Towards multisensor data fusion for DoS detection

Siaterlis, Christos; Maglaris, Vasilis

doi:10.1145/967900.967992

Cited by 91 publications

(61 citation statements)

References 12 publications

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“…Within the first group are Gaussian models or Bayesian networks (Siaterlis andMaglaris, 2004, 2001). One of the boundary-based classifiers is the well-known kNN, modified for the case of one-class classification (Byers and Raftery, 1998;Eskin et al, 2002), or support vector machines (Ratsch et al, 2002;Tax and Duin, 2004).…”

Section: One-class Classificationmentioning

confidence: 99%

Discernment of bee pollen loads using computer vision and one-class classification techniques

Chica¹,

Campoy²

2012

Journal of Food Engineering

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In this paper, we propose a system for authenticating local bee pollen against fraudulent samples using image processing and classification techniques. Our system is based on the colour properties of bee pollen loads and the use of one-class classifiers to reject unknown pollen samples. The latter classification techniques allow us to tackle the major difficulty of the problem, the existence of many possible fraudulent pollen types.Also presented is a multi-classifier model with an ambiguity discovery process to fuse the output of the one-class classifiers. The method is validated by authenticating Spanish bee pollen types, the overall accuracy of the final system of being 94%. Therefore, the system is able to rapidly reject the non-local pollen samples with inexpensive hardware and without the need to send the product to the laboratory.

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Section: One-class Classificationmentioning

confidence: 99%

Discernment of bee pollen loads using computer vision and one-class classification techniques

Chica¹,

Campoy²

2012

Journal of Food Engineering

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“…D-S theory has been previously used in the intrusion detection field to enhance detection accuracy [3], [15], [16]. In [15], the problem of discovering anomalies in large-scale networks based on the data fusion of heterogeneous monitors is considered.…”

Section: Related Workmentioning

confidence: 99%

“…Data fusion can be defined as the process of collecting information from multiple and heterogeneous sources, and combining this to obtain a more accurate final result [3]. As we have shown in previous work [1], [2], Dempster-Shafer (D-S) theory of evidence is a good candidate for this purpose because it does not require a priori knowledge of the system, and provides the ability to manage uncertainty.…”

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confidence: 99%

Manual and Automatic assigned thresholds in multi‐layer data fusion intrusion detection system for 802.11 attacks

Kyriakopoulos

Aparicio-Navarro

Parish

2014

IET Information Security

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., 2014. Manual and automatic assigned thresholds in multi-layer data fusion intrusion detection system for 802.11 attacks. IET Information Security,8 (1), pp. 42 -50.Additional Information:• This paper was published in the journal, IET Information Security [ c Intrusion Detection Systems for wireless attacks either focuses on just one layer of observation or uses a limited number of metrics without proper data fusion techniques. However, the true status of a network is rarely accurately detectable by examining only one network layer. The goal of this work is to detect injection types of attacks in wireless networks by fusing multi-metrics using the Dempster-Shafer (D-S) belief theory. When combining beliefs, an important step to consider is the automatic and self-adaptive process of Basic Probability Assignment (BPA).This work presents a comparison between manual and automatic BPA methods using the D-S technique. Custom tailoring BPAs in an optimum manner under specific network conditions could be extremely time consuming and difficult. In contrast, automatic methods have the advantage of not requiring any prior training or calibration from an administrator. The results show that multi-layer techniques perform more efficiently when compared to conventional methods. In addition, the automatic assignment of beliefs makes the use of such a system easier to deploy whilst providing a similar performance to that of a manual system.

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“…Context information fusion concerns with how this contextual information gathered by sensors can be processed to increase its relevance. Contextual information fusion can be commonly used in detection and classi cation tasks, such as robotics and military applications [39], intrusion detection [40] and Denial of Service (DoS) detection [41].…”

Section: Classi Cation Of Context Information Fusionmentioning

confidence: 99%

Survey of context information fusion for ubiquitous Internet-of-Things (IoT) systems

Borges

2016

Open Computer Science

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Internet-of-Things (IoT) is the latest buzzword, havings its origins in the erstwhile Sensor Networks. Sensor Networks produce a large amount of data. According to the needs this data requires to be processed, delivered and accessed. This processed data when made available with the physical device location, user preferences, time constraints; generically called as context-awareness; is widely referred to as the core function for ubiquitous systems. To our best knowledge there is lack of analysis of context information fusion for ubiquitous sensor networks. Adopting appropriate information fusion techniques can help in screening noisy measurements, control data in the network and take necessary inferences that can help in contextual computing. In this paper we try and explore di erent context information fusion techniques by comparing a large number of solutions, their methods, architectures and models. All the surveyed techniques can be adapted to the IoT framework.

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Towards multisensor data fusion for DoS detection

Cited by 91 publications

References 12 publications

Discernment of bee pollen loads using computer vision and one-class classification techniques

Discernment of bee pollen loads using computer vision and one-class classification techniques

Manual and Automatic assigned thresholds in multi‐layer data fusion intrusion detection system for 802.11 attacks

Survey of context information fusion for ubiquitous Internet-of-Things (IoT) systems

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