SA-DRL: Security-Aware Deep Reinforcement Learning for Ransomware Detection with Asymmetric Reward Design | Paper Citation Record

Observation 95975069-648f-4670-bc1f-433c848e4cd3 · outbound

This paper cites ANY.RUNinteractivemalwarehuntingservice.

SA-DRL: Security-Aware Deep Reinforcement Learning for Ransomware Detection with Asymmetric Reward Design ANY.RUNinteractivemalwarehuntingservice

Reference 1

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Observation 00797d33-6cc7-4af4-9a27-9339d34e0364 · outbound

This paper cites Narrowed sights, bigger payoffs: Ransomware in 2019.

SA-DRL: Security-Aware Deep Reinforcement Learning for Ransomware Detection with Asymmetric Reward Design Narrowed sights, bigger payoffs: Ransomware in 2019

Reference 2

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Observation 9b475c73-f1c8-4956-abcd-85f84f41dbb6 · outbound

This paper cites MalwareBazaar database.

SA-DRL: Security-Aware Deep Reinforcement Learning for Ransomware Detection with Asymmetric Reward Design MalwareBazaar database

Reference 3

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Observation e08ff836-62df-4d97-9837-cd88a59aa883 · outbound

This paper cites The state of ransomware 2025.

SA-DRL: Security-Aware Deep Reinforcement Learning for Ransomware Detection with Asymmetric Reward Design The state of ransomware 2025

Reference 4

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Observation 1050e9ec-23f5-4c6a-8b4a-36d422f41b30 · outbound

This paper cites Adnan Alvi and Zunera Jalil.

SA-DRL: Security-Aware Deep Reinforcement Learning for Ransomware Detection with Asymmetric Reward Design Adnan Alvi and Zunera Jalil

Reference 5

Resolution: metadata mismatch; arxiv_id, observed 2026-07-09T23:56:38.347303Z

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Observation 99b8a2a4-a075-4b63-858f-c5bdcef29973 · outbound

This paper cites Deep q- learningbasedreinforcementlearningapproachfornetworkintrusion detection.

SA-DRL: Security-Aware Deep Reinforcement Learning for Ransomware Detection with Asymmetric Reward Design Deep q- learningbasedreinforcementlearningapproachfornetworkintrusion detection

Reference 6

Resolution: verified exact; doi, observed 2026-07-09T23:56:38.359589Z

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Observation bfe2988c-4ccf-476e-9c09-ad000bc52512 · outbound

This paper cites Machine learning-based static ran- somware detection using pe header features and shap interpretation.

SA-DRL: Security-Aware Deep Reinforcement Learning for Ransomware Detection with Asymmetric Reward Design Machine learning-based static ran- somware detection using pe header features and shap interpretation

Reference 7

Resolution: verified exact; doi, observed 2026-07-09T23:56:38.295711Z

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Observation c3083e12-86ca-4b1a-8d3b-bd1b051fe260 · outbound

This paper cites Reward shaping for hap- pier autonomous cyber security agents, Association for Computing Machinery, New York, NY, USA.

SA-DRL: Security-Aware Deep Reinforcement Learning for Ransomware Detection with Asymmetric Reward Design Reward shaping for hap- pier autonomous cyber security agents, Association for Computing Machinery, New York, NY, USA

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-07-09T23:56:38.333851Z

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Observation 03e15f84-9746-4ca8-9606-25b77303b5dd · outbound

This paper cites Computers & Security 150, 104293.

SA-DRL: Security-Aware Deep Reinforcement Learning for Ransomware Detection with Asymmetric Reward Design Computers & Security 150, 104293

Reference 9

Resolution: metadata mismatch; arxiv_id, observed 2026-07-09T23:56:38.320656Z

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Observation 80e6ad37-9760-4af2-9d9c-f38625784fc9 · outbound

This paper cites Castelluccio , keywords =.

SA-DRL: Security-Aware Deep Reinforcement Learning for Ransomware Detection with Asymmetric Reward Design Castelluccio , keywords =

Reference 10

Resolution: metadata mismatch; arxiv_id, observed 2026-07-09T23:56:38.305464Z

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Observation fe2201ce-3a2e-4765-ba41-3d5fc681245c · outbound

This paper cites Statistical comparisons of classifiers over multiple data sets.

SA-DRL: Security-Aware Deep Reinforcement Learning for Ransomware Detection with Asymmetric Reward Design Statistical comparisons of classifiers over multiple data sets

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T23:56:38.703437Z

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Observation 0ae6273a-47ca-4ebd-a0ad-cfeedd033bcd · outbound

This paper cites Ransomware early detection using deep reinforcement learning on portable executable header.

