Launching denial-of-service jamming attacks in underwater sensor networks

2011 ◽  
Author(s):  
Michael Zuba ◽  
Zhijie Shi ◽  
Zheng Peng ◽  
Jun-Hong Cui
Keyword(s):  
Sensor Networks ◽  
Denial Of Service ◽  
Underwater Sensor Networks ◽  
Jamming Attacks

scholarly journals Enhancing and Securing the Routing Techniques in Wireless Sensor Networks

2020 ◽  
pp. 141-145
Author(s):  
Rupinder Singh
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Wireless Network ◽  
Denial Of Service ◽  
Wireless Sensor ◽  
Physical Parameters ◽  
Network Node ◽  
Jamming Attacks ◽  
Proposed Model ◽  
Node Network

A wireless network node network (WSN) is defined as being composed of a large number of small light weighted nodes called network node nodes with routing, processing and communication facilities, which are densely deployed in physical or environmental condition. Each of these nodes collects data and its purpose is to route this information back to a sink. WSN is highly constrained type of network, having network node nodes with more capabilities. All network node nodes in the wireless network node network are interact with each other by intermediated network node nodes. Physical parameters computations are power, energy, memory, communication range and bandwidth. Wireless ad-hoc networks mainly use broadcast communication. Upon deployment, network node nodes automatically collaborate and form a network, start collecting data without any input from the user. The proposed model has been improved for the route metric calculation along with node and link load availability information module to avoid the connectivity loopholes and link congestions. The proposed model results have been obtained in the form of various network performance parameters such as network load, transmission delay, throughput, energy consumption, etc. In wireless sensor networks, there are many types of attacks that can hinder or obstruct the data to be deliver to the authenticated node so in order to check which node is authenticated various algorithms have been proposed. There are various attacks like Denial of Service, Distributed Denial of Service and various types of Jamming attacks that can disrupt or deny the communication between sender and receiver. It is important to develop some powerful tools for network analysis, design and managing the performance optimization of the network. In this paper some of the most common attacks and threats are explained and the prevention that can be taken by using various tools is implemented. Also the different routes are configured if the particular route is not available. All the nodes and the attacks are been shown by using a simulator NS2.

Jamming Attacks and Countermeasures in Wireless Sensor Networks

2010 ◽  
pp. 334-352 ◽  
Author(s):  
Sun Yan-qiang ◽  
Wang Xiao-dong
Keyword(s):  
Sensor Networks ◽  
Signal To Noise Ratio ◽  
Denial Of Service ◽  
Future Research ◽  
Delivery Ratio ◽  
Jamming Attacks ◽  
Jamming Attack ◽  
Timing Channel ◽  
Wireless Medium ◽  
Radio Signals

Guaranteeing security of the sensor network is a challenging job due to the open wireless medium and energy constrained hardware. Jamming style Denial-of-Service attacks is the transmission of radio signals that disrupt communications by decreasing the signal to noise ratio. These attacks can easily be launched by jammer through, either bypassing MAC-layer protocols or emitting a radio signal targeted at blocking a particular channel. In this chapter, we survey different jamming attack models and metrics, and figure out the difficulty of detecting and defending such attacks. We also illustrate the existed detecting strategies involving signal strength and packet delivery ratio and defending mechanisms such as channel surfing, mapping jammed region, and timing channel. After that, we explore methods to localize a jammer, and propose an algorithm Geometric-Covering based Localization. Later, we discuss the future research issues in jamming sensor networks and corresponding countermeasures.

scholarly journals A SECURE PACKET HIDING TECHNIQUE FOR PREVENTING JAMMING ATTACKS

2013 ◽  
pp. 31-34
Author(s):  
ANNAPOORNA B.R ◽  
MRS.SHANTHI M. B ◽  
DR.JITENDRANATH MUNGARA
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Denial Of Service ◽  
Transport Network ◽  
Wireless Transmission ◽  
Wireless Sensor ◽  
Security Threat ◽  
Jamming Attacks ◽  
Network Layer ◽  
Threat Model

Wireless Sensor Networks are exposed to serious security threat called jamming. This type of attacks with wireless transmission can be used as a catalyst for rising Denial-of-service attacks. This paper considers the problem of jamming under an internal threat model, where the attacker who is aware of all the network secrets and the details of implementation which results in the difficulty of detection. Jamming is broken down in to layers and this paper focuses on jamming at the Transport/Network layer. To overcome these attacks, we develop three schemes that prevent the attacker from attacking the packets. Then we evaluate the security of our schemes.

Multi-AUV Aided Cooperative 3D-localization for Underwater Sensor Networks

2020 ◽  
Vol 13 (1) ◽  
pp. 80-90 ◽  
Author(s):  
Meiyan Zhang ◽  
Wenyu Cai
Keyword(s):  
Sensor Networks ◽  
Mean Squared Error ◽  
Autonomous Underwater Vehicles ◽  
Estimation Method ◽  
Location Estimation ◽  
Underwater Sensor Networks ◽  
Coverage Ratio ◽  
Minimum Mean Squared Error ◽  
3D Localization ◽  
Localization Scheme

Background: Effective 3D-localization in mobile underwater sensor networks is still an active research topic. Due to the sparse characteristic of underwater sensor networks, AUVs (Autonomous Underwater Vehicles) with precise positioning abilities will benefit cooperative localization. It has important significance to study accurate localization methods. Methods: In this paper, a cooperative and distributed 3D-localization algorithm for sparse underwater sensor networks is proposed. The proposed algorithm combines with the advantages of both recursive location estimation of reference nodes and the outstanding self-positioning ability of mobile AUV. Moreover, our design utilizes MMSE (Minimum Mean Squared Error) based recursive location estimation method in 2D horizontal plane projected from 3D region and then revises positions of un-localized sensor nodes through multiple measurements of Time of Arrival (ToA) with mobile AUVs. Results: Simulation results verify that the proposed cooperative 3D-localization scheme can improve performance in terms of localization coverage ratio, average localization error and localization confidence level. Conclusion: The research can improve localization accuracy and coverage ratio for whole underwater sensor networks.

Security based on Received Signal Strength in Localization for Underwater Sensor Networks

2016 ◽  
Vol 1 (2) ◽  
pp. 1-7
Author(s):  
Karamjeet Kaur ◽  
Gianetan Singh Sekhon
Keyword(s):  
Sensor Networks ◽  
Threshold Value ◽  
Critical Issue ◽  
Sensor Nodes ◽  
Underwater Sensor Networks ◽  
Research Subjects ◽  
Malicious Nodes ◽  
Intermediate Node ◽  
End To End Delay ◽  
End To End

Underwater sensor networks are envisioned to enable a broad category of underwater applications such as pollution tracking, offshore exploration, and oil spilling. Such applications require precise location information as otherwise the sensed data might be meaningless. On the other hand, security critical issue as underwater sensor networks are typically deployed in harsh environments. Localization is one of the latest research subjects in UWSNs since many useful applying UWSNs, e.g., event detecting. Now day’s large number of localization methods arrived for UWSNs. However, few of them take place stability or security criteria. In purposed work taking up localization in underwater such that various wireless sensor nodes get localize to each other. RSS based localization technique used remove malicious nodes from the communication intermediate node list based on RSS threshold value. Purposed algorithm improves more throughput and less end to end delay without degrading energy dissipation at each node. The simulation is conducted in MATLAB and it suggests optimal result as comparison of end to end delay with and without malicious node.

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