Here, misbehaving nodes i.e. malicious and selfish nodes launch various attacks including flood, packet drop and fake packets attack, inevitably overuse scarce resources (e.g., buffer and bandwidth) in DTNs. The focus of this survey is on a review of misbehaving node attacks, and detection algorithms. We firstly classify various of attacks depending on the type of misbehaving nodes. Then, detection algorithms for these misbehaving nodes are categorized depending on preventive and detective based features. The panoramic view on misbehaving nodes and detection algorithms are further analyzed, evaluated mathematically through a number of performance metrics. Future directions guiding this topic are also presented

ReFIoV: a novel reputation framework for information-centric vehicular applications

In this article, a novel reputation framework for information-centric vehicular applications leveraging on machine learning and the artificial immune system (AIS), also known as ReFIoV, is proposed. Specifically, Bayesian learning and classification allow each node to learn as newly observed data of the behavior of other nodes become available and hence classify these nodes, meanwhile, the K-Means clustering algorithm allows to integrate recommendations from other nodes even if they behave in an unpredictable manner. AIS is used to enhance misbehavior detection. The proposed ReFIoV can be implemented in a distributed manner as each node decides with whom to interact. It provides incentives for nodes to cache and forward others’ mobile data as well as achieves robustness against false accusations and praise. The performance evaluation shows that ReFIoV outperforms state-of-the-art reputation systems for the metrics considered. That is, it presents a very low number of misbehaving nodes incorrectly classified in comparison to another reputation scheme. The proposed AIS mechanism presents a low overhead. The incorporation of recommendations enabled the framework to reduce even further detection time

Analysis of cyber risk and associated concentration of research (ACR)² in the security of vehicular edge clouds

Intelligent Transportation Systems (ITS) is a rapidly growing research space with many issues and challenges. One of the major concerns is to successfully integrate connected technologies, such as cloud infrastructure and edge cloud, into ITS. Security has been identified as one of the greatest challenges for the ITS, and security measures require consideration from design to implementation. This work focuses on providing an analysis of cyber risk and associated concentration of research (ACR2). The introduction of ACR2 approach can be used to consider research challenges in VEC and open up further investigation into those threats that are important but under-researched. That is, the approach can identify very high or high risk areas that have a low research concentration. In this way, this research can lay the foundations for the development of further work in securing the future of ITS

MARINE: Man-in-the-middle attack resistant trust model IN connEcted vehicles

Vehicular Ad-hoc NETwork (VANET), a novel technology holds a paramount importance within the transportation domain due to its abilities to increase traffic efficiency and safety. Connected vehicles propagate sensitive information which must be shared with the neighbors in a secure environment. However, VANET may also include dishonest nodes such as Man-in-the-Middle (MiTM) attackers aiming to distribute and share malicious content with the vehicles, thus polluting the network with compromised information. In this regard, establishing trust among connected vehicles can increase security as every participating vehicle will generate and propagate authentic, accurate and trusted content within the network. In this paper, we propose a novel trust model, namely, Man-in-the-middle Attack Resistance trust model IN connEcted vehicles (MARINE), which identifies dishonest nodes performing MiTM attacks in an efficient way as well as revokes their credentials. Every node running MARINE system first establishes trust for the sender by performing multi-dimensional plausibility checks. Once the receiver verifies the trustworthiness of the sender, the received data is then evaluated both directly and indirectly. Extensive simulations are carried out to evaluate the performance and accuracy of MARINE rigorously across three MiTM attacker models and the bench-marked trust model. Simulation results show that for a network containing 35% MiTM attackers, MARINE outperforms the state of the art trust model by 15%, 18%, and 17% improvements in precision, recall and F-score, respectively.N/A

Intelligent detection of black hole attacks for secure communication in autonomous and connected vehicles

Detection of Black Hole attacks is one of the most challenging and critical routing security issues in vehicular ad hoc networks (VANETs) and autonomous and connected vehicles (ACVs). Malicious vehicles or nodes may exist in the cyber-physical path on which the data and control packets have to be routed converting a secure and reliable route into a compromised one. However, instead of passing packets to a neighbouring node, malicious nodes bypass them and drop any data packets that could contain emergency alarms. We introduce an intelligent black hole attack detection scheme (IDBA) tailored to ACV. We consider four key parameters in the design of the scheme, namely, Hop Count, Destination Sequence Number, Packet Delivery Ratio (PDR), and End-to-End delay (E2E). We tested the performance of our IDBA against AODV with Black Hole (BAODV), Intrusion Detection System (IdsAODV), and EAODV algorithms. Extensive simulation results show that our IDBA outperforms existing approaches in terms of PDR, E2E, Routing Overhead, Packet Loss Rate, and Throughput

Comparative Analysis of Secure Routing Protocol with Malicious Node Attack in VANET Using CBR/UDP Traffic: A Review

One of biggest challenges to implement adhoc network is its dynamic topology and security issue. There are possibilities of active and passive attack in network to alter the authentic data or to steal the data. There are various types of passive attacks which are very dangerous for effective communication. Black hole attack in Vehicular Ad Hoc Network is major problem related with the field of computer networking. In this paper we present the performance analysis of the black hole attack in Vehicular Ad Hoc Network. We elaborate the different types of attacks and their depth in ad hoc network. The performance metric is taken for the evaluation of attack which depends on a packet end to end delay, network throughput and network load. In reference work black hole attack used in network communication using AODV protocol. There are many problems in VANET and specifically security issues. Besides this need a security algorithm which helps to secure our privacy so that unauthorized person cannot access data

Robust Analysis for AODV Protocol in Vehicular Adhoc Network under Black Hole Attack in NS 2

Security is one of biggest challenges in implementing adhoc network like VANET and MANET and it is due to changing behavior of topology of adhoc network. There are possibilities of various attacks like active and passive attack in network to change the real data or to steal the data. There are diverse types of passive attacks which are very dangerous for communication. Black hole attack in Vehicular Ad Hoc Network is major problem related with the field of computer networking. In this paper we present the performance analysis of the black hole attack in Vehicular Ad Hoc Network. We elaborate the different types of attacks and their depth in ad hoc network. The performance metric is taken for the evaluation of attack which depends on a packet end to end delay, network throughput and network load. In our base work black hole attack used in network communication using AODV protocol. As we know there are many issues in VANET and specially security issues. Therefore in our research work we proposed a new protocol which is known as GPSR which has superior result as compared to base work in term of end to end delay, energy consumption, packet delivery ratio, throughput and overhead. Besides this a security algorithm also implemented so that unauthorized person cannot access the authenti

VANET: Analysis of Black hole Attack using CBR/UDP Traffic Pattern with Hash Function and GPSR Routing Protocol

With momentum of time huge development occurred in the field of MANET and VANET. As we know when new technology emerges it came with many advanages but definitely some limitation must be there. In WSN security is one of biggest challenges which we need to tackle to implement adhoc network. Main reason behind this is dynamic topology of sensing node because nodes are dynamic in nature rather than static. As technology came into existence side by side unethical activity also take place which try to access the data illegally to gain personnel profit. There are so many types of attack possibilities are there in adhoc network. Attack can be classified into active and passive. In our research article oue main concern is on black hole attack. According to this attack a malicious node with high priority number is deployed in between other nodes. Now this malicious node access the data and sends acknowledgement to source that data received. Source node will think that acknowledgement is sent by destination node but actually it is sent by malicious node.  In this research article performance analysis of the black hole attack in Vehicular Ad Hoc Network is executed