SA-DRL: Security-Aware Deep Reinforcement Learning for Ransomware Detection with Asymmetric Reward Design Ransomware early detection using deep reinforcement learning on portable executable header

Reference 12

Resolution: verified exact; doi, observed 2026-07-09T23:56:38.309882Z

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Observation 02da372c-19f9-4480-8b0f-4adf6f607e87 · outbound

This paper cites Privacy-aware machine unlearning with sisa for reinforcement learning-based ransomware detection.

SA-DRL: Security-Aware Deep Reinforcement Learning for Ransomware Detection with Asymmetric Reward Design Privacy-aware machine unlearning with sisa for reinforcement learning-based ransomware detection

Reference 13

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Observation 69e99466-1e44-4982-8f79-64b3c12cbb94 · outbound

This paper cites Anoveltech- nique for ransomware detection using image based dynamic features andtransferlearningtoaddressdatasetlimitations.

SA-DRL: Security-Aware Deep Reinforcement Learning for Ransomware Detection with Asymmetric Reward Design Anoveltech- nique for ransomware detection using image based dynamic features andtransferlearningtoaddressdatasetlimitations

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T23:56:38.713149Z

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Observation 8eac928f-7e0f-4f01-a905-1874b633eea0 · outbound

This paper cites an unresolved cited work.

SA-DRL: Security-Aware Deep Reinforcement Learning for Ransomware Detection with Asymmetric Reward Design Unresolved cited work

Reference 15

Resolution: verified exact; doi, observed 2026-07-09T23:56:38.338912Z

Source-reported events for the cited work

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Observation 4639274f-be9b-4a1d-8b5c-80a8ab942220 · outbound

This paper cites Tl-rl- fusionnet: An adaptive and efficient reinforcement learning-driven transfer learning framework for detecting evolving ransomware threats.

SA-DRL: Security-Aware Deep Reinforcement Learning for Ransomware Detection with Asymmetric Reward Design Tl-rl- fusionnet: An adaptive and efficient reinforcement learning-driven transfer learning framework for detecting evolving ransomware threats

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T23:56:38.736777Z

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Observation 2cec5ad5-43bf-48fe-9079-1a6aa2312737 · outbound

This paper cites AI - based Ransomware D etection: A Comprehensive Review.

SA-DRL: Security-Aware Deep Reinforcement Learning for Ransomware Detection with Asymmetric Reward Design AI - based Ransomware D etection: A Comprehensive Review

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-07-09T23:56:38.330844Z

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Observation 5e02c4db-dec3-4d4c-8956-6c13afed6c3b · outbound

This paper cites XRan: Explainable deep learning-based ransomware detection using dynamic analysis.

SA-DRL: Security-Aware Deep Reinforcement Learning for Ransomware Detection with Asymmetric Reward Design XRan: Explainable deep learning-based ransomware detection using dynamic analysis

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-07-09T23:56:38.317243Z

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Observation 4197d3c4-0c59-4bd3-ad59-1c9f4382a37f · outbound

This paper cites React-d3qn: resilientadaptiveconcept-drift-awareduelingdoubledeepq-network for robust edge-centric intrusion detection.

SA-DRL: Security-Aware Deep Reinforcement Learning for Ransomware Detection with Asymmetric Reward Design React-d3qn: resilientadaptiveconcept-drift-awareduelingdoubledeepq-network for robust edge-centric intrusion detection

Reference 19

Resolution: verified exact; doi, observed 2026-07-09T23:56:38.293428Z

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Observation 217e1b06-8ce3-4a39-9b8a-5a806824afe6 · outbound

This paper cites Ransomware behavioural analysis on windows platforms.

SA-DRL: Security-Aware Deep Reinforcement Learning for Ransomware Detection with Asymmetric Reward Design Ransomware behavioural analysis on windows platforms

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T23:56:38.742225Z

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Observation ec950525-81f3-4389-804c-dd23e861344d · outbound

This paper cites Dynamic feature dataset for ransomware detection using machine learning algorithms.

SA-DRL: Security-Aware Deep Reinforcement Learning for Ransomware Detection with Asymmetric Reward Design Dynamic feature dataset for ransomware detection using machine learning algorithms

Reference 21

Resolution: verified exact; doi, observed 2026-07-09T23:56:38.312076Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 0f72e87e-b49d-43d1-b031-72bad9477e03 · outbound

This paper cites an unresolved cited work.

