Mobile Sensor Network Resilient Against Node Replication Attacks

Yu, Chia-Fu; Lu, Chun-Shien; Kuo, Sy‐Yen

doi:10.1109/sahcn.2008.82

Cited by 54 publications

(58 citation statements)

References 7 publications

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“…All the nodes (normal and malicious) has equal radio range (r=10). In addition, the mobility model of [21] is employed for the nodes movement in the function area. In order to be sure of the results validity, each test is repeated 100 times and the final result is the average of these 100 tests.…”

Section: Simulation Resultsmentioning

confidence: 99%

A Lightweight Algorithm for Detecting Sybil Attack in Mobile Wireless Sensor Networks using Sink Nodes

Andalib¹,

Jamshidi²,

Andalib³

et al. 2016

IJCATR

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Considering the application of wireless sensor networks in critical area, such as battlefields, establishing security in these networks is of utmost importance. One of the most serious and dangerous attack against these networks is Sybil attack. In this attack, a malicious hostile node creates multiple fake identities simultaneously. This misleads legitimate nodes and, by mistake, they assume each of these identifiers as real separate nodes. In this attack, malicious hostile node attracts so heavy traffic that can dramatically disrupt routing protocols which has devastating effects on the network functions such as data integration, voting, and resource allocation. The current research proposes a new lightweight algorithm for detecting Sybil attack in Mobile Wireless Sensor Networks using sink nodes. The proposed algorithm is implemented to be assessed in terms of detection and error rates efficiency in a series of experiments. Comparison of the experiment results with the results of other available algorithms revealed optimal performance of the proposed algorithm.

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Section: Simulation Resultsmentioning

confidence: 99%

A Lightweight Algorithm for Detecting Sybil Attack in Mobile Wireless Sensor Networks using Sink Nodes

Andalib¹,

Jamshidi²,

Andalib³

et al. 2016

IJCATR

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show abstract

“…BC-MEM achieves a higher detection probability than both LSM and B-MEM by using the cell forwarding technique [21]. HDA have more efficient detection probability than RM and LSM [22].A unique feature of XED is that each node is capable of detecting replicas per move which contrasts sharply with other protocols that need to mobilize the whole network for replica detection [27]. The probability of detecting replicated nodes in NBDS is much higher than RM, LSM protocols.…”

Section: Detection Of Replicated Nodesmentioning

confidence: 99%

“…The sensor node si meets another sensor node sj. If si never meets sj before, they exchange random numbers otherwise the sensor node si request the sensor node sj for the random number exchanged at earlier time, For the sensor node si, if the sensor node sj cannot replies or replies a number which does not match the number in si's memory, si announces the detection of a replica [27].…”

Section: Extremely Efficient Detection (Xed)mentioning

confidence: 99%

“…It cannot affordable for the current generation of wireless sensor networks [26]. The XED protocol assumed that the replicas cannot communicate each other, suppose replicas communicate each other then they can establish secret channels among each other and they can easily deceive the detection technique [27]. The EDD protocol is inapplicable due to high storage over head for large scale WSNs [29].…”

Section: Drawbacks Of the Protocolsmentioning

confidence: 99%

See 1 more Smart Citation

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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“…Furthermore, voting detection schemes lack the ability to detect clones if the number of replicas is large because of their collusion. A recent scheme [18] for clone attack detection in mobile environments proposed a solution in which each node records random numbers it has exchanged with the nodes which it encounters. If these two nodes contact again, they check the random number they have exchanged before.…”

Section: Related Workmentioning

confidence: 99%

Social closeness based clone attack detection for mobile healthcare system

Ren

Chen

Chuah

2012

2012 IEEE 9th International Conference on Mobile Ad-Hoc and Sensor Systems (MASS 2012)

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Abstract-The explosion of the usage of mobile devices and their rapid deployment of sensing technology provide users with the ability to sense the world. The collected sensing data from the mobile device enabled network can be mined not only for extracting social communities which reflect close relationships or similar behavior patterns among people but also for supporting a broad range of applications including mobile healthcare systems. However, the mobile networks are vulnerable to clone attacks, in which the adversary replicates the legitimate nodes and distributes the clones throughout the network to start a variety of insider attacks. The traditional detection approaches can not address such kind of insider attacks in the mobile device network. (ychen: too vague.) In this paper, we propose a social community based method that exploits the social relationships to detect the clone attack launched to undermine a mobile device enabled disease propagation control framework. We define a new metric called community betweenness, which considers both the community and neighboring information of mobile users. We further propose three methods based on community betweenness to facilitate the detection of the clone attack. An analytical analysis on threshold setting when using community betweeness is provided. We evaluate our clone attack detection scheme through extensive simulations using a trace-driven approach by utilizing data sets collected from mobile phones. The results confirm that our method can detect clone attacks efficiently with high detection ratio and low false positive rate. This strongly indicates the feasibility of exploiting the social community information derived from mobile sensing data for detecting the clone attacks.

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Mobile Sensor Network Resilient Against Node Replication Attacks

Cited by 54 publications

References 7 publications

A Lightweight Algorithm for Detecting Sybil Attack in Mobile Wireless Sensor Networks using Sink Nodes

A Lightweight Algorithm for Detecting Sybil Attack in Mobile Wireless Sensor Networks using Sink Nodes

Clone Attack Detection Protocols in Wireless Sensor Networks: A Survey

Social closeness based clone attack detection for mobile healthcare system

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