Fast Detection of Replica Node Attacks in Mobile Sensor Networks Using Sequential Analysis

Ho, Jun-Won; Wright, Matthew; Das, Sajal K.

doi:10.1109/infcom.2009.5062097

Cited by 65 publications

(72 citation statements)

References 11 publications

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“…Figure 5 illustrates the measurement of average delay for detection of intrusion attacks in WSN. Compared to the existing sequential hypothesis testing (Ho et al, 2009), the average delay is lesser using the proposed PC model. Figure 6 shows the energy consumption with comparison made to the existing sequential hypothesis testing (Ho et al, 2009) and finally, Fig.…”

Section: Resultsmentioning

confidence: 94%

“…Figure 6 shows the energy consumption with comparison made to the existing sequential hypothesis testing (Ho et al, 2009) and finally, Fig. 7 describes the false positive rate when compared to the existing sequential hypothesis testing (Ho et al, 2009), the proposed PC model has less false positive rate.…”

Section: Resultsmentioning

confidence: 99%

“…A fast,efficient mobile replica node recognition method is presented in (Ho et al, 2009) utilizing the sequential probability ratio test. On the other hand, these systems processes on permanent sensor positions and therefore do not proceed in mobile sensor networks.…”

Section: Literature Reviewmentioning

confidence: 99%

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Detecting Multiple Intrusion Attacks Using Permanent Girth Clustering Model in Wireless Sensor Network

Jayamurugan¹

2014

Journal of Computer Science

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Security is the central challenge and one of the serious concerns for designing reliable sensor networks. Of the different types of security threats in wireless sensor network, particularly dangerous attack is the replica node attack, in which the opponent takes the secret keying materials from a compromised node. It then produces large number of attacker-controlled replicas that divide up the cooperation node's keying materials and ID. In this study we are specifically interested in investigating the extremely difficult problem concerning multiple attacks being routed in parallel with a given utility field and see if significant hypothetical solutions could be drawn. A clustering model for discovering multiple intrusions in WSN is identified. Permanent-girth Clustering (PC) model used to detect abnormal traffic patterns and then uses PC model to built the normal traffic behavior. In this study we develop mechanisms so that the PC model is capable to distinguish attacks. Furthermore, the detection scheme is based on position of traffic features that potentially are practical to an extensive variety of routing attacks. In order to approximate intrusion detection scheme, extensive sensor network simulator producing routing attacks in wireless sensor networks is designed. PC model for intrusion detection is capable to attain high detection accuracy with a low false positive rate for a multiplicity of replicated routing attacks. NS2 simulator is used to perform the experimental work of PC model on wireless sensor network. The experimental evaluation of PC model is measured in terms of average delay measurement, energy consumption and low false positive rate.

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Section: Resultsmentioning

confidence: 94%

Section: Resultsmentioning

confidence: 99%

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Detecting Multiple Intrusion Attacks Using Permanent Girth Clustering Model in Wireless Sensor Network

Jayamurugan¹

2014

Journal of Computer Science

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“…Ho et al proposed a scheme which utilizes a sequential probability ratio test [11]. A claimer node locally broadcasts its location claim to its neighbors from time to time.…”

Section: Related Workmentioning

confidence: 99%

Imposter Detection in Mobile Wireless Sensor Networks

Alrashed¹,

Karaata²

2014

IJCCE

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Abstract-Mobile wireless sensor networks (MWSNs) are wireless networks of small sensors moving around in a specified coverage area, conveying their readings and data to static or mobile base stations. In contrast to a static wireless sensor network where the fixed node position can be used to authenticate a sensor node, the changing position of a node in a MWSN cannot be used in the authentication process. This can lead to imposters posing as legitimate nodes anywhere in the network. An imposter can use the identity of a legitimate node to communicate within the network and eavesdrop on confidential communication or to send false information. In this paper, we propose a novel scheme which employs nonce-values and blackout-time mechanisms to detect imposters in the network. The proposed scheme employs a quarantining mechanism to quarantine the detected imposters, and a node-restoration mechanism to appropriately authenticate and restore quarantined nodes into the network.Index Terms-Imposter detection, mobile sensor networks, wireless network security, wireless networks.

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“…The base station computes the speed from every two consecutive claims of a mobile node and performs the SQRT by taking speed as an observed sample. Each time maximum speed is exceeded by the mobile node, it will expedite the random walk to hit (or) cross the upper limit and thus lead to the base station accepting the alternate hypothesis that the mobile node has been replicated [26].…”

Section: Fast Detection Using Sqrtmentioning

confidence: 99%

Clone Attack Detection Protocols in Wireless Sensor Networks: A Survey

Anthoniraj¹,

Razak²

2014

IJCA

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Wireless Sensor Network (WSN) is a collection of autonomous sensor nodes which are low cost hardware components consists of sensor nodes with constraints on battery life, memory size and computation capabilities to monitor physical (or) environmental conditions. WSN is deployed in unattended and unsecure environments, so it is vulnerable to various types of attacks. One of the physical attacks is node replication attack (or) clone attack. An adversary can easily capture one node from the network and extract information from captured node. Then reprogram it to create a clone of a captured node. Then these clones can be deployed in all network areas, they can be considered as legitimate members of the network, so it is difficult to detect a replicated node. WSN can be either static (or) mobile, in that centralized and distributed clone attack detection methods are available. In this paper we analysis various centralized and distributed protocols in the static and mobile environments. We review these protocols and compare their performance with the help of witness selection, communication and memory overhead, detection probability of replicated nodes, resilience against adversary's node compromise. General TermsClone attack, Clone attack detection approach, Static nodes, and Mobile nodes.

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Fast Detection of Replica Node Attacks in Mobile Sensor Networks Using Sequential Analysis

Cited by 65 publications

References 11 publications

Detecting Multiple Intrusion Attacks Using Permanent Girth Clustering Model in Wireless Sensor Network

Detecting Multiple Intrusion Attacks Using Permanent Girth Clustering Model in Wireless Sensor Network

Imposter Detection in Mobile Wireless Sensor Networks

Clone Attack Detection Protocols in Wireless Sensor Networks: A Survey

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