An Adaptive Protection System for Sensor Networks Based on Analysis of Neighboring Nodes

Creation and operation of sensor systems is a complex challenge not only for industrial and military purposes but also for consumer services (“smart city”, “smart home”) and other applications such as agriculture (“smart farm”, “smart greenhouse”). The use of such systems gives a positive economic effect and provides additional benefits from various points of view. At the same time, due to a large number of threats and challenges to cyber security, it is necessary to detect attacks on sensor systems in a timely manner. Here we present an anomaly detection method in which sensor nodes observe their neighbors and detect obvious deviations in their behavior. In this way, the community of neighboring nodes works collectively to protect one another. The nodes record only those parameters and attributes that are inherent in any node. Regardless of the node’s functionality, such parameters include the amount of traffic passing through the node, its Central Processing Unit (CPU) load, as well as the presence and number of packets dropped by the node. Our method’s main goal is to implement protection against the active influence of an internal attacker on the whole sensor network. We present the anomaly detection method, a dataset collection strategy, and experimental results that show how different types of attacks can be distinguished in the data produced by the nodes.

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Anomaly Detection in Medical Wireless Sensor Networks using SVM and Linear Regression Models

International Journal of E-Health and Medical Communications ◽

10.4018/ijehmc.2014010102 ◽

2014 ◽

Vol 5 (1) ◽

pp. 20-45 ◽

Cited By ~ 32

Author(s):

Osman Salem ◽

Alexey Guerassimov ◽

Ahmed Mehaoua ◽

Anthony Marcus ◽

Borko Furht

Keyword(s):

Wireless Sensor Networks ◽

Sensor Networks ◽

Anomaly Detection ◽

Patient Data ◽

Sensor Nodes ◽

Sensor Data ◽

Wireless Sensor ◽

Smart Devices ◽

Support Vector ◽

Processing Unit

This paper details the architecture and describes the preliminary experimentation with the proposed framework for anomaly detection in medical wireless body area networks for ubiquitous patient and healthcare monitoring. The architecture integrates novel data mining and machine learning algorithms with modern sensor fusion techniques. Knowing wireless sensor networks are prone to failures resulting from their limitations (i.e. limited energy resources and computational power), using this framework, the authors can distinguish between irregular variations in the physiological parameters of the monitored patient and faulty sensor data, to ensure reliable operations and real time global monitoring from smart devices. Sensor nodes are used to measure characteristics of the patient and the sensed data is stored on the local processing unit. Authorized users may access this patient data remotely as long as they maintain connectivity with their application enabled smart device. Anomalous or faulty measurement data resulting from damaged sensor nodes or caused by malicious external parties may lead to misdiagnosis or even death for patients. The authors' application uses a Support Vector Machine to classify abnormal instances in the incoming sensor data. If found, the authors apply a periodically rebuilt, regressive prediction model to the abnormal instance and determine if the patient is entering a critical state or if a sensor is reporting faulty readings. Using real patient data in our experiments, the results validate the robustness of our proposed framework. The authors further discuss the experimental analysis with the proposed approach which shows that it is quickly able to identify sensor anomalies and compared with several other algorithms, it maintains a higher true positive and lower false negative rate.

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Fault-Tolerant Anomaly Detection Method in Wireless Sensor Networks

Information ◽

10.3390/info9090236 ◽

2018 ◽

Vol 9 (9) ◽

pp. 236 ◽

Cited By ~ 3

Author(s):

Nengsong Peng ◽

Weiwei Zhang ◽

Hongfei Ling ◽

Yuzhao Zhang ◽

Lixin Zheng

Keyword(s):

Sensor Networks ◽

Anomaly Detection ◽

Sensor Network ◽

Detection Method ◽

Fault Tolerant ◽

False Negative ◽

False Negative Rate ◽

Temporal Correlation ◽

Sensor Nodes ◽

Wireless Sensor

A key issue in wireless sensor network applications is how to accurately detect anomalies in an unstable environment and determine whether an event has occurred. This instability includes the harsh environment, node energy insufficiency, hardware and software breakdown, etc. In this paper, a fault-tolerant anomaly detection method (FTAD) is proposed based on the spatial-temporal correlation of sensor networks. This method divides the sensor network into a fault neighborhood, event and fault mixed neighborhood, event boundary neighborhood and other regions for anomaly detection, respectively, to achieve fault tolerance. The results of experiment show that under the condition that 45% of sensor nodes are failing, the hit rate of event detection remains at about 97% and the false negative rate of events is above 92%.

