Optimal distributed decision in wireless sensor network using gray wolf optimization

Saleh, Ibrahim A.; Alsaif, Omar Ibrahim; Yahya, Maan A.

doi:10.11591/ijai.v9.i4.pp646-654

Cited by 9 publications

(8 citation statements)

References 8 publications

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“…Adaptive variation of the search vector A ⃗ allows to algorithm transit between exploration and exploitation. Moreover, High exploration ability of WOA is updating mechanism of position of whales using (11). High exploitation and convergence asserted, which originate from ( 9) and ( 5).…”

Section: Whale Optimization Algorithmmentioning

confidence: 97%

“…The algorithm is an established method for detecting abnormal traffic state of the disaster progression; the false alarm rate of attack detection required to improve. Consequently, Hoque et al [11] introduce a genetic algorithm for the intrusion detection system. The authors applied information theory on KDD99 benchmark dataset to volute the filter the traffic data and reduce the complexity.…”

Section: Related Workmentioning

confidence: 99%

“…5) If the standard deviation λ of the w hale is less than threshold λ max, jump to step (6), otherwise uses D as the initial point and replace X(t+1) illustrated in Eqns. (10) and (11) with recalculating its fitness; 6) Determine whether the fitness of humpback whales is better than the fitness of X(t) and if so, let X(t+) be the fitness value.…”

Section: Intrusion Detection With Woamentioning

confidence: 99%

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Intrusion Detection Network Attacks Based on Whale Optimization Algorithm

Mohamed¹,

Alsaif²,

Saleh³

2022

ISI

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Network intrusion detection is a significant issue faced by the Information technology industry. Hacker's attacks set of techniques cause confusion network and computer systems, therefore, the need for a network penetration detection process became urgent. This paper proposed an optimization method to detect the potential of attacks on network packets effectively by defining unfamiliar patterns in a massive volume of the network traffic. Initially, the packet characteristics are defined as the normal dispersal of attributes from the data packet. This algorithm is used a bio-inspired meta-heuristic technique named whale optimization algorithm (WOA) to detect systems against the attackers. The detection method whale optimization (DMWO) is applied to calculate the process of standard deviation distribution to judge the anomaly of the data packet. The simulation algorithm performed by OPNET and Matlab-2015a to main factors of DMGO to classify the input to check whether any attack is present or not. Finally, the performance of this algorithm is a better flexibility result compared with other algorithms DMCM and IHMM.

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Section: Whale Optimization Algorithmmentioning

confidence: 97%

Section: Related Workmentioning

confidence: 99%

Section: Intrusion Detection With Woamentioning

confidence: 99%

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Intrusion Detection Network Attacks Based on Whale Optimization Algorithm

Mohamed¹,

Alsaif²,

Saleh³

2022

ISI

Self Cite

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“…There is highpopulationscalability in thisalgorithm. ConsiderSalp Ad Hoc salp algorithm (see Figure 4) is routing process in a self-organizing network [11]. The first stagen "intelligence" consists of direct and reverse intelligence.…”

Section: Routing In a Self-organizing Network Based On Salp Swarm Opt...mentioning

confidence: 99%

Routing flying Ad Hoc network using salp swarm algorithm

Mahmood

Alsaif

Saleh

2022

IJEECS

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An Ad-hoc network is collection of mobile nodes without the necessity of existing any centralized access points. In this paper introduce novel protocol that used to improveAdHoc protocol with salp swarm algorithm to routed flying Ad Hoc Network (FANET) by method of simulation. FANET predicated unmanned aerial vehicles (UAV), it designed wireless network has nodes withwith high mobility, actively changing topology and movement in 3D space. The main problem for FANET manner of routing packets among managed nodes. The new protocol based on salp swarm algorithm called “SalpAdHoc” protocol to solve routing problem and less the conjunction, thesimulation results of an experimental study confirming the feasibility of using salp swarm algorithms for routing in FANET are presented.

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“…As an optimization algorithm grey wolf optimization (GWO) has been used for different applications domains due to its simplicity to adapt in any optimization algorithm [38]- [43]. It successfully showed promising results such as in wireless sensor [44], resource allocation in cloud environment [45], classification [45], feature selection [46], and other optimization problems [47], [48].…”

Section: Introductionmentioning

confidence: 99%

Grey wolf optimization algorithm for hierarchical document clustering

Jabbar

Ku-Mahamud²

2021

IJEECS

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In data mining, the application of grey wolf optimization (GWO) algorithm has been used in several learning approaches because of its simplicity in adapting to different application domains. Most recent works that concern unsupervised learning have focused on text clustering, where the GWO algorithm shows promising results. Although GWO has great potential in performing text clustering, it has limitations in dealing with outlier documents and noise data. This research introduces medoid GWO (M-GWO) algorithm, which incorporates a medoid recalculation process to share the information of medoids among the three best wolves and the rest of the population. This improvement aims to find the best set of medoids during the algorithm run and increases the exploitation search to find more local regions in the search space. Experimental results obtained from using well-known algorithms, such as genetic, firefly, GWO, and k-means algorithms, in four benchmarks. The results of external evaluation metrics, such as rand, purity, F-measure, and entropy, indicates that the proposed M-GWO algorithm achieves better document clustering than all other algorithms (i.e., 75% better when using Rand metric, 50% better than all algorithm based on purity metric, 75% better than all algorithms using F-measure metric, and 100% based on entropy metric).

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Optimal distributed decision in wireless sensor network using gray wolf optimization

Cited by 9 publications

References 8 publications

Intrusion Detection Network Attacks Based on Whale Optimization Algorithm

Intrusion Detection Network Attacks Based on Whale Optimization Algorithm

Routing flying Ad Hoc network using salp swarm algorithm

Grey wolf optimization algorithm for hierarchical document clustering

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