A Study of Defense Mechanisms Against Multiple Attacks in Clustered Wireless Sensor Networks

Wireless sensor networks (WSNs) have attracted a lot of interest in the research community due to their potential applicability in a wide range of real-world practical applications. However, due to the distributed nature and their deployments in critical applications without human interventions and sensitivity and criticality of data communicated, these networks are vulnerable to numerous security and privacy threats that can adversely affect their performance. These issues become even more critical in cognitive wireless sensor networks (CWSNs) in which the sensor nodes have the capabilities of changing their transmission and reception parameters according to the radio environment under which they operate in order to achieve reliable and efficient communication and optimum utilization of the network resources. This chapter presents a comprehensive discussion on the security and privacy issues in CWSNs by identifying various security threats in these networks and various defense mechanisms to counter these vulnerabilities. Various types of attacks on CWSNs are categorized under different classes based on their natures and targets, and corresponding to each attack class, appropriate security mechanisms are also discussed. Some critical research issues on security and privacy in CWSNs are also identified.

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Security Issues, Vulnerabilities, and Defense Mechanisms in Wireless Sensor Networks: State of the Art and Recommendation

Integration of WSNs into Internet of Things ◽

10.1201/9781003107521-1 ◽

2021 ◽

pp. 1-15

Author(s):

N. Rahimi ◽

B. Gupta

Keyword(s):

Wireless Sensor Networks ◽

Sensor Networks ◽

Defense Mechanisms ◽

State Of The Art ◽

Wireless Sensor ◽

Security Issues

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Distributed Software-Attestation Defense against Sensor Worm Propagation

Journal of Sensors ◽

10.1155/2015/874782 ◽

2015 ◽

Vol 2015 ◽

pp. 1-6 ◽

Cited By ~ 3

Author(s):

Jun-Won Ho

Keyword(s):

Wireless Sensor Networks ◽

Sensor Networks ◽

Defense Mechanisms ◽

Main Idea ◽

Sensor Nodes ◽

Wireless Sensor ◽

Worm Propagation ◽

On Demand ◽

Software Diversity ◽

Worm Attacks

Wireless sensor networks are vulnerable to sensor worm attacks in which the attacker compromises a few nodes and makes these compromised nodes initiate worm spread over the network, targeting the worm infection of the whole nodes in the network. Several defense mechanisms have been proposed to prevent worm propagation in wireless sensor networks. Although these proposed schemes use software diversity technique for worm propagation prevention under the belief that different software versions do not have common vulnerability, they have fundamental drawback in which it is difficult to realize the aforementioned belief in sensor motes. To resolve this problem, we propose on-demand software-attestation based scheme to defend against worm propagation in sensor network. The main idea of our proposed scheme is to perform software attestations against sensor nodes in on-demand manner and detect the infected nodes by worm, resulting in worm propagation block in the network. Through analysis, we show that our proposed scheme defends against worm propagation in efficient and robust manner. Through simulation, we demonstrate that our proposed scheme stops worm propagation at the reasonable overhead while preventing a majority of sensor nodes from being infected by worm.

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Deep Reinforcement Learning for Attacking Wireless Sensor Networks

Sensors ◽

10.3390/s21124060 ◽

2021 ◽

Vol 21 (12) ◽

pp. 4060

Author(s):

Juan Parras ◽

Maximilian Hüttenrauch ◽

Santiago Zazo ◽

Gerhard Neumann

Keyword(s):

Wireless Sensor Networks ◽

Reinforcement Learning ◽

Sensor Networks ◽

Defense Mechanisms ◽

Defense Mechanism ◽

A Priori ◽

Parameter Tuning ◽

Wireless Sensor ◽

Distributed Spectrum Sensing ◽

Priori Information

Recent advances in Deep Reinforcement Learning allow solving increasingly complex problems. In this work, we show how current defense mechanisms in Wireless Sensor Networks are vulnerable to attacks that use these advances. We use a Deep Reinforcement Learning attacker architecture that allows having one or more attacking agents that can learn to attack using only partial observations. Then, we subject our architecture to a test-bench consisting of two defense mechanisms against a distributed spectrum sensing attack and a backoff attack. Our simulations show that our attacker learns to exploit these systems without having a priori information about the defense mechanism used nor its concrete parameters. Since our attacker requires minimal hyper-parameter tuning, scales with the number of attackers, and learns only by interacting with the defense mechanism, it poses a significant threat to current defense procedures.

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A Survey on Security in Wireless Sensor Networks

Theory and Practice of Cryptography Solutions for Secure Information Systems - Advances in Information Security, Privacy, and Ethics ◽

10.4018/978-1-4666-4030-6.ch010 ◽

2013 ◽

pp. 223-251 ◽

Cited By ~ 2

Author(s):

Ilker Korkmaz ◽

Orhan Dagdeviren ◽

Fatih Tekbacak ◽

Mehmet Emin Dalkilic

Keyword(s):

Wireless Sensor Networks ◽

Wireless Sensor Network ◽

Sensor Networks ◽

Defense Mechanisms ◽

Attack Detection ◽

Sensor Nodes ◽

Wireless Sensor ◽

Single Node ◽

Research Issues ◽

Specific Part

Wireless Sensor Network (WSN) is a promising technology that has attracted the interest of research in the last decade. Security is one of the fundamental issues in sensor networks since sensor nodes are very resource constrained. An attacker may modify, insert, and delete new hardware and software components to the system where a single node, a specific part of the sensing area, and the whole network may become inoperable. Thus, the design of early attack detection and defense mechanisms must be carefully considered. In this chapter, the authors survey attacks and their defense mechanisms in WSNs. Attacks are categorized according to the related protocol layer. They also investigate the open research issues and emerging technologies on security in WSNs.

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