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Intelligent Surveillance and Image Transmission Based on Wireless Video Sensor Network

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.457-458.690 ◽

2012 ◽

Vol 457-458 ◽

pp. 690-695

Author(s):

Cheng Bo Yu ◽

Yu Xuan Liu ◽

Yi Meng Zhang ◽

Hong Bing Li

Keyword(s):

Assisted Living ◽

Image Transmission ◽

Image Sensor ◽

Wireless Channel ◽

Sensor Nodes ◽

Processing Unit ◽

Wireless Video ◽

Central Processing ◽

Video Sensors ◽

Video Sensor

Design and implement an energy-efficient smart camera mote architecture to be used as surveillance device for assisted living. Add the Passive Infrared Sensor (PIR) to WVSN, PIR detect the human or animal’s moving, then it triggers the camera to wake up. The image captured will be grayscale processing by the central processing unit. Camera sensor nodes transmit a grayscale image over wireless channel to master control station. It offers reduced complexity, response time, and power consumption over conventional solutions. By experimental results from the test illustrate that performance of the designed wireless image sensor is better than the exiting ones in the market in terms of received signal strength intensity (RSSI) and packet rate ratio (PRR) with respect to the distance. This scheme is less complicated than other wireless video sensor surveillance techniques, allowing resource-constrained video sensors to operate more reliably and longer.

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An ARM based Network of Wireless Sensors for Plant Health Monitoring

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.b4669.029320 ◽

2020 ◽

Vol 9 (3) ◽

pp. 433-437

Keyword(s):

Wireless Sensors ◽

Base Station ◽

Sensor Nodes ◽

Processing Unit ◽

Universal Serial Bus ◽

Smart System ◽

Central Processing ◽

Effective Combination ◽

Zigbee Protocol ◽

The Internet Of Things

In the present age, the Internet of Things (IoT) is turning into an essential part of our day by day existence with the new innovative improvements. The objective of this project is to utilize the IoT with a smart system of wireless sensors to observe plant healthiness and watch larvae populace in a remote yield field. A wireless sensor network is proposed in this setting to recognize larvae and calculate certain gadget parameters, namely, the Acoustic Complexity Index (ACI), temperature, humidity and soil moisture. The information of the sensors is gathered through a serial port through the front end sensing node built with a STM32F407VG board. The leading group of STM32F407VG depends on the processor of Advanced RISC Machine (ARM). Utilizing a remote ZigBee protocol, the node information is transmitted to a base station. Information from a gathering of sensor nodes is obtained by the base station. This information is transmitted by means of the Universal Serial Bus (USB) association between the base station and the Central Processing Unit (CPU). On the CPU, this information is examined utilizing the clearly planned application dependent on MATLAB. The discoveries will be shown and put away on the CPU and logged by means of Thingspeak liaison on the cloud too. At any moment, it requires access to this data globally. An auspicious contact and healing of the arranged yield field is accomplished. To accomplish the effective combination and execution of the modules, the unit parameters are changed. An experimental setup is used to test the proposed system operation. The results confirmed the proper functionality of the system.

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A Novel Distributed Online Anomaly Detection Method in Resource-Constrained Wireless Sensor Networks

International Journal of Distributed Sensor Networks ◽

10.1155/2015/146189 ◽

2015 ◽

Vol 2015 ◽

pp. 1-12

Author(s):

Zhiguo Ding ◽

Haikuan Wang ◽

Minrui Fei ◽

Dajun Du

Keyword(s):

Anomaly Detection ◽

Detection Method ◽

Cluster Head ◽

Sensor Nodes ◽

Wireless Sensor ◽

Resource Constrained ◽

State Matrix ◽

Head Node ◽

Global Anomaly ◽

Online Anomaly Detection

In this paper, a novel distributed online anomaly detection method in resource-constrained WSNs was proposed. Firstly, the spatiotemporal correlation existing in the sensed data was exploited and a series of single anomaly detectors were built in each distributed deployment sensor node based on ensemble learning theory. Secondly, these trained detectors were broadcasted to the member sensor nodes in the cluster, combining with its trained detector, and the initial ensemble detector was built. Thirdly, considering resources-constrained WSNs, ensemble pruning based on biogeographical based optimization (BBO) was employed in the cluster head node to obtain an optimized subset of ensemble members. Further, the pruned ensemble detector coded by the state matrix was broadcasted to each member sensor nodes for the distributed online global anomaly detection. Finally, the experiments operated on a real WSN dataset demonstrated the effectiveness of the proposed method.

