Improving Security for SCADA Sensor Networks with Reputation Systems and Self-Organizing Maps

Moya, José Manuel Huidobro; Araújo, Álvaro; Banković, Zorana; Goyeneche, Juan-Mariano de; Vallejo, Juan Carlos; Malagón, Pedro; Villanueva, Daniel; Fraga, David; Romero, Elena; Blesa, Javier

doi:10.3390/s91109380

Cited by 28 publications

(28 citation statements)

References 27 publications

(33 reference statements)

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“…In our previous work [14] we have proven that our system needs lower level of redundancy than the standard reputation systems, so most of the sensors in all the cases are not affected and can remain in sleep mode if necessary.…”

Section: H Incorporation Of the Proposed Model Into Envisioned Wsn Mmentioning

confidence: 97%

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Holistic Solution for Confining Insider Attacks in Wireless Sensor Networks Using Reputation Systems Coupled with Clustering Techniques

Banković

Moya

Vallejo

et al. 2011

2011IEEE 10th International Conference on Trust, Security and Privacy in Computing and Communications

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The most serious obstacle in further proliferation of wireless sensor networks is their low level of security, where the insider attacks are the most challenging issue. In this work we propose a holistic solution for detecting and confining insider attacks that couples reputation systems with clustering techniques, namely unsupervised genetic algorithm and selforganizing maps, trained for detecting outliers in data. The novelty of this work is the redundancy in detecting agents, their evaluation based on the majority voting and the calculation of the reputation as the average value, which makes it more robust to different attack scenarios and their parameter variations. The algorithms use the feature space based on sequences of sensor outputs (both temporal and spatial), as well as the routing paths used to forward the data to the base station, and designed with the idea of introducing the ability to detect a wide range of attacks. The solution performs both attack detection and recovery from attacks, and it offers many benefits: scalable solution, fast response to adversarial activities, ability to detect unknown attacks, high adaptability and high ability in detecting and confining attacks.

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Section: H Incorporation Of the Proposed Model Into Envisioned Wsn Mmentioning

confidence: 97%

“…For this reason, we follow the idea presented in [14], [15], [16] based on extracted n-grams and their frequencies within different time windows. Thus, the vectors used for characterization that allow the deployment of machine learning are composed of the extracted n-grams.…”

Section: Feature Extraction and Formation Of Modelmentioning

confidence: 99%

Holistic Solution for Confining Insider Attacks in Wireless Sensor Networks Using Reputation Systems Coupled with Clustering Techniques

Banković

Moya

Vallejo

et al. 2011

2011IEEE 10th International Conference on Trust, Security and Privacy in Computing and Communications

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“…Moya et al [18] proposed a reputation system in the wireless sensor network, which allows bad reputation feedback to effectively detect and confine some common attacks. Rather than focusing on deploying a reputation system in a wireless sensor network, Moya et al [19] proposed a reputation mechanism in supervisory control and data acquisition (SCADA) sensor networks, which can achieve fault tolerance and enhanced resistance to some unknown attacks. The proposed mechanism enhanced with distributed agents using an unsupervised type of neural network ( i.e.…”

Section: Related Workmentioning

confidence: 99%

Improving Data Quality with an Accumulated Reputation Model in Participatory Sensing Systems

Wang

et al. 2014

Sensors

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The ubiquity of mobile devices brings forth a sensing paradigm, participatory sensing, to collect and interpret sensory information from the environment. Participants join in multifarious sensing tasks and share their data. The sensing result can be obtained in light of shared data. It is not uncommon that some corrupted data is provided by participants, which makes sensing result unreliable accordingly. To address this nontrivial issue, we proposed the accumulated reputation model (ARM) to improve the accuracy of the sensing result. In ARM, participants' reputation will be computed and accumulated based on their sensing data. The sensing data from reputable participants make higher contributions to the sensing result. ARM performs well on calculating accurate sensing results, even in extreme scenarios, where there are many inexperienced or malicious participants.

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“…Trust and reputation [18] have recently been suggested as an effective security mechanism for open and distributed environments (Ad Hoc networks [2], WSNs [1,2], P2P networks [3], etc.). In essence, the nodes that do not behave properly (according to the established policy of 'proper' behaviour) will have low reputation, so the rest of the nodes will avoid any collaboration with them, which is equivalent to its isolation from the network.…”

Section: Introductionmentioning

confidence: 99%

Detecting false testimonies in reputation systems using self-organizing maps

Banković

Vallejo

Fraga

et al. 2012

Logic Journal of IGPL

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It has been demonstrated that rating trust and reputation of individual nodes is an effective approach in distributed environments in order to improve security, support decision-making and promote node collaboration. Nevertheless, these systems are vulnerable to deliberate false or unfair testimonies. In one scenario, the attackers collude to give negative feedback on the victim in order to lower or destroy its reputation. This attack is known as bad mouthing attack. In another scenario, a number of entities agree to give positive feedback on an entity (often with adversarial intentions). This attack is known as ballot stuffing. Both attack types can significantly deteriorate the performances of the network. The existing solutions for coping with these attacks are mainly concentrated on prevention techniques. In this work, we propose a solution that detects and isolates the abovementioned attackers, impeding them in this way to further spread their malicious activity. The approach is based on detecting outliers using clustering, in this case self-organizing maps. An important advantage of this approach is that we have no restrictions on training data, and thus there is no need for any data pre-processing. Testing results demonstrate the capability of the approach in detecting both bad mouthing and ballot stuffing attack in various scenarios.

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Improving Security for SCADA Sensor Networks with Reputation Systems and Self-Organizing Maps

Cited by 28 publications

References 27 publications

Holistic Solution for Confining Insider Attacks in Wireless Sensor Networks Using Reputation Systems Coupled with Clustering Techniques

Holistic Solution for Confining Insider Attacks in Wireless Sensor Networks Using Reputation Systems Coupled with Clustering Techniques

Improving Data Quality with an Accumulated Reputation Model in Participatory Sensing Systems

Detecting false testimonies in reputation systems using self-organizing maps

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