An Efficient and Reliable Routing Method for Hybrid Mobile Ad Hoc Networks Using Deep Reinforcement Learning

Alkadhmi, Murtadha M. A.; Uçan, Osman N.; Ilyas, Muhammad

doi:10.1155/2020/8888904

Cited by 17 publications

(8 citation statements)

References 28 publications

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“…His work used only the random forest algorithm in training of the attack models which produced a best detection rate. A research work done by Yasar Shahid Hussain used the CICDDoS2019 dataset that contained 80 features part of which are legitimate traffic [14] whole the rest are DDoS attack. His work was titled network intrusion detection of DDoS attack using machine learning classification techniques.…”

Section: Literature Reviewmentioning

confidence: 99%

A Machine Learning-Based Intrusion Detection of DDoS Attack on IoT Devices

2021

IJATCSE

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DDoS attack aims to prevent legitimate users from getting access to a targeted system service by exhausting the resources, bandwidth and so on. Though, there are different intrusion mechanisms for detection DDoS attack, having an automated system that can learn the nature of the attack and instantly detect it is the reason why machine learning is used in this work. Decision tree, KNN and Naïve Bayes are the algorithms used classify a benign traffic from a DDoS attack. About nineteen different feature was carefully selected from CIC2019DDoS dataset. The DDoS attack types used for the experiment are UDP, DNS, SYN and NetBIOS. The results of the experiment indicate that Decision tree and KNN proved to be the most effective with an accuracy of 100% and 98% respectively. Naïve Bayes gave a very poor result with an accuracy of 29%.

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Section: Literature Reviewmentioning

confidence: 99%

A Machine Learning-Based Intrusion Detection of DDoS Attack on IoT Devices

2021

IJATCSE

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“…MANET systems have an infrastructure with a lesser configuration of corresponding nodes that are connected by wireless connections. The nodes are moving spontaneously or rather in a random section 5 . From node to network level, security in MANETs is important 6 .…”

Section: Introductionmentioning

confidence: 99%

“…The nodes are moving spontaneously or rather in a random section. 5 From node to network level, security in MANETs is important. 6 Owing to the dynamic nature of MANETs, there needs to be an acceptable level of trust.…”

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confidence: 99%

Proficient link state routing in mobile ad hoc network‐based deep Q‐learning network optimized with chaotic bat swarm optimization algorithm

Rahul¹,

Kaarthick²

2022

Int J Communication

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Mobile ad-hoc network (MANET) is a category of ad-hoc network that can be reconfigurable its network. MANETS are self-organized networks, that can use the wireless links to connect various networks via mobile nodes: but it consumes more energy and it also has routing problems. This is the major drawback of being connected with the MANET technology. Therefore, this study proposes a new protocol as deep Q-learning network optimized with chaotic bat swarm optimization algorithm (CBS)-based optimized link state routing (OLSR) (CBS-OLSR) for MANET. This protocol reduces MANET energy usage and adopts OLSR multi-point relay (MPR) technology. MANET's OLSR and the CBS algorithm utilize a similar method to locate the best optimum path from source to destination node. By embedding the new improved deep Qlearning and OLSR algorithms, both are used for optimizing the MPR sets selection, it can efficiently diminish the energy consumption in the network topology, but automatically increase the lifespan of the network. It also enhances the package delivery ratio and decreases end-to-end delay. The experimental outcomes prove that the proposed protocol is reliable and proficient that is appropriate for numerous MANET applications. K E Y W O R D Schaotic bat swarm optimization, deep Q-learning algorithm, end-to-end delay, mobile adhoc network, MPR (multi-point relay), optimized link state routing, packet delivery rate | INTRODUCTIONMANET is an autonomous system that consists of individually connected set of self-configuring nodes that is implemented using a number of techniques, viz Bluetooth or WLAN. 1 The feature of MANET is autonomous, due to every node treating its host and router simultaneously. 2 To communicate among the distant nodes, they depend on direct communication with multi-hops. These advantages allow them to accommodate multiple nodes and decentralize management with its structure at any time. 3 Unlike other wireless systems, MANET has lack of central authority to monitor traffic transmission. 4 MANET systems have an infrastructure with a lesser configuration of corresponding nodes that are connected by wireless connections. The nodes are moving spontaneously or rather in a random section. 5 From node to network level, security in MANETs is important. 6 Owing to the dynamic nature of MANETs, there needs to be an acceptable level of trust. 7,8 Due to the dynamic nature and characteristics of MANETs, security on the MANET is more challenge

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“…15.4,ultrawideband,IEEE 802.15.3,IEEE 802.11 and Bluetooth [3]. Due the features MANETs have, they are finding popular applications in coal mining networks [4], military, emergency communications [5], as well as, 5G wide area coverage and ultra-dense network scenes [6]. Routing protocols are the key aspect of enhancing network performance and ensuring that communication is taking place [7], at the same utilizing network resources as much as possible.…”

Section: Introductionmentioning

confidence: 99%

Mutation Based Hybrid Routing Algorithm for Mobile Ad-hoc Networks

Musyoka

Omala

Katila

2022

IJCIT

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Mobile Adhoc NETworks (MANETs) usually present challenges such as a highly dynamic topology due to node mobility, route rediscovery process, and packet loss. This leads to low throughput, a lot of energy consumption, delay and low packet delivery ratio. In order to ensure that the route is not rediscovered over and over, multipath routing protocols such as Adhoc Multipath Distance Vector (AOMDV) is used in order to utilize the alternate routes. However, nodes that have low residual energy can die and add to the problem of disconnection of network and route rediscovery. This paper proposes a multipath routing algorithm based on AOMDV and genetic mutation. It takes into account residual energy, hop count, congestion and received signal strength for primary route selection. For secondary path selection it uses residual energy, hop count, congestion and received signal strength together with mutation. The simulation results show that the proposed algorithm gives better performance results compared to AOMDV by 11% for residual energy, 45% throughput, 3% packet delivery ratio, and 63% less delay.

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An Efficient and Reliable Routing Method for Hybrid Mobile Ad Hoc Networks Using Deep Reinforcement Learning

Cited by 17 publications

References 28 publications

A Machine Learning-Based Intrusion Detection of DDoS Attack on IoT Devices

A Machine Learning-Based Intrusion Detection of DDoS Attack on IoT Devices

Proficient link state routing in mobile ad hoc network‐based deep Q‐learning network optimized with chaotic bat swarm optimization algorithm

Mutation Based Hybrid Routing Algorithm for Mobile Ad-hoc Networks

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