A Self-Explainable Deep Architecture for Security Applications | Paper Citation Record

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A Self-Explainable Deep Architecture for Security Applications Unresolved cited work

Reference 1

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This paper cites Jaakkola.

A Self-Explainable Deep Architecture for Security Applications Jaakkola

Reference 2

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This paper cites Tabnet: Attentive interpretable tabular learning.

A Self-Explainable Deep Architecture for Security Applications Tabnet: Attentive interpretable tabular learning

Reference 3

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This paper cites Sok: Modeling explainability in security analytics for interpretability, trustworthiness, and usability.

A Self-Explainable Deep Architecture for Security Applications Sok: Modeling explainability in security analytics for interpretability, trustworthiness, and usability

Reference 4

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A Self-Explainable Deep Architecture for Security Applications Unresolved cited work

Reference 5

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This paper cites This looks like that: Deep learning for interpretable image recognition.

A Self-Explainable Deep Architecture for Security Applications This looks like that: Deep learning for interpretable image recognition

Reference 6

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This paper cites Yong, and Wei King Tiong.

A Self-Explainable Deep Architecture for Security Applications Yong, and Wei King Tiong

Reference 7

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This paper cites Explanations can be manipulated and geometry is to blame.

A Self-Explainable Deep Architecture for Security Applications Explanations can be manipulated and geometry is to blame

Reference 8

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This paper cites Concept embedding models: Beyond the accuracy-explainability trade-off.Advances in Neural Information Processing Systems, 2022.

A Self-Explainable Deep Architecture for Security Applications Concept embedding models: Beyond the accuracy-explainability trade-off.Advances in Neural Information Processing Systems, 2022

Reference 9

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This paper cites Tabcbm: Concept-based interpretable neural networks for tabu- lar data.Transactions on Machine Learning Research, 2023.

A Self-Explainable Deep Architecture for Security Applications Tabcbm: Concept-based interpretable neural networks for tabu- lar data.Transactions on Machine Learning Research, 2023

Reference 10

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This paper cites A lightweight, efficient and explainable-by-design convo- lutional neural network for internet traffic classification.

A Self-Explainable Deep Architecture for Security Applications A lightweight, efficient and explainable-by-design convo- lutional neural network for internet traffic classification

Reference 11

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Observation 6c574f55-0f78-4f8b-aa9b-88ecadf82d4f · outbound

This paper cites Fong and Andrea Vedaldi.

A Self-Explainable Deep Architecture for Security Applications Fong and Andrea Vedaldi

Reference 12

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Observation 1adc5231-5ff0-4a44-b045-40257d4d81e3 · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

A Self-Explainable Deep Architecture for Security Applications Explaining and Harnessing Adversarial Examples

Reference 13

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This paper cites right to explanation.

A Self-Explainable Deep Architecture for Security Applications right to explanation

Reference 14

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A Self-Explainable Deep Architecture for Security Applications Unresolved cited work

Reference 15

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This paper cites Lemna: Explaining deep learn- ing based security applications.

A Self-Explainable Deep Architecture for Security Applications Lemna: Explaining deep learn- ing based security applications

Reference 16

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This paper cites Lemna: Explaining deep learn- ing based security applications.

A Self-Explainable Deep Architecture for Security Applications Lemna: Explaining deep learn- ing based security applications

Reference 17

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This paper cites Evaluation and improvement of interpretability for self- explainable part-prototype networks.

A Self-Explainable Deep Architecture for Security Applications Evaluation and improvement of interpretability for self- explainable part-prototype networks

Reference 18

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This paper cites An interpretable prototype parts-based neural network for medical tabular data.

A Self-Explainable Deep Architecture for Security Applications An interpretable prototype parts-based neural network for medical tabular data

Reference 19

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This paper cites Protogate: Prototype-based neural networks with global-to-local feature selection for tab- ular biomedical data.

A Self-Explainable Deep Architecture for Security Applications Protogate: Prototype-based neural networks with global-to-local feature selection for tab- ular biomedical data

Reference 20

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This paper cites Kingma and Jimmy Ba.

