A Trust-Based Predictive Model for Mobile Ad Hoc Network in Internet of Things

Alnumay, Waleed S.; Ghosh, Uttam; Chatterjee, Pushpita

doi:10.3390/s19061467

Cited by 69 publications

(42 citation statements)

References 28 publications

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“…Authors in [2] proposed ARAN protocol to prevent a number of attacks such as modification, impersonation and fabrication exploits. In [21], authors proposed a quantitative trust model for Ad-Hoc networks those are integrated with Internet of Things (IoT). The proposed model combines both direct and indirect trust to calculate a node's final trust value.…”

Section: Recent Work In Ad-hoc Network Securitymentioning

confidence: 99%

Detailed Security Evaluation of ARANz, ARAN and AODV Protocols

Qabajeh¹,

Qabajeh²

2020

Adv. sci. technol. eng. syst. j.

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Ad-Hoc networks are self-organized wireless networks. Finding a secure and efficient route leading from a specific source node to an intended destination node is one of the serious concerns in mobile Ad-Hoc networks. ARANz is one of the significant protocols that has been proposed for such networks. ARANz implements the authentication methods used with the original Authenticated Routing for Ad-Hoc Networks (ARAN) and enhance security and attain robustness by dividing the network into zones and introducing several local certificate authority servers. Using restricted directional flooding, ARANz reveals improved scalability and performance. The purpose of this paper is to discuss in details the misbehavior detection system used with ARANz protocol, along with presenting a detailed simulated security and performance evaluation of ARANz and other existing protocols. Through extensive simulation using GloMoSim simulator, a detailed security evaluation has been conducted to evaluate ARANz and compare it with the original ARAN and Ad-Hoc On-demand Distance Vector (AODV). Simulation results confirm the effectiveness of ARANz in discovering secure routes within quite large networks including large number of moving nodes, while retaining the minimum packet routing load. Results also prove that ARANz has superior performance regardless malicious nodes percentage conducting different types of attacks such as modification, black hole, grey hole and fabrication. Hence, ARANz can be a good choice for Ad-Hoc networks established among students on a campus or peers at a conference, where predeployment of some keys and certificates is possible.

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Section: Recent Work In Ad-hoc Network Securitymentioning

confidence: 99%

Detailed Security Evaluation of ARANz, ARAN and AODV Protocols

Qabajeh¹,

Qabajeh²

2020

Adv. sci. technol. eng. syst. j.

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“…Methods for trust evaluation are usually classified into two categories: Direct and indirect. Direct trust evaluation assesses the past behaviors of participants, whereas indirect trust evaluation assesses the past behaviors of peers in the neighborhood [23,24].…”

Section: Intrusion Detection Intrusion Detection Is Used To Detect Amentioning

confidence: 99%

Information-Aware Secure Routing in Wireless Sensor Networks

Shi

Ding

et al. 2019

Sensors

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Secure routing is crucial for wireless sensor networks (WSNs) because they are vulnerable to various attacks. In this paper, we propose a new secure routing protocol for WSNs in the presence of malicious nodes. For each relay node in the route, associated information such as its trust value and status is considered in the protocol. The trust value is defined as the attack probability of the node according to previous packet-forwarding behaviors, and the status is a hybrid metric that combines the residual energy and distance to the sink node. Therefore, the route generated by the protocol is secure against malicious attacks and globally optimal according to the associated information. We used an improved variant of the Dijkstra algorithm to generate the secure route for WSNs in the presence of malicious nodes. Compared with the Reputation-Based Mechanism to Stimulate Cooperation (RBMSC) model in the same simulation environment, the proposed model can maintain a higher delivery ratio, which verifies the effectiveness of the proposed model on the basis of global optimization. Furthermore, compared with the traditional Dijkstra algorithm, the packet loss ratio in the improved Dijkstra algorithm is lower because it can more effectively avoid malicious nodes, thus verifying the effectiveness of the improved algorithm.

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“…The Internet of things (IoT) is a part of the future Internet paradigm, which enables every physical object to be connected to the Internet by means of sensors or chips without human intervention [1]- [3]. It may comprise different types of wireless networks such as wireless sensor networks (WSNs), ZigBee, Wi-Fi, mobile ad hoc networks (MANETs), vehicular ad hoc networks (VANETs), and RFID [4], [5]. As an important part, MANET in the IoT becomes more attractive because MANET is a self-organizing and multi-hop network structure, in which nodes share a common wireless channel and can move freely without the aid of a fixed networking infrastructure [6].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, the interaction between MANET and IoT creates such a new MANET-IoT network [8] that it gives greater mobility to end users and reduces the deployment costs of the network. However, at the same time, due to its lack of centralized management [9], weak connectivity [4], and resource constraints [10], MANET-IoT is more prone to attacks than infrastructure-based IoT networks. Especially, the intentional attacks to critical nodes may significantly degrade the network's function, such as disconnecting the network into multiple separate components, causing cascading failure or even the entire collapse of the network [11], [12], which results in new challenging issues for MANET-IoT networks.…”

Section: Introductionmentioning

confidence: 99%

Identification of Critical Nodes for Enhanced Network Defense in MANET-IoT Networks

Niu

et al. 2020

IEEE Access

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In heterogeneous environments, the Internet of Things (IoT) combined with mobile ad hoc network (MANET), i.e., MANET-IoT network, becomes more attractive to end users and economically successful. However, the introduction of MANET potentially makes the system more prone to attacks due to its lack of centralized management, weak connectivity, and resource constraints. To enhance the network robustness to intentional attacks, the critical nodes of the MANET-IoT network should be firstly identified and then protected. Most of the existing methods for identifying critical nodes usually focus on static networks or a single topology snapshot in dynamic networks without considering the correlation between topology snapshots, which cannot effectively deal with the dynamic changes in the topology of MANET-IoT networks. In this paper, a dynamic critical node identification (DCNI) method is proposed. First, we propose a comprehensive metric to measure the node importance in the topology snapshot. Then, we introduce a sliding time window to filter out the topology snapshots which have a close correlation with the current snapshot, and fuse the importance values of the same node in different topology snapshots. Finally, the critical nodes are selected based on the ranking result of fused importance. Thereafter, the port hopping mechanism could be applied to the critical nodes for enhancing network defense capability. The simulation results show that the proposed method is more effective in identifying critical nodes than existing static methods in MANET-IoT networks, and the port hopping mechanism can improve network defense significantly to denial of service (DoS) attacks.

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A Trust-Based Predictive Model for Mobile Ad Hoc Network in Internet of Things

Cited by 69 publications

References 28 publications

Detailed Security Evaluation of ARANz, ARAN and AODV Protocols

Detailed Security Evaluation of ARANz, ARAN and AODV Protocols

Information-Aware Secure Routing in Wireless Sensor Networks

Identification of Critical Nodes for Enhanced Network Defense in MANET-IoT Networks

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