Security Analysis in Wireless Sensor Networks

Dener, Murat

doi:10.1155/2014/303501

Cited by 57 publications

(41 citation statements)

References 26 publications

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“…This involves computing the AUC for each ROC curve, one curve is associated with one device, and then computing the mean of all curves considered. Table 3 presents verification results via %Aut, AUC and AUC M at SNR = [18,20,22] dB; further verification results will only be considered herein for SNR = 20dB (the SNR at which GRLVQI achieves %C = 90%). As seen in the %Aut column Table 3, the %Aut rate involves dichotomization, c.f.…”

Section: Verification Mean Auc (Auc M )mentioning

confidence: 99%

“…As discussed in [16], PHY layer security involves either 1) adding physically traceable objects to devices [17] or 2) Radio Frequency Distinct Native Attribute (RF-DNA) fingerprinting based on PHY device emissions which overcome limitations of encryption key-based measures [18]. RF-DNA differs from typical WPAN defense and security strategies that target higher bitlevel network layers [19], i.e., the Network (NWK) and Media Access Control (MAC) layers [20]. Using underutilized PHY information [18] with NWK and MAC information yields a more robust biometric-like wireless security strategy that includes [18], [21]:…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Optimization Framework for Generalized Relevance Learning Vector Quantization with Application to Z-Wave Device Fingerprinting

Bihl¹,

Temple²,

Bauer³

2017

Proceedings of the 50th Hawaii International Conference on System Sciences (2017)

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Section: Verification Mean Auc (Auc M )mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Optimization Framework for Generalized Relevance Learning Vector Quantization with Application to Z-Wave Device Fingerprinting

Bihl¹,

Temple²,

Bauer³

2017

Proceedings of the 50th Hawaii International Conference on System Sciences (2017)

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“…As shown in fig. 2, when node S 9 broadcasts its routing table, S 2 hears it via tunnel and updates its routing table that S 9 is one hop away and {S 8 , S 10 ,S 11 , S 12 , S 13 } all are two hops away. 2) On-Demand Protocol: The mechanism to discover the route in DSR [20] and AODV [21] protocols is an example of on demand protocols.…”

Section: Wormhole Threat Against Routing Protocols 1) Periodic Promentioning

confidence: 99%

“…An attacker can easily capture and compromise a few sensor nodes without being noticed. When sensor nodes are compromised, the attacker can learn all the secrets stored on them and launch a variety of attacks [7][8][9][10][11][12]. Thus any security mechanism for sensor networks has to be resilient to compromised sensor nodes.…”

mentioning

confidence: 99%

Wormhole Attacks and Countermeasures in Wireless Sensor Networks : A Survey

Patel¹,

Aggarwal²,

Chaubey³

2017

IJET

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Abstract-Wireless sensor networks can be deployed in inhospitable terrains or in hostile environments to provide continuous monitoring and processing capabilities. Due to the wireless and distributed nature, security is very crucial issue in wireless sensor network. Security comes from attacks. Detecting wormhole attack is very hard compared to other attacks because it uses private, out-of-band channel to launch the attack. To launch this type of attack, attacker does not require any cryptographic breaks. Wormhole attack represents one forms of Denial of Service attack. It is a gateway of many more attacks. This paper focuses on the various wormhole detection techniques, the open research areas and future research directions.Keywords: Wireless sensor network, security, distributed, wormhole, out-of-band. I. INTRODUCTION Wireless sensor network consists of large number of sensor nodes. Each sensor node consists of processor, analog to digital converter, transceiver and battery. Sensor nodes are densely deployed. They use broadcast communication primitive. Sensor nodes rely on wireless channels for transmitting and receiving data from other nodes [1,2]. Sink node is the data aggregation point. Wireless sensor networks that are capable of observing the environment, processing data and making decisions based on these observations [3][4][5][6]. These networks are important for a number of applications such as coordinated target detection and localization, surveillance and environment monitoring.Providing security services in sensor networks turns out to be a very challenging task. First, sensor nodes usually have very limited resources such as storage, bandwidth, computation and energy. It is often undesirable to implement expensive algorithms on sensor nodes. Second, sensor nodes are usually deployed in unattended environment. An attacker can easily capture and compromise a few sensor nodes without being noticed. When sensor nodes are compromised, the attacker can learn all the secrets stored on them and launch a variety of attacks [7][8][9][10][11][12]. Thus any security mechanism for sensor networks has to be resilient to compromised sensor nodes. Third, most sensor applications are based on local computation and communication, while adversaries are usually much more powerful and resourceful than sensor nodes.Wireless sensor networks are vulnerable to many attacks, but among all the attacks wormhole attack is very dangerous. Two malicious nodes are located far away and create a high speed tunnel [13][14][15]. At one ends of the tunnel, one malicious node receives the traffic and forwards it to the end of the tunnel to the other malicious node. It is also possible that packet is altered that contains different information. The packets pass through the tunnel can propagate faster compared to the normal path. To launch the attack, attacker does not require knowing the protocols used in the network or the services offered in the network.The rest of the paper is organized as follows. In section 2 we have discussed the ...

