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Detecting unknown attacks in wireless sensor networks that contain mobile nodes.

Banković Z, Fraga D, Moya JM, Vallejo JC - Sensors (Basel) (2012)

Bottom Line: The data produced in the presence of an attacker are treated as outliers, and detected using clustering techniques.These techniques are further coupled with a reputation system, in this way isolating compromised nodes in timely fashion.The proposal exhibits good performances at detecting and confining previously unseen attacks, including the cases when mobile nodes are compromised.

View Article: PubMed Central - PubMed

Affiliation: Departamento de Ingeniería Electrónica, ETSI Telecomunicación, Universidad Politécnica de Madrid, Av. Complutense, 30, 28040 Madrid, Spain. zorana@die.upm.es

ABSTRACT
As wireless sensor networks are usually deployed in unattended areas, security policies cannot be updated in a timely fashion upon identification of new attacks. This gives enough time for attackers to cause significant damage. Thus, it is of great importance to provide protection from unknown attacks. However, existing solutions are mostly concentrated on known attacks. On the other hand, mobility can make the sensor network more resilient to failures, reactive to events, and able to support disparate missions with a common set of sensors, yet the problem of security becomes more complicated. In order to address the issue of security in networks with mobile nodes, we propose a machine learning solution for anomaly detection along with the feature extraction process that tries to detect temporal and spatial inconsistencies in the sequences of sensed values and the routing paths used to forward these values to the base station. We also propose a special way to treat mobile nodes, which is the main novelty of this work. The data produced in the presence of an attacker are treated as outliers, and detected using clustering techniques. These techniques are further coupled with a reputation system, in this way isolating compromised nodes in timely fashion. The proposal exhibits good performances at detecting and confining previously unseen attacks, including the cases when mobile nodes are compromised.

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Related in: MedlinePlus

Detection Rate vs. % of Malicious Nodes.
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Related In: Results  -  Collection

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f3-sensors-12-10834: Detection Rate vs. % of Malicious Nodes.

Mentions: In the following experiments we will gradually increase the number of compromised nodes, increasing as well the number of attacked mobile nodes. In Figure 3 we present the dependence of the average detection rate of the percentage of the malicious nodes.


Detecting unknown attacks in wireless sensor networks that contain mobile nodes.

Banković Z, Fraga D, Moya JM, Vallejo JC - Sensors (Basel) (2012)

Detection Rate vs. % of Malicious Nodes.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3472860&req=5

f3-sensors-12-10834: Detection Rate vs. % of Malicious Nodes.
Mentions: In the following experiments we will gradually increase the number of compromised nodes, increasing as well the number of attacked mobile nodes. In Figure 3 we present the dependence of the average detection rate of the percentage of the malicious nodes.

Bottom Line: The data produced in the presence of an attacker are treated as outliers, and detected using clustering techniques.These techniques are further coupled with a reputation system, in this way isolating compromised nodes in timely fashion.The proposal exhibits good performances at detecting and confining previously unseen attacks, including the cases when mobile nodes are compromised.

View Article: PubMed Central - PubMed

Affiliation: Departamento de Ingeniería Electrónica, ETSI Telecomunicación, Universidad Politécnica de Madrid, Av. Complutense, 30, 28040 Madrid, Spain. zorana@die.upm.es

ABSTRACT
As wireless sensor networks are usually deployed in unattended areas, security policies cannot be updated in a timely fashion upon identification of new attacks. This gives enough time for attackers to cause significant damage. Thus, it is of great importance to provide protection from unknown attacks. However, existing solutions are mostly concentrated on known attacks. On the other hand, mobility can make the sensor network more resilient to failures, reactive to events, and able to support disparate missions with a common set of sensors, yet the problem of security becomes more complicated. In order to address the issue of security in networks with mobile nodes, we propose a machine learning solution for anomaly detection along with the feature extraction process that tries to detect temporal and spatial inconsistencies in the sequences of sensed values and the routing paths used to forward these values to the base station. We also propose a special way to treat mobile nodes, which is the main novelty of this work. The data produced in the presence of an attacker are treated as outliers, and detected using clustering techniques. These techniques are further coupled with a reputation system, in this way isolating compromised nodes in timely fashion. The proposal exhibits good performances at detecting and confining previously unseen attacks, including the cases when mobile nodes are compromised.

Show MeSH
Related in: MedlinePlus