A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation | Paper Citation Record

Observation b968fa4c-9aa1-4e60-99d6-fb788ce30133 · outbound

This paper cites Classiﬁcation of ransomware families with machine learning based on N-gram of opcodes,.

A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation Classiﬁcation of ransomware families with machine learning based on N-gram of opcodes,

Reference 1

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Observation 1b72a6cd-8302-42bf-a155-85c0516e6c39 · outbound

This paper cites Ransomware, threat and detection techniques: A review,.

A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation Ransomware, threat and detection techniques: A review,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:39:39.870153Z

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Observation d009690c-2bcf-429a-b5ca-00f01bc004f0 · outbound

This paper cites Ransomware threat success factors, taxonomy, and countermeasures: A survey and research directions,.

A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation Ransomware threat success factors, taxonomy, and countermeasures: A survey and research directions,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:39:39.856637Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation b49d9f7a-2f5c-43c1-953c-e811994d364a · outbound

This paper cites Evaluating shallow and deep networks for ransomware detection and classiﬁcation,.

A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation Evaluating shallow and deep networks for ransomware detection and classiﬁcation,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:39:39.842323Z

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Observation bd8eee9c-184b-4c72-8e43-3b159aca196e · outbound

This paper cites Extinguishing ransomware - A hybrid approach to Android ransomware detection,.

A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation Extinguishing ransomware - A hybrid approach to Android ransomware detection,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:39:39.828580Z

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Observation 69fcd322-bbbf-4ffd-a909-67fba3e95ca2 · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 6

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Observation 10fb691f-57a9-409d-8b3a-ef72b3d286e3 · outbound

This paper cites Deep residual learning for image recognition,.

A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation Deep residual learning for image recognition,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:39:39.814414Z

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Observation 93485287-1f10-479c-8ab3-ed2ae8201fb1 · outbound

This paper cites SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size.

A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-14T12:39:38.980604Z

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Observation 7bcf0d67-cd55-4e36-8b1b-1b2ad379840f · outbound

This paper cites Densely connected convolutional networks,.

A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation Densely connected convolutional networks,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:39:39.800696Z

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Observation dcdf9226-2646-44bc-a1f0-d810c3a4a3ce · outbound

This paper cites Rethinking the Inception architecture for computer vision,.

A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation Rethinking the Inception architecture for computer vision,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:39:39.786540Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 33b28ebf-740b-4c05-ab07-094af9510064 · outbound

This paper cites ShufﬂeNet v2: Practical guidelines for efﬁcient CNN architecture design,.

A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation ShufﬂeNet v2: Practical guidelines for efﬁcient CNN architecture design,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:39:39.773440Z

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Observation 5b4388ed-17d4-46cc-bc51-e90ead5f994a · outbound

This paper cites MobileNetv2: Inverted residuals and linear bottlenecks,.

A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation MobileNetv2: Inverted residuals and linear bottlenecks,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:39:39.759624Z

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Observation 68323498-ccf1-4aed-b195-9f056b73fdd8 · outbound

This paper cites Aggregated residual transformations for deep neural networks,.

A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation Aggregated residual transformations for deep neural networks,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:39:39.746385Z

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Observation f2ce3def-66aa-47bb-aa04-6c7e6ca3ae0c · outbound

This paper cites How to make a neural network say dont know,.

A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation How to make a neural network say dont know,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:39:39.733620Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e23ab249-6b8b-42cf-a9fb-a3a6071de66b · outbound

This paper cites Adversarial examples in the physical world,.

A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation Adversarial examples in the physical world,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:39:39.719650Z

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Observation b42d9889-007a-4c58-856b-c0559f244a40 · outbound

This paper cites Dropout as a Bayesian approximation: Representing model uncertainty in deep learning,.

A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation Dropout as a Bayesian approximation: Representing model uncertainty in deep learning,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:39:39.706624Z

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Observation ab3c49e0-443e-42c8-8d6f-6a62d4c0a955 · outbound

This paper cites Concrete dropout,.

A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation Concrete dropout,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:39:39.693969Z

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Observation bfbb39af-fc48-4b7f-8992-6bad3c1d6a9a · outbound

This paper cites Variational dropout and the local reparameterization trick,.

A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation Variational dropout and the local reparameterization trick,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:39:39.681127Z

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Observation 6a99925e-2b97-4d1f-a1ea-c8c2caf92ec8 · outbound

This paper cites Variational Gaussian Dropout is not Bayesian.

