scholarly journals Data mining approach to analyzing intrusion detection of wireless sensor network

2021 ◽  
Vol 21 (1) ◽  
pp. 516
Author(s):  
Md Alauddin Rezvi ◽  
Sidratul Moontaha ◽  
Khadija Akter Trisha ◽  
Shamse Tasnim Cynthia ◽  
Shamim Ripon
Keyword(s):  
Data Mining ◽  
Wireless Sensor Network ◽  
Intrusion Detection ◽  
Sensor Network ◽  
Detection System ◽  
Denial Of Service ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Support Vector

<span>Wireless sensor network (WSN) is a collection of wireless sensor nodes which are distributed in nature and a base station where the dispersed nodes are used to monitor and the physical conditions of the environment is recorded and then these data are organized into the base. Its application has been reached out from critical military application such as battlefield surveillance to traffic, health, industrial areas, intruder detection, security and surveillance. Due to various features in WSN it is very prone to various types external attacks. Preventing such attacks, intrusion detection system (IDS) is very important so that attacker cannot steal or manipulate data. Data mining is a technique that can help to discover patterns in large dataset. This paper proposed a data mining technique for different types of classification algorithms to detect denial of service (DoS) attacks which is of four types. They are Grayhole, Blackhole, Flooding and TDMA. A number of data mining techniques, such as KNN, Naïve Bayes, Logistic Regression, support vector machine (SVM) and ANN algorithms are applied on the dataset and analyze their performance in detecting the attacks. The analysis reveals the applicability of these algorithms for detecting and predicting such attacks and can be recommended for network specialist and analysts. </span>

scholarly journals Two Level Intrusion Detection Mechanism For Context and Trust in Wireless Sensor Network

2020 ◽  
Vol 8 (6) ◽  
pp. 65-71
Keyword(s):  
Wireless Sensor Networks ◽  
Wireless Sensor Network ◽  
Sensor Networks ◽  
Intrusion Detection ◽  
Sensor Network ◽  
Cluster Head ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Trust Evaluation

21st century is considered as the era of communication, and Wireless Sensor Networks (WSN) have assumed an extremely essential job in the correspondence period. A wireless sensor network is defined as a homogeneous or heterogeneous system contains a large number of sensors, namely called nodes used to monitor different environments in cooperatives. WSN is composed of sensor nodes (S.N.), base stations (B.S.), and cluster head (C.H.). The popularity of wireless sensor networks has been increased day by day exponentially because of its wide scope of utilizations. The applications of wireless sensor networks are air traffic control, healthcare systems, home services, military services, industrial & building automation, network communications, VAN, etc. Thus the wide range of applications attracts attackers. To secure from different types of attacks, mainly intruder, intrusion detection based on dynamic state context and hierarchical trust in WSNs (IDSHT) is proposed. The trust evaluation is carried out in hierarchical way. The trust of sensor nodes is evaluated by cluster head (C.H.), whereas the trust of the cluster head is evaluated by a neighbor cluster head or base station. Hence the content trust, honest trust, and interactive trust are put forward by combining direct evaluation and feedback based evaluation in the fixed hop range. In this way, the complexity of trust management is carried in a hierarchical manner, and trust evaluation overhead is minimized.

scholarly journals A Review of Support Vector Machine-based Intrusion Detection System for Wireless Sensor Network with Different Kernel Functions

2021 ◽  
Vol 11 (1) ◽  
pp. 59-67
Author(s):  
Muhammad Amir Hamzah ◽  
Siti Hajar Othman
Keyword(s):  
Support Vector Machine ◽  
Wireless Sensor Network ◽  
Intrusion Detection ◽  
Sensor Network ◽  
Kernel Function ◽  
Intrusion Detection System ◽  
Detection System ◽  
Wireless Sensor ◽  
Experimental Result ◽  
Support Vector

Wireless sensor network (WSN) is among the popular communication technology which capable of self-configured and infrastructure-less wireless networks to monitor physical or environmental conditions. WSN also is the most standard services employed in commercial and industrial applications, because of its technical development in a processor, communication, and low-power usage of embedded computing devices. However, WSN is vulnerable due to the dynamic nature of wireless network. One of the best solutions to mitigate the risk is implementing Intrusion Detection System (IDS) to the network. Numerous researches were done to improve the efficiency of WSN-IDS because attacks in networks has been evolved due to the rapid growth of technology. Support Vector Machine (SVM) is one of the best algorithms for the enhancement of WSN-IDS. Nevertheless, the efficiency of classification in SVM is based on the kernel function used. Since dynamic environment of WSN consist of nonlinear data, linear classification of SVM has limitations in maximizing its margin during the classification. It is important to have the best kernel in classifying nonlinear data as the main goal of SVM to maximize the margin in the feature space during classification. In this research, kernel function of SVM such as Linear, RBF, Polynomial and Sigmoid were used separately in data classification. In addition, a modified version of KDD’99, NSL-KDD was used for the experiment of this research. Performance evaluation was made based on the experimental result obtained. Finally, this research found out that RBF kernel provides the best classification result with 91% accuracy.

