An optimized intrusion response system for MANET:

Mechtri, Leila; Tolba, Fatiha Djemili; Ghanemi, Salim

doi:10.1007/s12083-017-0573-5

Cited by 7 publications

(4 citation statements)

References 14 publications

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“…The source node initiates the creation of a route request packet RREQ RREQ contains the following information: the RREQ ID, the destination address, the source address, the destination sequence number, the source sequence number, the NmaxE, and the hop count NmaxE is updated by a node receiving the packet only if its energy level is greater than the current NmaxE; otherwise, NmaxE stays constant Continue checking until RREQ reaches its target The most energy efficient route is chosen from the numerous RREQ collected from several routes If (NmaxE=high &&hopcount=low), choose the communication route A route response RREP is created and returned Data is sent through the newly specified route 3 Wireless Communications and Mobile Computing to create a trust model that decreases the distance to the sink and delays while delivering more reliable packets. This method was established by Mechtri et al [24] as a measure of how well an attack may be evaded. Adversaries in the routing route are notified to source nodes so that they may make better routing decisions.…”

Section: Related Workmentioning

confidence: 99%

Cluster Head Selection and Energy Efficient Multicast Routing Protocol-Based Optimal Route Selection for Mobile Ad Hoc Networks

Manimegalai

Raj

et al. 2022

Wireless Communications and Mobile Computing

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Wireless networks with a large number of peer nodes are known as mobile ad hoc networks (MANETs). In MANETs, the mobility of nodes causes a number of challenges, including path preservation, battery life, safety, dependability, and unexpected connection characteristics. As a result, the network’s quality of service (QoS) would be compromised (QoS). For the discovery and maintenance of pathways in MANETs, the routing protocol is critical. By implementing the multicast routing protocol, the MANET network’s reliability may be improved significantly. Evaluation of multicast routing for quality of service (QoS) is the primary goal of this study. In multicasting, data packets from one node are transmitted to a set of receiver nodes at a time, simultaneously. Multicasting reduces transmission costs. Cluster head selection is one of the challenges in MANET. This proposed research paper optimal route selection (ORS) provides the cluster head selection and alternate cluster head selection for avoiding the failure of the cluster head, generation of the optimal path between the cluster head and member node based on reliability pair factor and node’s energy, and establishment of the path based on maximum energy and number of hops between the nodes (minimum number of hops). In comparison to existing methods, ORS is more effective in the energy-efficient path between base station and cluster head, and member node is provided by an ORS route. Results show that the proposed ORSMAN has higher throughput, minimum latency, minimum jitter, and maximum packet delivery ratio, when compared to the existing methodologies.

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Section: Related Workmentioning

confidence: 99%

Cluster Head Selection and Energy Efficient Multicast Routing Protocol-Based Optimal Route Selection for Mobile Ad Hoc Networks

Manimegalai

Raj

et al. 2022

Wireless Communications and Mobile Computing

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“…These two algorithms are integrated to provide a hybrid trust model that reduces the distance to the sink and delay with better packet delivery. MASID-R-SA is an optimized intrusion response system designed by Mechtri et al [16] as a measure of evading attack-severity. The intensity and nature of the detected adversaries in the routing path are reported effectively to the predecessor and source node to provide better routing choices.…”

Section: Related Workmentioning

confidence: 99%

“…Li Yushuai et al [27] investigated the issue of cooperative distributed energy management of the several multi-bodies along with the optimal energy consideration or generation of entire participant in the single bodies and the optimized distribution of energy at the lines that were interconnected among any pairs of energy bodies.The novelty of the proposed SNS-RR is that it classifies the state of the nodes on the basis of their reward and transition. This state is of the nodes is not estimated on the other existing methods such as [20], [15], & [16]. In another method, such as [13], [18], [21] and [22], optimization is used as an external measure.…”

Section: Related Workmentioning

confidence: 99%

A Recurrent Reward Based Learning Technique for Secure Neighbor Selection in Mobile AD-HOC Networks

et al. 2021

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Mobile ad-hoc network is an assortment of distinct attribute-based mobile devices that are autonomous and are cooperative in establishing communication. These nodes exploit wireless links for communication that causes injection of the adversaries in the network. Therefore, detection and mitigation of adversaries and anomalies in the network are mandatory to retain its performance. To strengthen this concept, in this article, a novel secure neighbor selection technique using recurrent reward-based learning is introduced. This proposed technique inherits the benefits of conventional routing and intelligent machine learning paradigm for classifying the states of the nodes based on their communication behavior. Thorough learning of the behavior of the nodes unanimously at all the hop-levels of communication enables establishing secure and consistent routing and transmission paths to the destination. The performance of the proposed technique is estimated using the metrics throughput, packet delivery ratio, and delay and detection ratio. Experimental analysis proves the consistency of the proposed technique by improving throughput, packet delivery ratio, and detection ratio under less delay.

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“…Identifying the dropped packet is more challenging. It has two phases: In the initial phase, the malicious node exploits the sender to inform that it has the best route to a target or receiver node 22 In the second phase, the malicious node drops the packets with a certain probability or selectively 23 …”

Section: Introductionmentioning

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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An optimized intrusion response system for MANET:

Cited by 7 publications

References 14 publications

Cluster Head Selection and Energy Efficient Multicast Routing Protocol-Based Optimal Route Selection for Mobile Ad Hoc Networks

Cluster Head Selection and Energy Efficient Multicast Routing Protocol-Based Optimal Route Selection for Mobile Ad Hoc Networks

A Recurrent Reward Based Learning Technique for Secure Neighbor Selection in Mobile AD-HOC Networks

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

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