An Enhanced End to End Route Discovery in AODV using Multi-Objectives Genetic Algorithm

Balas, Firas Al; Almomani, Omar; Jazoh, Reema M. Abu; Khamayseh, Yaser; Saaidah, Adeeb

doi:10.1109/jeeit.2019.8717489

Cited by 13 publications

(7 citation statements)

References 12 publications

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“…GA [11][12][13] is an evolutionary search method that is employed for addressing the optimization problems based on a natural selection method. GA encodes a set of solutions for addressing the optimization problem.…”

Section: Ga Features Selectionmentioning

confidence: 99%

“…The work aimed at reducing the number of the features for increasing the detection rate, and the performance of the NIDS. Although scholars have proposed several NIDS models, the present study aimed to propose a model that is based on four well-known bio-inspired metaheuristic algorithms: genetic algorithm (GA) [11][12][13], particle swarm optimization (PSO) [14][15][16], grey wolf optimizer (GWO) [17][18][19] and firefly optimization algorithm (FFA) [20,21]. The latter model is tested through using a support vector machine (SVM) [22][23][24], J48 (C4.5) [24][25][26] and ML classifier.…”

Section: Introductionmentioning

confidence: 99%

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A Feature Selection Model for Network Intrusion Detection System Based on PSO, GWO, FFA and GA Algorithms

Almomani

2020

Symmetry

Self Cite

193

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The network intrusion detection system (NIDS) aims to identify virulent action in a network. It aims to do that through investigating the traffic network behavior. The approaches of data mining and machine learning (ML) are extensively used in the NIDS to discover anomalies. Regarding feature selection, it plays a significant role in improving the performance of NIDSs. That is because anomaly detection employs a great number of features that require much time. Therefore, the feature selection approach affects the time needed to investigate the traffic behavior and improve the accuracy level. The researcher of the present study aimed to propose a feature selection model for NIDSs. This model is based on the particle swarm optimization (PSO), grey wolf optimizer (GWO), firefly optimization (FFA) and genetic algorithm (GA). The proposed model aims at improving the performance of NIDSs. The proposed model deploys wrapper-based methods with the GA, PSO, GWO and FFA algorithms for selecting features using Anaconda Python Open Source, and deploys filtering-based methods for the mutual information (MI) of the GA, PSO, GWO and FFA algorithms that produced 13 sets of rules. The features derived from the proposed model are evaluated based on the support vector machine (SVM) and J48 ML classifiers and the UNSW-NB15 dataset. Based on the experiment, Rule 13 (R13) reduces the features into 30 features. Rule 12 (R12) reduces the features into 13 features. Rule 13 and Rule 12 offer the best results in terms of F-measure, accuracy and sensitivity. The genetic algorithm (GA) shows good results in terms of True Positive Rate (TPR) and False Negative Rate (FNR). As for Rules 11, 9 and 8, they show good results in terms of False Positive Rate (FPR), while PSO shows good results in terms of precision and True Negative Rate (TNR). It was found that the intrusion detection system with fewer features will increase accuracy. The proposed feature selection model for NIDS is rule-based pattern recognition to discover computer network attack which is in the scope of Symmetry journal.

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Section: Ga Features Selectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Feature Selection Model for Network Intrusion Detection System Based on PSO, GWO, FFA and GA Algorithms

Almomani

2020

Symmetry

Self Cite

193

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“…In WSN the routing protocol is classified to Reactive and Proactive. The reactive protocols like AODV (Al Balas et al 2019), DSR (Johnson et al 2001) and TORA (Sharma & Kumar 2016), set routes when needed only. Therefore, the node doesn't need to start a route discovery process.…”

Section: Rpl Overviewmentioning

confidence: 99%

A Comprehensive Survey on Node Metrics of RPL Protocol for IoT

Saaidah¹,

Almomani²,

Al-Qaisi³

et al. 2019

MAS

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Internet of Things (IOT) system often consists of thousands of constrained connected devices. Resource-constrained devices one of critical issues in a low- power and lossy network LLNs. RPL is IPv6 routing protocol. It’s designed by IETF to be simple and inter-operable networking protocol to overcome these resource limitations. The RPL carries out Objective Functions (OFs) in the aim of finding the best path. The OFs chooses the best parent nodes aiming to build the route and optimize it. The metrics used to build the OF must be selected in an effective and accurate manner for finding the optimal path and meets all constraints. A survey about node metrics which can be utilized in OFs of RPL is presented, and node metrics calculations are explained then discussed thoroughly. The researcher displays the most relevant research efforts regarding the RPL OFs existing in literature.

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“…This reduces the overall performance of the network and distributed formation may totally collapsed. Al Balas et al [18] proposed a genetic algorithm for multi objective approach based on AODV for WSN. Minimum hop count of AODV resembles two major problems like, unbalanced energy depletion and traffic congestion.…”

Section: Related Workmentioning

confidence: 99%

“…Through these groupings, a better network may avail with improved performance. The major pitfall of this proposed work is that if the nodes are mobility then the distance between the source and destination is a major factor for energy constraints [18,19]. In addition, the network have to consider which route to be follow for each transmission (weak link and short path or high link and long path) drains the energy and without this consideration the network cannot follow the last transmission path because of the mobility feature of the network.…”

Section: Related Workmentioning

confidence: 99%

Improving the lifetime of Wireless Sensor Network through Energy Proficient AODV Protocol

H*,

Gunavathi

2019

IJEAT

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Wireless Sensor Network (WSN) is an emerging technology, which enables new innovative trends and equipments to resolve the issues of current and future world. The WSN is a combination of tiny sensor nodes equipped with a minimal battery power, mini processor and a RAM. The battery is non-rechargeable in nature and thus the need for energy efficiency becomes a vital task for WSN. This paper deals with improving the energy efficiency through Energy Proficient AODV protocol (EP-AODV). The proposed work utilizes minimal energy, power amplification and efficient routing technique to improve lifetime of the network. Comparison of EP-AODV with other existing schemes, EP-AODV achieves better throughput, energy efficient, First-Nod-Dies ratio and so on. Thus, the proposed EP-AODV protocol achieves better energy efficient protocol for WSN.

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An Enhanced End to End Route Discovery in AODV using Multi-Objectives Genetic Algorithm

Cited by 13 publications

References 12 publications

A Feature Selection Model for Network Intrusion Detection System Based on PSO, GWO, FFA and GA Algorithms

A Feature Selection Model for Network Intrusion Detection System Based on PSO, GWO, FFA and GA Algorithms

A Comprehensive Survey on Node Metrics of RPL Protocol for IoT

Improving the lifetime of Wireless Sensor Network through Energy Proficient AODV Protocol

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