A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation Variational Gaussian Dropout is not Bayesian

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-14T12:39:39.031360Z

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Observation d3fc5942-a056-4e03-a73f-c25bf4745ee1 · outbound

This paper cites Variational Bayesian dropout: pitfalls and fixes.

A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation Variational Bayesian dropout: pitfalls and fixes

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-08-14T12:39:39.254664Z

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Observation 2e92bf12-2cf2-4f6c-9faf-eaa739a51e13 · outbound

This paper cites Botminer: Clustering analysis of network trafﬁc for protocol and structure independent Botnet detection,.

A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation Botminer: Clustering analysis of network trafﬁc for protocol and structure independent Botnet detection,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:39:39.668687Z

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Observation ffbf1448-c189-43fc-931e-de79ee81d71f · outbound

This paper cites Software-deﬁned networking-based ransomware detection using HTTP trafﬁc characteris- tics,.

A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation Software-deﬁned networking-based ransomware detection using HTTP trafﬁc characteris- tics,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:39:39.656154Z

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Observation 3d2b04f3-dd70-4a4d-8473-44b7281e53af · outbound

This paper cites Scalable, behavior-based malware clustering,.

A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation Scalable, behavior-based malware clustering,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:39:39.643178Z

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Observation 1f39472a-891b-4991-8998-962ae0fc07a1 · outbound

This paper cites JACKSTRAWS: Picking command and control connections from Bot trafﬁc,.

A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation JACKSTRAWS: Picking command and control connections from Bot trafﬁc,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:39:39.629250Z

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Observation 61c63d31-3ef2-4f2e-85d8-c8b1d37e6052 · outbound

This paper cites Heldroid: Fast and efﬁcient linguistic-based ransomware detection,.

A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation Heldroid: Fast and efﬁcient linguistic-based ransomware detection,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:39:39.614329Z

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Observation 04046f35-f5f7-4987-8265-eecbcec21e05 · outbound

This paper cites Cutting the gordian knot: A look under the hood of ransomware attacks,.

A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation Cutting the gordian knot: A look under the hood of ransomware attacks,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:39:39.600086Z

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Observation 107de4d6-abfc-4c66-a840-6ec8f79adda3 · outbound

This paper cites Cryptolock (and drop it): Stopping ransomware attacks on user data,.

A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation Cryptolock (and drop it): Stopping ransomware attacks on user data,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:39:39.586301Z

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Observation 5cfeedbe-efb3-4198-a781-952e293de4e9 · outbound

This paper cites Automated Dynamic Analysis of Ransomware: Benefits, Limitations and use for Detection.

A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation Automated Dynamic Analysis of Ransomware: Benefits, Limitations and use for Detection

Reference 28

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Observation f0b98535-e43d-4cd6-a22c-018bc1fe31ea · outbound

This paper cites Evaluating shallow and deep networks for ransomware detection and classiﬁcation,.

A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation Evaluating shallow and deep networks for ransomware detection and classiﬁcation,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:39:39.572335Z

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Observation 66d2c3f0-b2db-4cd8-b0c0-60e97fa441de · outbound

This paper cites Real-time monocular depth estimation using synthetic data with domain adaptation via image style transfer,.

A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation Real-time monocular depth estimation using synthetic data with domain adaptation via image style transfer,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:39:39.558664Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation aa6485b7-dc6a-4c5c-9e2f-b91878b5c34f · outbound

This paper cites Faster R-CNN: Towards real- time object detection with region proposal networks,.

A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation Faster R-CNN: Towards real- time object detection with region proposal networks,

Reference 31

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Observation c60180a3-f1fb-4152-90f7-e4cf6dcd6ac9 · outbound

This paper cites Veritatem Dies Aperit - temporally consistent depth prediction enabled by a multi-task geometric and semantic scene understanding approach,.

A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation Veritatem Dies Aperit - temporally consistent depth prediction enabled by a multi-task geometric and semantic scene understanding approach,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:39:39.535413Z

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Observation b8848b5e-b7f5-449b-839f-438ddf1fd080 · outbound

This paper cites Distributed representations of words and phrases and their composi- tionality,.

A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation Distributed representations of words and phrases and their composi- tionality,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:39:39.521417Z

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Observation 3e73ec52-4d83-4afe-9281-ba2afc1ac186 · outbound

This paper cites node2vec: Scalable feature learning for networks,.

