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Volume 18, Issue 2
  • ISSN: 2666-2558
  • E-ISSN: 2666-2566

Abstract

Ransomware, a form of malicious software originating from cryptovirology, poses a serious threat by coercing victims to pay a ransom under the risk of exposing their data or permanently restricting access. While basic ransomware may lock a system without causing harm to files, more sophisticated variants utilize cryptoviral extortion techniques. The danger of ransomware is significant, with ongoing discoveries of new strains and families on the internet and dark web. Recovering from ransomware infections is challenging due to the complex encryption schemes employed. The exploration of machine learning and deep learning methods for ransomware detection is crucial, as these technologies can identify zero-day threats. This survey delves into research contributions on the detection of ransomware using deep learning algorithms. With deep learning gaining prominence in cybersecurity, we aimed to explore techniques for ransomware detection, assess weaknesses in existing deep learning approaches, and propose enhancements using those deep learning algorithms. Machine learning algorithms can be employed to tackle worldwide computer security challenges, encompassing the detection of malware, recognition of ransomware, detection of fraud, and identification of spoofing attempts. Machine learning algorithms play a crucial role in assessing prevalent forms of cyber security risks. They are instrumental in identifying and mitigating attacks, conducting vulnerability scans, and evaluating the risks associated with the public internet. By leveraging machine learning, computer defense mechanisms can effectively identify and respond to various cyber threats. These techniques aid in fortifying systems against potential vulnerabilities and enhance the overall security posture. Research in this field investigates the utilization of cyber training in both defensive and offensive contexts, offering insights into the intersection of cyber threats and machine learning techniques.