SA-DRL: Security-Aware Deep Reinforcement Learning for Ransomware Detection with Asymmetric Reward Design Unresolved cited work

Reference 22

Resolution: unresolved; raw_fallback, observed 2026-07-09T23:56:38.723438Z

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Observation 70a4a607-bc42-483e-81bc-9db10b1119b5 · outbound

This paper cites IEEE Access 9, 138345–138351.

SA-DRL: Security-Aware Deep Reinforcement Learning for Ransomware Detection with Asymmetric Reward Design IEEE Access 9, 138345–138351

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-07-09T23:56:38.323477Z

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Observation b4b57a4b-d5bb-4d61-b6f7-beb6cbc20f3f · outbound

This paper cites Ransomware detection and family classification using fine-tuned bert and roberta models.

SA-DRL: Security-Aware Deep Reinforcement Learning for Ransomware Detection with Asymmetric Reward Design Ransomware detection and family classification using fine-tuned bert and roberta models

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-07-09T23:56:38.337001Z

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Observation 4ee39354-0582-481b-93cf-33fac4ef117f · outbound

This paper cites Two-stage ransomware detection using dynamic analysis and machine learning techniques.

SA-DRL: Security-Aware Deep Reinforcement Learning for Ransomware Detection with Asymmetric Reward Design Two-stage ransomware detection using dynamic analysis and machine learning techniques

Reference 25

Resolution: malformed identifier; raw_fallback, observed 2026-07-09T23:56:38.740457Z

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Observation 9d461e19-c8d7-4551-9668-9b2fc8ea5cb3 · outbound

This paper cites an unresolved cited work.

SA-DRL: Security-Aware Deep Reinforcement Learning for Ransomware Detection with Asymmetric Reward Design Unresolved cited work

Reference 26

Resolution: unresolved; raw_fallback, observed 2026-07-09T23:56:38.735034Z

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Observation a541db89-c13e-470e-ad34-ac5d4d8b26dd · outbound

This paper cites IEEE Access 12, 175473–175500.

SA-DRL: Security-Aware Deep Reinforcement Learning for Ransomware Detection with Asymmetric Reward Design IEEE Access 12, 175473–175500

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-07-09T23:56:38.355047Z

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Observation a3ccda04-8a08-490a-a16c-a48388a2a304 · outbound

This paper cites DikeDataset.

SA-DRL: Security-Aware Deep Reinforcement Learning for Ransomware Detection with Asymmetric Reward Design DikeDataset

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T23:56:38.730259Z

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Observation 70a7e81c-a66b-444a-ba3a-130f662822fe · outbound

This paper cites Beyond reinforcement learning for network security: A comprehen- sive survey and tutorial.

SA-DRL: Security-Aware Deep Reinforcement Learning for Ransomware Detection with Asymmetric Reward Design Beyond reinforcement learning for network security: A comprehen- sive survey and tutorial

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-07-09T23:56:38.357913Z

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Observation cf18bc80-6186-4547-9d66-0ae938e13d8e · outbound

This paper cites A digital dna sequencing engine for ransomware detection using machine learning.

SA-DRL: Security-Aware Deep Reinforcement Learning for Ransomware Detection with Asymmetric Reward Design A digital dna sequencing engine for ransomware detection using machine learning

Reference 30

Resolution: malformed identifier; arxiv_id, observed 2026-07-09T23:56:38.460688Z

Source-reported events for the cited work

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Observation 5b13a095-56fa-4392-9f5b-b24dd7cd626d · outbound

This paper cites Adversarial robustness of deep reinforcement learning-based intrusion detection.

SA-DRL: Security-Aware Deep Reinforcement Learning for Ransomware Detection with Asymmetric Reward Design Adversarial robustness of deep reinforcement learning-based intrusion detection

Reference 31

Resolution: verified exact; doi, observed 2026-07-09T23:56:38.325701Z

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Observation 41b920e3-a7c3-4d93-b897-c5015e2dfd98 · outbound

This paper cites Deep sarsa-based reinforcement learning approach for anomaly network intrusion detection system.

SA-DRL: Security-Aware Deep Reinforcement Learning for Ransomware Detection with Asymmetric Reward Design Deep sarsa-based reinforcement learning approach for anomaly network intrusion detection system

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T23:56:38.716680Z

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Observation dd1a19a0-cda8-405d-aba9-5f4dac93e8da · outbound

This paper cites Improving ransomware detection based on portable executable header using xception convolutional neural network.

SA-DRL: Security-Aware Deep Reinforcement Learning for Ransomware Detection with Asymmetric Reward Design Improving ransomware detection based on portable executable header using xception convolutional neural network

Reference 33

Resolution: metadata mismatch; arxiv_id, observed 2026-07-09T23:56:38.341631Z

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Observation bd8a8576-b9a5-4287-8abb-f777c34ea840 · outbound

This paper cites A comprehen- sive analysis combining structural features for detection of new ran- somware families.