A Self-Explainable Deep Architecture for Security Applications Kingma and Jimmy Ba

Reference 21

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This paper cites Pantypes: Diverse representatives for self- explainable models.

A Self-Explainable Deep Architecture for Security Applications Pantypes: Diverse representatives for self- explainable models

Reference 22

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This paper cites Concept bottleneck models.

A Self-Explainable Deep Architecture for Security Applications Concept bottleneck models

Reference 23

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This paper cites Howard, Wayne Hubbard, and Lawrence Jackel.

A Self-Explainable Deep Architecture for Security Applications Howard, Wayne Hubbard, and Lawrence Jackel

Reference 24

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This paper cites Deep learning for case-based reasoning through proto- types: A neural network that explains its predictions.

A Self-Explainable Deep Architecture for Security Applications Deep learning for case-based reasoning through proto- types: A neural network that explains its predictions

Reference 25

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This paper cites Shap: Shapley addi- tive explanations.

A Self-Explainable Deep Architecture for Security Applications Shap: Shapley addi- tive explanations

Reference 26

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This paper cites Lundberg and Su-In Lee.

A Self-Explainable Deep Architecture for Security Applications Lundberg and Su-In Lee

Reference 27

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This paper cites Towards deep learning models resistant to adversarial attacks.

A Self-Explainable Deep Architecture for Security Applications Towards deep learning models resistant to adversarial attacks

Reference 28

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This paper cites Explainable artificial intelligence: a compre- hensive review.Artificial Intelligence Review, 2022.

A Self-Explainable Deep Architecture for Security Applications Explainable artificial intelligence: a compre- hensive review.Artificial Intelligence Review, 2022

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:04:57.758876Z

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This paper cites Security is not my field, i’m a stats guy: A qualitative root cause analysis of barriers to adversarial machine learning defenses in industry.

A Self-Explainable Deep Architecture for Security Applications Security is not my field, i’m a stats guy: A qualitative root cause analysis of barriers to adversarial machine learning defenses in industry

Reference 30

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This paper cites Big data analytics for intrusion detection system: Statistical decision-making using finite dirichlet mixture models.

A Self-Explainable Deep Architecture for Security Applications Big data analytics for intrusion detection system: Statistical decision-making using finite dirichlet mixture models

Reference 31

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This paper cites Unsw-nb15: a compre- hensive dataset for network intrusion detection systems (unsw-nb15 network dataset).

A Self-Explainable Deep Architecture for Security Applications Unsw-nb15: a compre- hensive dataset for network intrusion detection systems (unsw-nb15 network dataset)

Reference 32

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This paper cites The evaluation of network anomaly detection systems: Statistical analysis of the unsw-nb15 dataset and the comparison with the kdd99 dataset.

A Self-Explainable Deep Architecture for Security Applications The evaluation of network anomaly detection systems: Statistical analysis of the unsw-nb15 dataset and the comparison with the kdd99 dataset

Reference 33

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This paper cites Novel geometric area analysis technique for anomaly detec- tion using trapezoidal area estimation on large-scale networks.

A Self-Explainable Deep Architecture for Security Applications Novel geometric area analysis technique for anomaly detec- tion using trapezoidal area estimation on large-scale networks

Reference 34

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This paper cites Neu- ral prototype trees for interpretable fine-grained image recognition.

A Self-Explainable Deep Architecture for Security Applications Neu- ral prototype trees for interpretable fine-grained image recognition

Reference 35

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This paper cites Pytorch: an imperative style, high-performance deep learning library.

A Self-Explainable Deep Architecture for Security Applications Pytorch: an imperative style, high-performance deep learning library

Reference 36

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This paper cites Captum: Model in- terpretability for pytorch.

A Self-Explainable Deep Architecture for Security Applications Captum: Model in- terpretability for pytorch

Reference 37

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Observation 4d974ff9-e40a-43b1-b177-36263ece6e62 · outbound

This paper cites Lime: Local interpretable model-agnostic explanations.

