Decision Fusion Using Fuzzy Dempster-Shafer Theory

Surathong, Somnuek; Auephanwiriyakul, Sansanee; Theera-Umpon, Nipon

doi:10.1007/978-3-319-93692-5_12

Cited by 6 publications

(2 citation statements)

References 23 publications

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“…The Dempster-Shafer theory is a probabilistic combination strategy that has been used to combine the output from classifiers [45]. The output that we get from the DNN will be the probability of test data belonging to a particular IIoT attack.…”

Section: Dempster Shafer's Combination Rulementioning

confidence: 99%

Deep Learning Based Attack Detection in IIoT using Two-Level Intrusion Detection System

Raja

Karthikeyan

Abilash

et al. 2021

Preprint

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The Industrial Internet of Things (IIoT), also known as Industry 4.0, has brought a revolution in the production and manufacturing sectors as it assists in the automation of production management and reduces the manual effort needed in auditing and managing the pieces of machinery. IoT-enabled industries, in general, use sensors, smart meters, and actuators. Most of the time, the data held by these devices is surpassingly sensitive and private. This information might be modified, 1 stolen, or even the devices may be subjected to a Denial of Service (DoS) attack. As a consequence, the product quality may deteriorate or sensitive information may be leaked. An Intrusion Detection System (IDS), implemented in the network layer of IIoT, can detect attacks, thereby protecting the data and devices. Despite substantial advancements in attack detection in IIoT, existing works fail to detect certain attacks obfuscated from detectors resulting in a low detection performance. To address the aforementioned issue, we propose a Deep Learning-based Two Level Network Intrusion Detection System (DLTL-NIDS) for IIoT environment, emphasizing challenging attacks. The attacks that attain low accuracy or low precision in level-1 detection are marked as challenging attacks. Experimental results show that the proposed model, when tested against TON IoT, figures out the challenging attacks well and achieves an accuracy of 99.97%, precision of 95.62%, recall of 99.5%, and F1-score of 99.65%. The proposed DL-TLNIDS, when compared with state-of-art models, achieves a decrease in false alarm rate to 2.34% (flagging normal traffic as an attack) in IIoT.

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Section: Dempster Shafer's Combination Rulementioning

confidence: 99%

Deep Learning Based Attack Detection in IIoT using Two-Level Intrusion Detection System

Raja

Karthikeyan

Abilash

et al. 2021

Preprint

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“…The Dempster Shafer Theory (DST) of evidence is the extension of the probabilistic approach, which can handle imprecise and uncertain data. It is primarily used in sensor fusion [14] and decision level fusion [15], [16]. Unlike the other technique that deals with approximation concepts such as fuzzy set theory, conventional DST provides the objective form of analysis without relying on the reliability of a particular method and its associated result.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty based Regional-Decision Fusion of Multispectral and nDSM for Ultra-High Resolution UAV Imagery

Soni

Koner

Villuri

2022

Preprint

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The recent advancement in the pattern recognition technique has demonstrated the superiority in remote sensing technology, where Deep neural network uses the spatial feature representation such as convolution neural network (CNN), to provide better generalization capability. Regardless of any CNN structure, the prediction always involves uncertainty and imprecision while classifying the ultra-high resolution (UHR) image. Our aim is two-fold: firstly, increase the reliability feature by performing the Dual-scale fusion via a modified Markov random field known as DuCNN-MMRF. Secondly, an integration framework was introduced to combine the multispectral image classification produced by DuCNN-MMRF and normalized-Digital Surface Model (nDSM) information, using a novel approach known as constraint-based Dempster Shafer theory (C-DST). C-DST targeted DuCNN-MMRF’s uncertain information (ambiguous information) and rectified it with complementary information. The complementary information (i.e., nDSM) was processed using conventional machine learning (ML) techniques such as Multilayer perceptron (MLP), Support vector machine (SVM) and Random Forest (RF). The integration approach also uses the Shannon entropy function to exploit the uncertain information of model prediction (i.e. DuCNN-MMRF) at the regional level and subdivide into the positive and negative regions. The positive region is trusted by DuCNN-MMRF, and the negative region was combined with DuCNN-MMRF and MLP (and others ML) in a restricted manner. An ultra-high resolution (UHR) images was collected with an unmanned aerial vehicle (UAV) in the semi-urban region of IIT, Dhanbad, India, and labeled with building features. In addition, ISPRS Potsdam dataset with five land cover classes (Tree, building, grassland, impervious surface, and car) was used. The proposed framework was tested with several combinations i.e. MMRF-SVM, MMRF-RF, MMRF-MLP, along with max-voting fusion. The MMRF-MLP yielded highest accuracy with 85.24% (Fmeasure) and 97.79%(OA), and 76.12%(Fmeasure) and 91.09%(OA), for study area and Potsdam dataset, respectively.

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Wireless Sensor Network to Improve Security Performance and Packet Delivery Ratio Using FCL-Boost Based Classification Method

Sangeethapriya,

Bharathi,

Jagan Mohan Rao

et al. 2024

Studies in Computational Intelligence

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Decision Fusion Using Fuzzy Dempster-Shafer Theory

Cited by 6 publications

References 23 publications

Deep Learning Based Attack Detection in IIoT using Two-Level Intrusion Detection System

Deep Learning Based Attack Detection in IIoT using Two-Level Intrusion Detection System

Uncertainty based Regional-Decision Fusion of Multispectral and nDSM for Ultra-High Resolution UAV Imagery

Wireless Sensor Network to Improve Security Performance and Packet Delivery Ratio Using FCL-Boost Based Classification Method

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