A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation node2vec: Scalable feature learning for networks,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:39:39.508439Z

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Observation 610f0bb6-f34c-4214-9d45-c6ce9825e33c · outbound

This paper cites Temporal graph offset reconstruction: Towards tempo- rally robust graph representation learning,.

A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation Temporal graph offset reconstruction: Towards tempo- rally robust graph representation learning,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:39:39.494666Z

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Observation 8e8a106d-55e1-433c-925d-c9e962c2ed64 · outbound

This paper cites One-shot learning of object categories,.

A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation One-shot learning of object categories,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:39:39.479255Z

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Observation 3ac1c374-b925-402d-a690-4765abbd26dc · outbound

This paper cites One-shot learning of generative speech concepts,.

A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation One-shot learning of generative speech concepts,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:39:39.466381Z

Source-reported events for the cited work

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Observation a0d552d8-a077-4b25-b0a9-fe9d5906c423 · outbound

This paper cites Siamese neural networks for one-shot image recognition,.

A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation Siamese neural networks for one-shot image recognition,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:39:39.454115Z

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Observation 36e9663c-1b60-49ba-bd54-4bfd599a6dab · outbound

This paper cites One-shot Learning with Memory-Augmented Neural Networks.

A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation One-shot Learning with Memory-Augmented Neural Networks

Reference 39

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Observation d72d3d75-644e-401b-939c-8003bd2e0d4e · outbound

This paper cites Matching networks for one shot learning,.

A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation Matching networks for one shot learning,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:39:39.441602Z

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Observation f8665759-6323-45d8-bd6b-5bf59021c72c · outbound

This paper cites Multi- level semantic feature augmentation for one-shot learning,.

A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation Multi- level semantic feature augmentation for one-shot learning,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:39:39.429709Z

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Observation b4c2ef0b-9ae5-4d35-a4fd-872e9f9dec60 · outbound

This paper cites Data augmentation using learned transformations for one-shot medical image segmentation,.

A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation Data augmentation using learned transformations for one-shot medical image segmentation,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:39:39.417086Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c2799f6f-15b5-43d7-bdb2-897e4e3efd0b · outbound

This paper cites Dropout inference in Bayesian networks with alpha- divergences,.

A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation Dropout inference in Bayesian networks with alpha- divergences,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:39:39.403924Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e29f3a8a-d338-49c0-a287-32a4f632dadc · outbound

This paper cites Neural network based intrusion detection system for critical infrastructures,.

A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation Neural network based intrusion detection system for critical infrastructures,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:39:39.389917Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 51f6dc0d-5398-46e5-ba7a-c5961b9666f8 · outbound

This paper cites What uncertainties do we need in Bayesian deep learning for computer vision?.

A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation What uncertainties do we need in Bayesian deep learning for computer vision?

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:39:39.376350Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f8bca989-d789-48e6-8d66-93ebc49dea29 · outbound

This paper cites Dropout: A simple way to prevent neural networks from overﬁtting,.

A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation Dropout: A simple way to prevent neural networks from overﬁtting,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:39:39.361439Z

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Observation 5891346b-f864-4a08-a982-8f8feaec8d9a · outbound

This paper cites Deep Gaussian processes,.

A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation Deep Gaussian processes,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:39:39.348587Z

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Observation 3aa7bc20-cb6f-4de1-abea-990fe67619dd · outbound

This paper cites Automatic differentiation in PyTorch,.

A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation Automatic differentiation in PyTorch,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:39:39.334608Z

Source-reported events for the cited work

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Observation 3588ff1f-3a22-474e-9057-e8d11f2d667b · outbound

This paper cites Adam: A method for stochastic optimization,.

A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation Adam: A method for stochastic optimization,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:39:39.318912Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 5144f906-3c4f-494d-bd9c-e3f95987a44a · outbound

This paper cites Bayesian Layers: A Module for Neural Network Uncertainty.

A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation Bayesian Layers: A Module for Neural Network Uncertainty

Reference 50

Resolution: verified exact; local_arxiv, observed 2026-08-14T12:39:39.209871Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 9581a8cc-8260-4873-b616-5744420bd344 · outbound

This paper cites Optimizing over a Bayesian last layer,.

A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation Optimizing over a Bayesian last layer,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:39:39.305606Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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