Security perspective of wireless sensor networks

被引:0
|
作者
Gutierrez-Portela, Fernando [1 ]
Almenarez-Mendoza, Florina [2 ]
Calderon-Benavides, Liliana [3 ,4 ]
Romero-Riano, Efren
机构
[1] Univ Cooperat Colombia, Ingn Civil, Aqua, Medellin, Colombia
[2] Univ Carlos III Madrid, Dept Ingn Telemat, Aplicac & Serv Telemat, Madrid, Spain
[3] Univ Autonoma Bucaramanga, Unidad Acad, Tecnol Informac, Bucaramanga, Santander, Colombia
[4] Univ Autonoma Bucaramanga, Bucaramanga, Santander, Colombia
来源
UIS INGENIERIAS | 2021年 / 20卷 / 03期
关键词
wireless sensor networks; WSN attacks; security mechanisms; artificial intelligence; intrusion detection; computational resources; countermeasures; ZigBee protocol; machine learning; supervised techniques; unsupervised techniques; anomaly detection; clustering algorithms; VOSviewer; security prospective; EFFICIENT; PRIVACY; ATTACKS; SCHEME; AUTHENTICATION; ALGORITHM; PROTOCOL; SYSTEM;
D O I
10.18273/revuin.v20n3-2021014
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
In Wireless Sensor Networks (WSN), nodes are vulnerable to security attacks because they are installed in a harsh environment with limited power and memory, low processing power, and medium broadcast transmission. Therefore, identifying threats, challenges, and solutions of security and privacy is a talking topic today. This article analyzes the research work that has been carried out on the security mechanisms for the protection of WSN against threats and attacks, as well as the trends that emerge in other countries combined with future research lines. From the methodological point of view, this analysis is shown through the visualization and study of works indexed in databases such as IEEE, ACM, Scopus, and Springer, with a range of 7 years as an observation window, from 2013 to 2019. A total of 4,728 publications were obtained, with a high rate of collaboration between China and India. The research raised developments, such as advances in security principles and defense mechanisms, which have led to the design of countermeasures in intrusion detection. Finally, the results show the interest of the scientific and business community in the use of artificial intelligence and machine learning (ML) to optimize performance measurements.
引用
收藏
页码:189 / 201
页数:13
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