scholarly journals Wormhole Detection Mechanism in Wireless Sensor Networks

2020 ◽  
Vol 9 (2) ◽  
pp. 1845-1847
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Base Station ◽  
Sensor Nodes ◽  
Base Stations ◽  
Wireless Sensor ◽  
Health Care Environment ◽  
Detection Mechanism ◽  
Primary Base ◽  
Power And Energy

A Wireless Sensor Network (WSN) is a component with sensor nodes that continuously observes environmental circumstances. Sensor nodes accomplish different key operations like sensing temperature and distance. It has been used in many applications like computing, signal processing, and network selfconfiguration to expand network coverage and build up its scalability. The Unit of all these sensors that exhibit sensing and transmitting information will offer more information than those offered by autonomously operating sensors. Usually, the transmitting task is somewhat critical as there is a huge amount of data and sensors devices are restricted. Being the limited number of sensor devices the network is exposed to different types of attacks. The Traditional security mechanisms are not suitable for WSN as they are generally heavy and having limited number of nodes and also these mechanisms will not eliminate the risk of other attacks. WSN are most useful in different crucial domains such as health care, environment, industry, and security, military. For example, in a military operation, a wireless sensor network monitors various activities. If an event is detected, these sensor nodes sense that and report the data to the primary (base) station (called sink) by making communication with other nodes. To collect data from WSN base Stations are commonly used. Base stations have more resources (e.g. computation power and energy) compared to normal sensor nodes which include more or less such limitations. Aggregation points will gather the data from neighboring sensor nodes to combine the data and forward to master (base) stations, where the data will be further forwarded or processed to a processing center. In this manner, the energy can be preserved in WSN and the lifetime of network is expanded.

scholarly journals In-Situ Pipeline Monitoring System Design for Identifying and Locating Damaging Activities Based on Wireless Sensor Network

2020 ◽  
pp. 33-46
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Monitoring System ◽  
Remote Monitoring ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Monitoring Methods ◽  
Pipeline Monitoring

Due to the recent advancements in the fields of Micro Electromechanical Sensors (MEMS), communication, and operating systems, wireless remote monitoring methods became easy to build and low cost option compared to the conventional methods such as wired cameras and vehicle patrols. Pipeline Monitoring Systems (PMS) benefit the most of such wireless remote monitoring since each pipeline would span for long distances up to hundreds of kilometers. However, precise monitoring requires moving large amounts of data between sensor nodes and base station for processing which require high bandwidth communication protocol. To overcome this problem, In-Situ processing can be practiced by processing the collected data locally at each node instead of the base station. This Paper presents the design and implementation of In-situ pipeline monitoring system for locating damaging activities based on wireless sensor network. The system built upon a WSN of several nodes. Each node contains high computational 1.2GHz Quad-Core ARM Cortex-A53 (64Bit) processor for In-Situ data processing and equipped in 3-axis accelerometer. The proposed system was tested on pipelines in Al-Mussaib gas turbine power plant. During test knocking events are applied at several distances relative to the nodes locations. Data collected at each node are filtered and processed locally in real time in each two adjacent nodes. The results of the estimation is then sent to the supervisor at base-station for display. The results show the proposed system ability to estimate the location of knocking event.

scholarly journals An Exploration of Current Data Agglomeration Technique in Wireless Sensor Network

2020 ◽  
Vol 9 (5) ◽  
pp. 732-746
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Base Station ◽  
Current Data ◽  
Energy Utilization ◽  
Sensor Nodes ◽  
Communication Process ◽  
Wireless Sensor ◽  
Intermediate Node ◽  
Process Energy

Wireless sensor network plays prominently in various applications of the emerging advanced wireless technology such as smart homes, Commercial, defence sector and modern agriculture for effective communication. There are many issues and challenges involved during the communication process. Energy conservation is the major challenging matter and fascinates issue among the researchers. The reason for that, Wireless sensor network has ‘n’ number of sensor nodes to identify and recognize the data and send that data to the base station or sink through either directly or intermediate node. These nodes with poor energy create intricacy on the data rate or flow and substantially affect the lifespan of a wireless sensor network. To decrease energy utilization the sensor node has to neglect unnecessary received data from the neighbouring nodes prior to send the optimum data to the sink or another device. When a specific target is held in a particular sector, it can be identified by many sensors. To rectify such process this paper present Data agglomeration technique is one of the persuasive techniques in the neglecting unnecessary data and of improves energy efficiency and also it increases the lifetime of WSNs. The efficacious data aggregation paradigm can also decrease traffic in the network. This paper discussed various data agglomeration technique for efficient energy in WSN.

scholarly journals Desarrollo de un sistema inalámbrico escalable de medición de humedad del suelo en un cultivo de vid

2020 ◽  
pp. 20-30
Author(s):  
Ortega-Corral César ◽  
B. Ricardo Eaton-González ◽  
Florencio López Cruz ◽  
Laura Rocío, Díaz-Santana Rocha
Keyword(s):  
Soil Moisture ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Precision Agriculture ◽  
Drip Irrigation ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Wireless System ◽  
Different Depths

We present a wireless system applied to precision agriculture, made up of sensor nodes that measure soil moisture at different depths, applied to vine crops where drip irrigation is applied. The intention is to prepare a system for scaling, and to create a Wireless Sensor Network (WSN) that communicates by radio frequency with a base station (ET), so that the gathered data is stored locally and can be sent out an Internet gateway.

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