Denial of service attack solution in OLSR based manet by varying number of fictitious nodes

Ramachandran, B.; Ramachandran, Ramkumar Ketti

doi:10.1007/s10586-018-1723-0

Cited by 28 publications

(16 citation statements)

References 12 publications

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“…The introduced ABMZP mechanism compute the parameters like throughput, overhead, packet delivery ratio (PDR), packet delay, and end_to_end delay. This manner is compared with other strategies such as MBDP-AODV [19], OLSR [29], and CLPDM-SI [30]. The proposed method achieved better results in overhead, packet delay, PDR, and throughput.…”

Section: Performance Metricsmentioning

confidence: 99%

Prevention of Attacks in Mobile Ad Hoc Network using African Buffalo Monitoring Zone Protocol

Srilakshmi¹,

Bhaskar²

2020

IJACSA

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Section: Performance Metricsmentioning

confidence: 99%

Prevention of Attacks in Mobile Ad Hoc Network using African Buffalo Monitoring Zone Protocol

Srilakshmi¹,

Bhaskar²

2020

IJACSA

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“…This remote channel is entirely untrustworthy and furthermore unprotected from the obstructions from outside. Thinking about this as a top priority, R. Bhuvaneswari and R. Ramachandran 30 proposed a fabricated node system; utilizing this device, they can counteract the malicious attack and limit bundle drop.…”

Section: Related Workmentioning

confidence: 99%

Identifying and detecting black hole and gray hole attack in MANET using gray wolf optimization

Vatambeti

Supriya²,

Sanshi

2020

Int J Communication

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SummaryMobile ad hoc networks (MANETs) become most vibrant and are widely used in various applications, including military, commercial sectors, and personal area networks. However, security is a major issue in routing; because of moving nodes, mainly it suffers from a black hole and gray hole attack. This kind of attack is difficult to predict and is harmful to the network. To address this problem, here, we introduce the gray wolf optimization (GWO) with trust setup data aggregation called gray wolf trust accumulation in wireless ad hoc network architecture. To maintain and enhance the security in routing, here, we update the trust setup data aggregation in GWO mechanism. After the detection of black hole and gray hole attack, we need to maintain the path stability for data transmission, trust schematic process monitor all the nodes, and maintain path stability. Using the behavior of GWO, we can detect the black hole and gray hole attack at a rate of 98.5% and also improve the packet delivery ratio by 98.2% with 10 m/s. Also, with the help of the trust setup data aggregation model, we can update our trust model and security maintenance. Thus, our proposed technique enhances the packet transmission in MANET and secures the routing layer.

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“…Fig. 12 defines the comparison graph for throughput values using FFBPNN and analyzed by Bhuvaneswari et al [19] using OLSR as a routing protocol. From the graph, it has been observed that using FFBPNN the throughput has been increased by 45.52%.…”

Section: A Comparison Of Our Proposed Work With Bhuvaneswari Et Al mentioning

confidence: 99%

Ddos Attack Detection and Prevention using Aodv Routing Mechanism and FFBP Neural Network in a Manet

Batra¹,

Krishna²

2019

IJRTE

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Security is considered as the most important feature in a Mobile Ad-hoc Network (MANET). There are different types of attacks which may affect the data transmission in MANET but Distributed Denial of Service (DDoS) attack is one of the complex and harsh worthy in a MANET. In the existing work, it has been found that the researchers have utilized Support Vector Machine (SVM) and fuzzy logic as a classification algorithm to identify the DoS attack in MANET. The problem with SVM and Fuzzy logic is that they are more complex and more time consuming mechanism to detect attackers. Also, in the existing work, Optimized Link State Routing (OLSR) routing protocol is used to find route and it is a searching mechanism which does not include the concept of trust routing table and hence the searching mechanism consumes more energy. To solve the mentioned problems, we are presenting a machine learning approach that is Feed Forward Back Propagation Neural Network (FFBPNN) as a classifier and Ad hoc On-Demand Distance Vector(AODV) routing protocol for route discovery to shield the network from Distributed Denial of Service (DDoS) attack. The MANET is trained using FFBPNN. Therefore, when malicious node appears in the network, the node is identified on the basis of the node properties like energy consumption and delay. The route is changed by discarding the malicious nodes from the route and hence the network is protected. The throughputs, PDR have been increased by 60.71%, 53.57% and delay has been reduced by 42.21%.

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Denial of service attack solution in OLSR based manet by varying number of fictitious nodes

Cited by 28 publications

References 12 publications

Prevention of Attacks in Mobile Ad Hoc Network using African Buffalo Monitoring Zone Protocol

Prevention of Attacks in Mobile Ad Hoc Network using African Buffalo Monitoring Zone Protocol

Identifying and detecting black hole and gray hole attack in MANET using gray wolf optimization

Ddos Attack Detection and Prevention using Aodv Routing Mechanism and FFBP Neural Network in a Manet

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