SA-DRL: Security-Aware Deep Reinforcement Learning for Ransomware Detection with Asymmetric Reward Design A comprehen- sive analysis combining structural features for detection of new ran- somware families

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T23:56:38.718377Z

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Observation 0be4cf7b-3f8d-477e-8fe0-e5bbfddfbc80 · outbound

This paper cites Cybersecurity ventures 2025 ransomware report.

SA-DRL: Security-Aware Deep Reinforcement Learning for Ransomware Detection with Asymmetric Reward Design Cybersecurity ventures 2025 ransomware report

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T23:56:38.720087Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 3f14de51-7f3a-4fbf-9705-6106bb663e31 · outbound

This paper cites Limits of static analysis for malware detection, in: Twenty-third annual computer security applications conference (ACSAC 2007), IEEE.

SA-DRL: Security-Aware Deep Reinforcement Learning for Ransomware Detection with Asymmetric Reward Design Limits of static analysis for malware detection, in: Twenty-third annual computer security applications conference (ACSAC 2007), IEEE

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T23:56:38.743985Z

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Observation 6eb53fe7-78ff-4427-99c4-c94e80613089 · outbound

This paper cites A State-of-the- Art Survey on Ransomware Detection using Machine Learning and Deep Learning.

SA-DRL: Security-Aware Deep Reinforcement Learning for Ransomware Detection with Asymmetric Reward Design A State-of-the- Art Survey on Ransomware Detection using Machine Learning and Deep Learning

Reference 37

Resolution: malformed identifier; raw_fallback, observed 2026-07-09T23:56:38.714863Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 06ba4c1c-37ec-4157-8dac-93cf1f9c48d5 · outbound

This paper cites Deep reinforcement learning for cyber security.

SA-DRL: Security-Aware Deep Reinforcement Learning for Ransomware Detection with Asymmetric Reward Design Deep reinforcement learning for cyber security

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-07-09T23:56:38.344364Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d2feb9b7-dc29-4984-a81b-ef2963bf3f11 · outbound

This paper cites Ml- ran: A behavioural dataset for ransomware analysis and detec- tion.

SA-DRL: Security-Aware Deep Reinforcement Learning for Ransomware Detection with Asymmetric Reward Design Ml- ran: A behavioural dataset for ransomware analysis and detec- tion

Reference 39

Resolution: verified exact; arxiv_id, observed 2026-07-09T23:56:38.350504Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c06ba0c0-a4cc-4d5c-8c55-1d99587aa67f · outbound

This paper cites Selcuk Ulu- agac.

SA-DRL: Security-Aware Deep Reinforcement Learning for Ransomware Detection with Asymmetric Reward Design Selcuk Ulu- agac

Reference 40

Resolution: metadata mismatch; doi, observed 2026-07-09T23:56:38.327832Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 17ab7e40-63c6-4902-adb0-e3b005d8d438 · outbound

This paper cites Unit 42 ransomware threat report.

SA-DRL: Security-Aware Deep Reinforcement Learning for Ransomware Detection with Asymmetric Reward Design Unit 42 ransomware threat report

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T23:56:38.738670Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation dcce97c4-efe8-4e6a-b602-55621303f027 · outbound

This paper cites Portable app directory.

SA-DRL: Security-Aware Deep Reinforcement Learning for Ransomware Detection with Asymmetric Reward Design Portable app directory

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T23:56:38.706661Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c1acccc8-9ebf-4f39-89d3-9e4f8ae09acc · outbound

This paper cites Automateddynamicanalysisofransomware:Benefits,limitationsand use for detection.

SA-DRL: Security-Aware Deep Reinforcement Learning for Ransomware Detection with Asymmetric Reward Design Automateddynamicanalysisofransomware:Benefits,limitationsand use for detection

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T23:56:38.745732Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 6e692a3b-e4f2-4e24-b741-ad0dae22a4d2 · outbound

This paper cites an unresolved cited work.

SA-DRL: Security-Aware Deep Reinforcement Learning for Ransomware Detection with Asymmetric Reward Design Unresolved cited work

Reference 44

Resolution: unresolved; raw_fallback, observed 2026-07-09T23:56:38.721908Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 2b2ff43b-fd63-457d-b29c-7a7a048626e2 · outbound

This paper cites IEEE Access8, 7925–7936 (2020) https://doi.org/10.1109/ACCESS.2020.