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DWT-based anomaly detection method for cyber security of wireless sensor networks

Security and Communication Networks ◽

10.1002/sec.1550 ◽

2016 ◽

Vol 9 (15) ◽

pp. 2911-2922 ◽

Cited By ~ 4

Author(s):

Łukasz Saganowski ◽

Tomasz Andrysiak ◽

Rafał Kozik ◽

Michał Choraś

Keyword(s):

Wireless Sensor Networks ◽

Sensor Networks ◽

Anomaly Detection ◽

Cyber Security ◽

Detection Method ◽

Wireless Sensor

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The Simulation Experiment and Research on an Improved Cumulative Sum Anomaly Detection Method

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.743.219 ◽

2015 ◽

Vol 743 ◽

pp. 219-225 ◽

Cited By ~ 1

Author(s):

Hua Zhao

Keyword(s):

Control System ◽

Intrusion Detection ◽

Anomaly Detection ◽

Cyber Security ◽

Detection Method ◽

Metallurgical Industry ◽

Detection Algorithm ◽

Temperature Control System ◽

Measured Signal ◽

Cumulative Sum

In order to solve the rising serious cyber security problem of the industry control system (ICS) and to improve the reliability of process control of industrial control system, this paper presents an anomaly detection algorithm based on statistical methods. Aimed at the dome temperature control system of hot blast stove in the metallurgical industry, we established that system’s mathematical model and calculate the difference between the predicted output of the model and the measured signal at each moment to form the time-based statistical sequence. Applying the improved non-parametric cumulative sum intrusion detection method, we realizes the online intrusion detection and alarm. The simulation detection experiment shows that the method has a good real-time.

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Designing a Virtual Platform for Modeling Nodes in Wireless Sensor Networks at the Central Processing Unit Level: تصميم منصة برمجية افتراضية لنمذجة العقد ضمن شبكات الحساسات اللاسلكية على مستوى وحدة المعالجة المركزية

Journal of engineering sciences and information technology - مجلة العلوم الهندسية و تكنولوجيا المعلومات ◽

10.26389/ajsrp.e120621 ◽

2021 ◽

Vol 5 (5) ◽

pp. 29-1

Author(s):

Ghaidaa Mohammad Esber, Mothanna Alkubeily, Samer Sulaiman Ghaidaa Mohammad Esber, Mothanna Alkubeily, Samer Sulaiman

Keyword(s):

Wireless Sensor Network ◽

Sensor Network ◽

Assembly Language ◽

Sensor Nodes ◽

Wireless Sensor ◽

Processing Unit ◽

Wireless Sensor Nodes ◽

Unit Level ◽

Low Level ◽

Central Processing

Wireless sensor network simulation programs provide representation for an actual system, without needing to deploy real testbed which is highly constrained by the available budget, and the direct operations inside physical layer in most of these programs are hidden and work implicitly. This is what motivated us to build a kernel for a virtual simulation platform to be able to simulate protocol operations and algorithms at the node processing unit level, The proposed system aims to observe the execution of operations at the low level of the wireless sensor physical infrastructure with the ability to modify at this level. That give the improvers of wireless sensor nodes the ability to test their ideas without needing to use physical environment. We have built the functionality operations which are related to the platform kernel at several stages. We defined (as a first step) the essential operations inside a virtual microprocessor that uses a partial set pf MIPS instructions, and built the kernel of minimized virtual WSN simulator depending on the proposed microprocessor, that means we can add any number of nodes inside the GUI of the WSN simulator kernel, and these nodes use the proposed virtual microprocessor . Then we improved this platform by adding the instruction set of a real microprocessor that is used in wireless sensor network nodes. Finally, (and to ease and simplify the interaction operation between program GUI of the platform kernel and the user), we have built simplified compiler that allows user to deal with microprocessor GUI inside each node, and to clarify protocol and algorithm operations by a set of orders and functions without needing to deal with low level language (Assembly language) in a direct way. The simulation results have presented high flexibility and performance to this platform in observing the operation sequence inside wireless sensor nodes at assembly level, in addition to focus on some parameters that are related to microprocessor inside each node.

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