A Self-Explainable Deep Architecture for Security Applications Lime: Local interpretable model-agnostic explanations

Reference 38

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This paper cites why should i trust you?.

A Self-Explainable Deep Architecture for Security Applications why should i trust you?

Reference 39

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This paper cites Interpretable image classification with differ- entiable prototypes assignment.

A Self-Explainable Deep Architecture for Security Applications Interpretable image classification with differ- entiable prototypes assignment

Reference 40

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This paper cites Protopshare: Prototypical parts shar- ing for similarity discovery in interpretable image clas- sification.

A Self-Explainable Deep Architecture for Security Applications Protopshare: Prototypical parts shar- ing for similarity discovery in interpretable image clas- sification

Reference 41

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Observation db5bf0a5-cdef-41a2-be57-b24afe077b9b · outbound

This paper cites Evaluating the visualization of what a deep neural net- work has learned.IEEE Transactions on Neural Net- works and Learning Systems, 2017.

A Self-Explainable Deep Architecture for Security Applications Evaluating the visualization of what a deep neural net- work has learned.IEEE Transactions on Neural Net- works and Learning Systems, 2017

Reference 42

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This paper cites Linearly-Interpretable Concept Embedding Models for Text Analysis.

A Self-Explainable Deep Architecture for Security Applications Linearly-Interpretable Concept Embedding Models for Text Analysis

Reference 43

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This paper cites Netflow datasets for machine learning-based network intrusion detection systems.

A Self-Explainable Deep Architecture for Security Applications Netflow datasets for machine learning-based network intrusion detection systems

Reference 44

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This paper cites Towards a standard feature set for network intru- sion detection system datasets.

A Self-Explainable Deep Architecture for Security Applications Towards a standard feature set for network intru- sion detection system datasets

Reference 45

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Observation 096ccf3b-8bc5-48f2-bf78-3b3fe87ee8f4 · outbound

This paper cites Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, et al.

A Self-Explainable Deep Architecture for Security Applications Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, et al

Reference 46

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This paper cites On the robustness of domain constraints.

A Self-Explainable Deep Architecture for Security Applications On the robustness of domain constraints

Reference 47

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This paper cites Krishnan.

A Self-Explainable Deep Architecture for Security Applications Krishnan

Reference 48

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Observation d62e46b5-e8b4-4448-8ab4-c2ae95cb244f · outbound

This paper cites Deep inside convolutional networks: Visualising image classification models and saliency maps.

A Self-Explainable Deep Architecture for Security Applications Deep inside convolutional networks: Visualising image classification models and saliency maps

Reference 49

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Observation 42b485bd-c04b-4e58-b55f-9cb6042a8d80 · outbound

This paper cites Fooling lime and shap: Ad- versarial attacks on post hoc explanation methods.

A Self-Explainable Deep Architecture for Security Applications Fooling lime and shap: Ad- versarial attacks on post hoc explanation methods

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:04:57.581969Z

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Observation dc581b8b-a3c9-45ab-8de9-cfee8abcc30b · outbound

This paper cites SmoothGrad: removing noise by adding noise.

A Self-Explainable Deep Architecture for Security Applications SmoothGrad: removing noise by adding noise

Reference 51

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This paper cites Malicious pdf de- tection using metadata and structural features.

A Self-Explainable Deep Architecture for Security Applications Malicious pdf de- tection using metadata and structural features

Reference 52

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Observation e7ccc99c-eb42-470e-9332-db8db238fb50 · outbound

This paper cites Striving for simplicity: The all convolutional net.

A Self-Explainable Deep Architecture for Security Applications Striving for simplicity: The all convolutional net

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:04:57.564843Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3cdbe187-6f71-4b3d-a1e5-a9451edae707 · outbound

This paper cites Practical evasion of a learning-based classifier: A case study.

A Self-Explainable Deep Architecture for Security Applications Practical evasion of a learning-based classifier: A case study

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:04:57.555393Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 8c9241d9-8a0d-4afc-99ea-a217cdfae5d7 · outbound

This paper cites Ax- iomatic attribution for deep networks.