SA-DRL: Security-Aware Deep Reinforcement Learning for Ransomware Detection with Asymmetric Reward Design IEEE Access8, 7925–7936 (2020) https://doi.org/10.1109/ACCESS.2020

Reference 45

Resolution: verified exact; doi, observed 2026-07-09T23:56:38.307628Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 40649971-2dc2-4c12-b5e0-c2ef9c4867b6 · outbound

This paper cites What is cuckoo sandbox? URL:https:// cuckoosandbox.org/about.html.

SA-DRL: Security-Aware Deep Reinforcement Learning for Ransomware Detection with Asymmetric Reward Design What is cuckoo sandbox? URL:https:// cuckoosandbox.org/about.html

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T23:56:38.731956Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ef610340-6bba-4c78-81ed-23d8b13e1c31 · outbound

This paper cites Popular freeware categories.

SA-DRL: Security-Aware Deep Reinforcement Learning for Ransomware Detection with Asymmetric Reward Design Popular freeware categories

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T23:56:38.724985Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 6b38a2e8-175b-464f-988f-62d063b63a15 · outbound

This paper cites Maturing criminal marketplaces present new challenges to defenders.

SA-DRL: Security-Aware Deep Reinforcement Learning for Ransomware Detection with Asymmetric Reward Design Maturing criminal marketplaces present new challenges to defenders

Reference 48

Resolution: verified exact; arxiv_id, observed 2026-07-09T23:56:38.458198Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8118d2e6-c0dc-4314-b170-dbfcbf8465f4 · outbound

This paper cites The Ransomware Threat Landscape.

SA-DRL: Security-Aware Deep Reinforcement Learning for Ransomware Detection with Asymmetric Reward Design The Ransomware Threat Landscape

Reference 49

Resolution: malformed identifier; raw_fallback, observed 2026-07-09T23:56:38.747286Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation fd00b8ba-ed3e-44e3-ac84-29fcd58808d7 · outbound

This paper cites Phobos emerges as a formidable threat in Q1 2024, LockBit stays in the top spot.

SA-DRL: Security-Aware Deep Reinforcement Learning for Ransomware Detection with Asymmetric Reward Design Phobos emerges as a formidable threat in Q1 2024, LockBit stays in the top spot

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T23:56:38.699891Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b6e82b3b-f516-4d23-b3e9-b5198afbe474 · outbound

This paper cites 2025 Data Breach Inves- tigations Report.

SA-DRL: Security-Aware Deep Reinforcement Learning for Ransomware Detection with Asymmetric Reward Design 2025 Data Breach Inves- tigations Report

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T23:56:38.705103Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 2776f483-c7e1-43cb-9ca3-6a30b580193f · outbound

This paper cites VirusShare.com — because sharing is caring.

SA-DRL: Security-Aware Deep Reinforcement Learning for Ransomware Detection with Asymmetric Reward Design VirusShare.com — because sharing is caring

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T23:56:38.728529Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f500c1de-84f1-449c-9a1f-009b82db451e · outbound

This paper cites Ransomware detection using deep learning based unsupervised feature extr action and a cost sensitive Pareto Ensemble classifier.

SA-DRL: Security-Aware Deep Reinforcement Learning for Ransomware Detection with Asymmetric Reward Design Ransomware detection using deep learning based unsupervised feature extr action and a cost sensitive Pareto Ensemble classifier

Reference 53

Resolution: verified exact; doi, observed 2026-07-09T23:56:38.352461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b1e567e6-2da6-4e67-8ee2-3f55d3f9f5c8 · outbound

This paper cites Classification of ransomware families with machine learning based onn-gram of opcodes.

SA-DRL: Security-Aware Deep Reinforcement Learning for Ransomware Detection with Asymmetric Reward Design Classification of ransomware families with machine learning based onn-gram of opcodes

Reference 54

Resolution: verified exact; doi, observed 2026-07-09T23:56:38.314213Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1e64c4fc-73c7-469b-9140-ee58fcb68ea7 · outbound

This paper cites Information Processing & Management , volume=.

SA-DRL: Security-Aware Deep Reinforcement Learning for Ransomware Detection with Asymmetric Reward Design Information Processing & Management , volume=

Reference 55

Resolution: metadata mismatch; arxiv_id, observed 2026-07-09T23:56:38.299323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8c401d91-2750-4150-8f61-c0c570e2074f · outbound

This paper cites Zscaler: 2022 ThreatLabz state of ransomware re- port.

SA-DRL: Security-Aware Deep Reinforcement Learning for Ransomware Detection with Asymmetric Reward Design Zscaler: 2022 ThreatLabz state of ransomware re- port

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T23:56:38.726615Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers