A Black Widow Optimization Algorithm (BWOA) for Node Capture Attack to Enhance the Wireless Sensor Network Protection

Khare, Ankur; Gupta, Rajendra; Shukla, Piyush Kumar; Chowdhury, Rajdeep; Datta, Pushan Kumar

doi:10.1007/978-981-33-4968-1_47

Cited by 6 publications

(6 citation statements)

References 10 publications

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“…Table 1 provides an overview of the simulation parameters, which include the number of sensor nodes (250), the network area dimensions (100 m * 100 m), the number of source nodes (20), the transmission range (tx_rng, 25), the number of destination nodes (7), the number of keys (K, 250), the population size of Grey Wolves (n, 250), and the number of repetitions (iterations, 250) [16].…”

Section: Simulation and Resultsmentioning

confidence: 99%

“…(a) Resource Allocation: GWO efficiently allocates resources within the WSN, optimizing energy consumption and reducing operational costs. (b) Node Placement: It optimizes node placement to ensure maximum coverage and efficient data collection [7]. (c) Data Transmission Efficiency: GWO enhances data transmission efficiency, reducing delays and improving overall system performance.…”

Section: Application In Security Managementmentioning

confidence: 99%

“…For example, in a communication network, nodes may represent communication devices, and edges may represent communication links. The objective function could be minimizing latency or maximizing throughput [8].…”

Section: Network Key Link and Attacker Modelmentioning

confidence: 99%

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Leveraging Gray Wolf Optimization for Enhanced Security Management in Wireless Sensor Network

Surendra Yadav

2023

tjjpt

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In the realm of enhancing security management systems within Wireless Sensor Network (WSNs) for Big Data applications, the role of optimization algorithms is pivotal. This chapter delves into the utilization of Gray Wolf Optimization (GWO) as a fundamental tool to achieve efficient resource allocation, node placement optimization, and the enhancement of data transmission efficiency within WSNs. A detailed exploration of the mathematical foundations of GWO is provided to foster a comprehensive understanding of its application in security enhancement. In this paper Gray Wolf Optimization (GWO) is a nature-inspired metaheuristic algorithm presented that draws inspiration from the social behavior of gray wolves in the wild. In our study, GWO is employed to optimize various parameters that significantly impact the efficiency and effectiveness of WSNs.

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Section: Simulation and Resultsmentioning

confidence: 99%

Section: Application In Security Managementmentioning

confidence: 99%

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Leveraging Gray Wolf Optimization for Enhanced Security Management in Wireless Sensor Network

Surendra Yadav

2023

tjjpt

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“…This node capture attack was identified by using several optimization methodologies [45] and reduced the harmful effects of attacks over WSN. The GWOA (Grey Wolf Optimization Algorithm) [46] was well utilized for determining the malicious nodes optimally with the help of node`s characteristics such as stability and contribution. The outcomes describe the superior efficiency of GWOA based on traffic compromise, power expenses and attacking time against ACO (Ant Colony Optimization) and prior methods.…”

Section: Literature Reviewmentioning

confidence: 99%

HHOA_NCA – a Harris Hawk Optimization Algorithm (HHOA) on best possible Node Capture Attack (NCA) in Future WSN for Improving the Security of Ubiquitous Computing Intelligent System

Khare,

Gupta,

Shukla

2023

Preprint

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Wireless Sensor Network (WSN) is exceedingly predisposed to quite a lot of complex attacks due to confine resource deployment in big network communiqué vicinity. The adversary initiates a node capture attack to future WSN for snatching the private data from ubiquitous computing intelligent system through confiscating vertices (sensor nodes) for cooperating full network. We develop a Harris Hawk Optimization Algorithm for Node Capture Attack (HHOA_NCA) to identify the vertices in ubiquitous computing having higher seizing chance by attack over future network system. HHOA_NCA applies on function sensor node participation with multi objectives such as sensor node key contribution through links, sensor node contribution, sensor node energy cost, distance between sensor nodes and sensor node velocity. Key contribution is the number of keys utilized by vertices. The contribution of sensor nodes is evaluated as the association among all paths and vertices. Sensor node energy cost is obtained on the basis of energy expenditure for packet transmission through vertices over WSN. The distance between two sensor nodes is evaluated using neighbourhood degree. The velocity is measured by the stability time of a sensor node. Simulation is performed in MATLAB 2019a environment and experimental outcomes represent the greater performance of HHOA_NCA based on compromised traffic fraction, power utilization cost, attacking rounds and complexity over future WSN. The results of simulation for ubiquitous computing show that the HHOA_NCA obtains better results than MA, PCA, FGA, MREA, GA, ACO, PSO, FiRAO-PG and FFOA for single and multipath routing based on all performance factors over future network system.

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“…(5) [21]. Following the detection of overload and underload hosts, the next step is to schedule VM hosts using algorithm.…”

Section: Dcp � 1 Pepmentioning

confidence: 99%

Energy-Efficient Resource Allocation and Migration in Private Cloud Data Centre

Dhaya

Ujwal²,

Sharma³

et al. 2022

Wireless Communications and Mobile Computing

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The level of difficulty that can be envisioned in a cloud data center will not grow with convention. As a result, all hosts should have a standard and pervasive collection of memory and communication characteristics in order to lower ownership costs and operate virtual machine instances. This solution includes fundamental foundations and integrated component basics that will allow an IT or federal agency to embrace cloud computing domestically via private virtual cloud data centers. These private cloud data centers would later be developed to purchase and develop IT services on the outside. They are well aware of the obstacles to cloud computing’s acceptance, including concerns about credibility, privacy, interoperability, and marketplaces. In addition, this procedure describes critical standards and collaborations to address these issues. Ultimately, it offers a coherent response to deploying safe data centers using cloud computing services from both a technological and an IT strategic standpoint. To foster creativity, invention, learning, and enterprise, a private data center and cloud computing must be established to combine the activities of different research teams. In the framework of energy-efficient distribution of resources in private cloud data center architecture, we focus on system structure investigations. On the other hand, we want to equip private cloud providers with the current design and performance analysis for energy-efficient resource allocation. The methodology should be adaptable enough to support a wide range of computing systems, as well as on-demand and extensive resource providing approaches, cloud environment scheduling, and bridging the gap between private cloud users and a complete image of offers.

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A Black Widow Optimization Algorithm (BWOA) for Node Capture Attack to Enhance the Wireless Sensor Network Protection

Cited by 6 publications

References 10 publications

Leveraging Gray Wolf Optimization for Enhanced Security Management in Wireless Sensor Network

Leveraging Gray Wolf Optimization for Enhanced Security Management in Wireless Sensor Network

HHOA_NCA – a Harris Hawk Optimization Algorithm (HHOA) on best possible Node Capture Attack (NCA) in Future WSN for Improving the Security of Ubiquitous Computing Intelligent System

Energy-Efficient Resource Allocation and Migration in Private Cloud Data Centre

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