A Self-Explainable Deep Architecture for Security Applications Ax- iomatic attribution for deep networks

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:04:57.546408Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 8bbee272-8e37-4e62-ab52-f95ac7aea42e · outbound

This paper cites Ghorbani.

A Self-Explainable Deep Architecture for Security Applications Ghorbani

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:04:57.537546Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ed28e061-255d-4045-bc77-3330e983317e · outbound

This paper cites Attention is all you need.

A Self-Explainable Deep Architecture for Security Applications Attention is all you need

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:04:57.528144Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0d092c81-5c2a-4835-a001-44d863f8a03b · outbound

This paper cites Interpretable image recognition by constructing trans- parent embedding space.

A Self-Explainable Deep Architecture for Security Applications Interpretable image recognition by constructing trans- parent embedding space

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:04:57.519026Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4bc84f9c-1fc7-4031-9675-ae6425f933cc · outbound

This paper cites Evaluating explanation methods for deep learning in security.

A Self-Explainable Deep Architecture for Security Applications Evaluating explanation methods for deep learning in security

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:04:57.509884Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d8a14bc8-5b56-4515-8590-980c3a206c62 · outbound

This paper cites xnids: Explaining deep learning-based network intrusion detection systems for active intrusion responses.

A Self-Explainable Deep Architecture for Security Applications xnids: Explaining deep learning-based network intrusion detection systems for active intrusion responses

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:04:57.500033Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 41657f18-1ced-4a13-bb55-8f9dfcdd5504 · outbound

This paper cites xnids: Explaining deep learning-based network intru- sion detection systems for active intrusion responses.

A Self-Explainable Deep Architecture for Security Applications xnids: Explaining deep learning-based network intru- sion detection systems for active intrusion responses

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:04:57.488504Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d9e22bcf-2f28-4a5a-9885-572fec2bb55f · outbound

This paper cites Bodmas: An open dataset for learning based temporal analysis of pe malware.

A Self-Explainable Deep Architecture for Security Applications Bodmas: An open dataset for learning based temporal analysis of pe malware

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:04:57.477345Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 65d94b80-2b08-47e4-8d87-c7ae2f8aff6d · outbound

This paper cites Cade: Detecting and ex- plaining concept drift samples for security applications.

A Self-Explainable Deep Architecture for Security Applications Cade: Detecting and ex- plaining concept drift samples for security applications

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:04:57.466852Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 8710e027-a1bb-470a-8fd7-a7c4c4c319af · outbound

This paper cites an unresolved cited work.

A Self-Explainable Deep Architecture for Security Applications Unresolved cited work

Reference 64

Resolution: unresolved; raw_fallback, observed 2026-08-08T11:04:57.457356Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 93144c59-e18a-4c03-ae76-3adc49342606 · outbound

This paper cites Concept embedding models: Beyond the accuracy- explainability trade-off.

A Self-Explainable Deep Architecture for Security Applications Concept embedding models: Beyond the accuracy- explainability trade-off

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:04:57.448169Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1a364c20-a404-422e-a85e-dbd78cefa638 · outbound

This paper cites Zeiler and Rob Fergus.

A Self-Explainable Deep Architecture for Security Applications Zeiler and Rob Fergus

Reference 66

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Source-reported events for the cited work

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Observation 591913a8-ab16-4798-8fdd-518a13fe8013 · outbound

This paper cites Interpretable deep learning under fire.

A Self-Explainable Deep Architecture for Security Applications Interpretable deep learning under fire

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:04:57.433402Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 878f1864-b015-42f3-9cab-02da38a7d673 · outbound

This paper cites Zintgraf, Taco S.

A Self-Explainable Deep Architecture for Security Applications Zintgraf, Taco S

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:04:57.422044Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4c74fcdd-ebb6-475b-b679-6d2d34860519 · outbound

This paper cites an unresolved cited work.

A Self-Explainable Deep Architecture for Security Applications Unresolved cited work

Reference 2016

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Reference resolution

Outbound references

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