A Comparative Evaluation of Mining Techniques to Detect Malicious Node in Wireless Sensor Networks

2017 ◽  
Vol 7 (2) ◽  
pp. 42-53
Author(s):  
Mandeep Singh ◽  
Navjyot Kaur ◽  
Amandeep Kaur ◽  
Gaurav Pushkarna
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Random Forest ◽  
Wireless Sensor ◽  
Detection Accuracy ◽  
Malicious Node ◽  
Malicious Nodes ◽  
Human Management ◽  
Wireless Interface ◽  
Detection Mechanisms

Wireless sensor networks have gained attention over the last few years and have significant applications for example remote supervising and target watching. They can communicate with each other though wireless interface and configure a network. Wireless sensor networks are often deployed in an unfriendly location and most of time it works without human management; individual node may possibly be compromised by the adversary due to some constraints. In this manner, the security of a wireless sensor network is critical. This work will focus on evaluation of mining techniques that can be used to find malicious nodes. The detection mechanisms provide the accuracy of the classification using different algorithm to detect the malicious node. Pragmatically the detection accuracy of J48 is 99.17%, Random Forest is 80.83%, NF Tree is 81.67% and BF Tree is 72.33%. J48 have very high detection accuracy as compared with BF Tree, NF Tree Random Forest.

A Comparative Evaluation of Mining Techniques to Detect Malicious Node in Wireless Sensor Networks

2020 ◽  
pp. 881-894
Author(s):  
Mandeep Singh ◽  
Navjyot Kaur ◽  
Amandeep Kaur ◽  
Gaurav Pushkarna
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Random Forest ◽  
Wireless Sensor ◽  
Detection Accuracy ◽  
Malicious Node ◽  
Malicious Nodes ◽  
Human Management ◽  
Wireless Interface ◽  
Detection Mechanisms

Wireless sensor networks have gained attention over the last few years and have significant applications for example remote supervising and target watching. They can communicate with each other though wireless interface and configure a network. Wireless sensor networks are often deployed in an unfriendly location and most of time it works without human management; individual node may possibly be compromised by the adversary due to some constraints. In this manner, the security of a wireless sensor network is critical. This work will focus on evaluation of mining techniques that can be used to find malicious nodes. The detection mechanisms provide the accuracy of the classification using different algorithm to detect the malicious node. Pragmatically the detection accuracy of J48 is 99.17%, Random Forest is 80.83%, NF Tree is 81.67% and BF Tree is 72.33%. J48 have very high detection accuracy as compared with BF Tree, NF Tree Random Forest.

scholarly journals Detection and Location of Malicious Nodes Based on Homomorphic Fingerprinting in Wireless Sensor Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Zhiming Zhang ◽  
Yu Yang ◽  
Wei Yang ◽  
Fuying Wu ◽  
Ping Li ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Evaluation Model ◽  
Original Data ◽  
Base Station ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Malicious Node ◽  
Malicious Nodes ◽  
Trust Value

The current detection schemes of malicious nodes mainly focus on how to detect and locate malicious nodes in a single path; however, for the reliability of data transmission, many sensor data are transmitted by multipath in wireless sensor networks. In order to detect and locate malicious nodes in multiple paths, in this paper, we present a homomorphic fingerprinting-based detection and location of malicious nodes (HFDLMN) scheme in wireless sensor networks. In the HFDLMN scheme, using homomorphic fingerprint and coding technology, the original data is divided into n packets and sent to the base station along n paths, respectively; the base station determines whether there are malicious nodes in each path by verifying the validity of the packets; if there are malicious nodes in one or more paths, the location algorithm of the malicious node is implemented to locate the specific malicious nodes in the path; if all the packets are valid, the original data is recovered. The HFDLMN scheme does not need any complex evaluation model to evaluate and calculate the trust value of the node, nor any monitoring nodes. Theoretical analysis results show that the HFDLMN scheme is secure and effective. The simulation results demonstrate promising outcomes with respect to key parameters such as the detection probability of the malicious path and the locating probability of the malicious node.

scholarly journals System for Malicious Node Detection in IPv6-Based Wireless Sensor Networks

2016 ◽  
Vol 2016 ◽  
pp. 1-20 ◽  
Author(s):  
Kresimir Grgic ◽  
Drago Zagar ◽  
Visnja Krizanovic Cik
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Detection System ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Security Threats ◽  
Malicious Node ◽  
Malicious Nodes ◽  
Network Topologies ◽  
Malicious Node Detection