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“…And the distance between any two nodes is more than 7 meters. The ID of node is orderly numbered by their distance to the Sink from near to Receiving Node adds its own information and rebroadcasts RDP; (6) else (7) Receiving Node drops RDP; (8) end if (9) end while (10) By using (17), the Sink computes the cost for the current route of the RDP; (11) Store the current RDP and its cost into Ω SourceNode → Sink ; (12) if Ω SourceNode → Sink is FULL; (13) By using (19), select the one with the least cost; (14) Sends the REP back to the Source Node over the selected route; (15) Generates the RER, and broadcasts the RER; (4) if (Monitoring Node == Source Node AND Monitored Node == Next Hop) (5) Deletes the route, and invokes Algorithm 1(a); (6) else if (Monitored Node == Next Hop) (7) Deletes the route; (8) end if; (9) end if; (10) while (Receiving Node ̸ = Source Node) (11) if (Sending Node == Next Hop OR Monitored Node == Next Hop) (12) Deletes the route; (13) end if; (14) Broadcast the RER; (15) end while; (16) far. Among the total number of the nodes in the experiment, 10 percent nodes are selected as the malicious nodes.…”

Section: Performance Evaluationmentioning

confidence: 99%

Trusted Tree-Based Trust Management Scheme for Secure Routing in Wireless Sensor Networks

Bie

Zhao

2015

International Journal of Distributed Sensor Networks

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The unique characteristics of wireless sensor networks make them susceptible to the attacks. The secure scheme is considered in designing the routing protocol and algorithm. The cryptography and authentication secure technology can defend the external attacks but have no way to protect the network when the internal attacks occur. However, the trust management scheme with attack-resistance and robustness can efficiently detect the internal attack. On the condition of defending the attacks, it is also an issue that the trust management scheme aids routing algorithm to improve the security and performance. In this paper, we proposed a Trusted Tree-based trust management for secure routing in wireless sensor networks. The common attacks are firstly analyzed. And then the dynamic time window is designed in the distributed trust model with the detection of direct trust and indirect trust. Based on the trusted nodes, Trusted Tree is constructed. And the path quality of nodes in Trusted Tree is used as the gradient for routing. Finally, we add Trusted Tree-based trust management into the QoS based routing protocol and algorithm. Experimental results show that the QoS based routing algorithm with Trusted Tree has better performance and security.

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Security Analysis in Wireless Sensor Networks

Cited by 57 publications

References 26 publications

An Optimization Framework for Generalized Relevance Learning Vector Quantization with Application to Z-Wave Device Fingerprinting

An Optimization Framework for Generalized Relevance Learning Vector Quantization with Application to Z-Wave Device Fingerprinting

Wormhole Attacks and Countermeasures in Wireless Sensor Networks : A Survey

Trusted Tree-Based Trust Management Scheme for Secure Routing in Wireless Sensor Networks

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