The trend of implementing the IPv6 into wireless sensor networks (WSNs) has recently occurred as a consequence of a tendency of their integration with other types of IP-based networks. The paper deals with the security aspects of these IPv6-based WSNs. A brief analysis of security threats and attacks which are present in the IPv6-based WSN is given. The solution to an adaptive distributed system for malicious node detection in the IPv6-based WSN is proposed. The proposed intrusion detection system is based on distributed algorithms and a collective decision-making process. It introduces an innovative concept of probability estimation for malicious behaviour of sensor nodes. The proposed system is implemented and tested through several different scenarios in three different network topologies. Finally, the performed analysis showed that the proposed system is energy efficient and has a good capability to detect malicious nodes.

scholarly journals Distributed Beacon Drifting Detection for Localization in Unstable Environments

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Ming Xia ◽  
Peiliang Sun ◽  
Xiaoyan Wang ◽  
Yan Jin ◽  
Qingzhang Chen
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Detection Algorithm ◽  
Experimental Results ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Location Information ◽  
Detection Accuracy ◽  
Research Issue ◽  
Fundamental Research

Localization is a fundamental research issue in wireless sensor networks (WSNs). In most existing localization schemes, several beacons are used to determine the locations of sensor nodes. These localization mechanisms are frequently based on an assumption that the locations of beacons are known. Nevertheless, for many WSN systems deployed in unstable environments, beacons may be moved unexpectedly; that is, beacons are drifting, and their location information will no longer be reliable. As a result, the accuracy of localization will be greatly affected. In this paper, we propose a distributed beacon drifting detection algorithm to locate those accidentally moved beacons. In the proposed algorithm, we designed both beacon self-scoring and beacon-to-beacon negotiation mechanisms to improve detection accuracy while keeping the algorithm lightweight. Experimental results show that the algorithm achieves its designed goals.

scholarly journals Secure and Robust 3D Localization in Wireless Sensor Networks

2019 ◽  
Vol 9 (1S) ◽  
pp. 104-111
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Sensor Node ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Malicious Nodes ◽  
3D Localization ◽  
Secure Localization ◽  
Simulation Results ◽  
Two Phases

The fundamental capacity of a sensor system is to accumulate and forward data to the destination. It is crucial to consider the area of gathered data, which is utilized to sort information that can be procured using confinement strategy as a piece of Wireless Sensor Networks (WSNs).Localization is a champion among the most basic progressions since it agreed as an essential part in various applications, e.g., target tracking. If the client can't gain the definite area information, the related applications can't be skillful. The crucial idea in most localization procedures is that some deployed nodes with known positions (e.g., GPS-equipped nodes) transmit signals with their coordinates so as to support other nodes to localize themselves. This paper mainly focuses on the algorithm that has been proposed to securely and robustly decide thelocation of a sensor node. The algorithm works in two phases namely Secure localization phase and Robust Localization phase. By "secure", we imply that malicious nodes should not effectively affect the accuracy of the localized nodes. By “robust”, we indicate that the algorithm works in a 3D environment even in the presence of malicious beacon nodes. The existing methodologies were proposed based on 2D localization; however in this work in addition to security and robustness, exact localization can be determined for 3D areas by utilizing anefficient localization algorithm. Simulation results exhibit that when compared to other existing algorithms, our proposed work performs better in terms of localization error and accuracy.

scholarly journals A Fuzzy-Decision Based Approach for Composite Event Detection in Wireless Sensor Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-20 ◽  
Author(s):  
Shukui Zhang ◽  
Hao Chen ◽  
Qiaoming Zhu ◽  
Juncheng Jia
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Event Detection ◽  
Dominating Set ◽  
Wireless Sensor ◽  
Rule Base ◽  
Detection Accuracy ◽  
Two Dimensional ◽  
Fuzzy Decision ◽  
Composite Event

The event detection is one of the fundamental researches in wireless sensor networks (WSNs). Due to the consideration of various properties that reflect events status, the Composite event is more consistent with the objective world. Thus, the research of the Composite event becomes more realistic. In this paper, we analyze the characteristics of the Composite event; then we propose a criterion to determine the area of the Composite event and put forward a dominating set based network topology construction algorithm under random deployment. For the unreliability of partial data in detection process and fuzziness of the event definitions in nature, we propose a cluster-based two-dimensionalτ-GAS algorithm and fuzzy-decision based composite event decision mechanism. In the case that the sensory data of most nodes are normal, the two-dimensionalτ-GAS algorithm can filter the fault node data effectively and reduce the influence of erroneous data on the event determination. The Composite event judgment mechanism which is based on fuzzy-decision holds the superiority of the fuzzy-logic based algorithm; moreover, it does not need the support of a huge rule base and its computational complexity is small. Compared to CollECT algorithm and CDS algorithm, this algorithm improves the detection accuracy and reduces the traffic.

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