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References

  1. JadeyS. Introduction to cyber security.Methods, Implementation, and Application of Cyber Security Intelligence and Analytics. PrakashJ.O. Hershey, PAIGI Global202212410.4018/978‑1‑6684‑3991‑3.ch001
     [Google Scholar]
  2. GoreR. PadillaJ. DialloS. Markov chain modeling of cyber threats.JDMS201714323324410.1177/1548512916683451
     [Google Scholar]
  3. HuangK. SiegelM. MadnickS. Systematically understanding the cyber attack business: A survey.ACM Comput. Surv.201951413610.1145/3199674
     [Google Scholar]
  4. AnghelMihail RacautanuAndrei A note on different types of ransomware attacks.Cryptology ePrint Archive2019
     [Google Scholar]
  5. AroraL. ThakurN. YadavS.K. USB rubber ducky detection by using heuristic rules.2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)19-20 February 2021, Greater Noida, India, pp.156-160, 2021.10.1109/ICCCIS51004.2021.9397064
     [Google Scholar]
  6. FernandoD.W. KomninosN. ChenT. A study on the evolution of ransomware detection using machine learning and deep learning techniques.IoT20201255160410.3390/iot1020030
     [Google Scholar]
  7. YaminM.M. KattB. SattarK. AhmadM.B. Implementation of insider threat detection system using honeypot based sensors and threat analytics.Proceedings of the 2019 Future of Information and Communication Conference (FICC)801829202010.1007/978‑3‑030‑12385‑7_56
     [Google Scholar]
  8. ReidysB. LiuP. HuangJ. RSSD: Defend against ransomware with hardware-isolated network-storage codesign and post-attack analysis.Proceedings of the 27th ACM International Conference on Architectural Support for Programming Languages and Operating Systemspp.726-739, 2022.10.1145/3503222.3507773
     [Google Scholar]
  9. AboaojaF. A. ZainalA. GhalebF. A. Toward an ensemble behavioral-based early evasive malware detection framework.2021 International Conference on Data Science and Its Applications (ICoDSA)06-07 October 2021, Bandung, Indonesia, pp.181-186, 2021.
     [Google Scholar]
  10. SharmaB. PokharelP. JoshiB. User behavior analytics for anomaly detection using LSTM autoencoder-insider threat detection.Proceedings of the 11th International Conference on Advances in Information Technologypp. 1-9, 2020.10.1145/3406601.3406610
     [Google Scholar]
  11. PerezJ.E. An examination of cyber reasoning vs. human reasoning.PhD dissertation, Utica College2021
     [Google Scholar]
  12. O’ShaughnessyS. SheridanS. Image-based malware classification hybrid framework based on space-filling curves.Comput. Secur.202211610266010.1016/j.cose.2022.102660
     [Google Scholar]
  13. RiosG. Machine learning for detecting ransomware attacks using BGP routing records.Thesis M.A.Sc. Simon Fraser University2022
     [Google Scholar]
  14. DongS. WangP. AbbasK. A survey on deep learning and its applications.Comput. Sci. Rev.20214010037910.1016/j.cosrev.2021.100379
     [Google Scholar]
  15. YuK. TanL. MumtazS. Al-RubayeS. Al-DulaimiA. BashirA.K. KhanF.A. Securing critical infrastructures: Deep-learning-based threat detection in IIoT.IEEE Commun. Mag.20215910768210.1109/MCOM.101.2001126
     [Google Scholar]
  16. AdnanM. HabibA. AshrafJ. MussadiqS. RazaA.A. AbidM. BashirM. KhanS.U. Predicting at-risk students at different percentages of course length for early intervention using machine learning models.IEEE Access202197519753910.1109/ACCESS.2021.3049446
     [Google Scholar]
  17. MahindruA. SangalA.L. SemiDroid: A behavioral malware detector based on unsupervised machine learning techniques using feature selection approaches.Int. J. Mach. Learn. Cybern.20211251369141110.1007/s13042‑020‑01238‑9
     [Google Scholar]
  18. HassanN.A. HassanN.A. Enterprise defense strategies against ransomware attacks: Protection against ransomware attacks on corporate environment.Ransomware RevealedApressBerkeley, CA201911515410.1007/978‑1‑4842‑4255‑1_5
     [Google Scholar]
  19. AlwashaliA.A.M.A. NorA.A.R. IsmailN. A survey of Ransomware as a Service (RaaS) and methods to mitigate the attack.2021 14th International Conference on Developments in eSystems Engineering (DeSE)07-10 December 2021, Sharjah, United Arab Emirates, 2021.10.1109/DeSE54285.2021.9719456
     [Google Scholar]
  20. ZhengQ. ZhaoP. LiY. WangH. YangY. Spectrum interference-based two-level data augmentation method in deep learning for automatic modulation classification.Neural Comput. Appl.202133137723774510.1007/s00521‑020‑05514‑1
     [Google Scholar]
  21. ZhengQ. ZhaoP. WangH. ElhanashiA. SaponaraS. Fine-grained modulation classification using multi-scale radio transformer with dual-channel representation.IEEE Commun. Lett.20222661298130210.1109/LCOMM.2022.3145647
     [Google Scholar]
  22. LiB. RoundyK. GatesC. VorobeychikY. Large-scale identification of malicious singleton files.CODASPY '17: Proceedings of the Seventh ACM on Conference on Data and Application Security and PrivacyMarch 2017, pp.227–238, 2017.10.1145/3029806.3029815
     [Google Scholar]
  23. AlazabM. VenkataramanS. WattersP. Towards understanding malware behaviour by the extraction of API calls.2010 Second Cybercrime and Trustworthy Computing Workshop19-20 July 2010, Ballarat, VIC, Australia, pp.52-59, 2010.10.1109/CTC.2010.8
     [Google Scholar]
  24. TangM. AlazabM. LuoY. Big data for cybersecurity: Vulnerability disclosure trends and dependencies.IEEE Trans. Big Data20195331732910.1109/TBDATA.2017.2723570
     [Google Scholar]
  25. VinayakumarR. AlazabM. SomanK.P. PoornachandranP. VenkatramanS. Robust intelligent malware detection using deep learning.IEEE Access20197467174673810.1109/ACCESS.2019.2906934
     [Google Scholar]
  26. KimT. KangB. RhoM. ImE.G. A multimodal deep learning method for android malware detection using various features.IEEE Trans. Inf. Forensics Security2018143773788
     [Google Scholar]
  27. HudaS. AbawajyJ. AlazabM. AbdollalihianM. IslamR. YearwoodJ. Hybrids of support vector machine wrapper and filter based framework for malware detection.Future Gener. Comput. Syst.20165537639010.1016/j.future.2014.06.001
     [Google Scholar]
  28. SharmeenS. AhmedY.A. HudaS. KoçerB.S. HassanM.M. Avoiding future digital extortion through robust protection against ransomware threats using deep learning based adaptive approaches.IEEE Access20208245222453410.1109/ACCESS.2020.2970466
     [Google Scholar]
  29. RaffE. SylvesterJ. NicholasC. Learning the PE header, malware detection with minimal domain knowledge.arXiv:1709.01471201710.1145/3128572.3140442
     [Google Scholar]
  30. PontJ.J. Identifying Ransomware Through Statistical and Behavioural Analysis.PhD thesis; University of Kent2023
     [Google Scholar]
  31. CasinoF. ChooK.K.R. PatsakisC. HEDGE: Efficient traffic classification of encrypted and compressed packets.IEEE Trans. Inf. Forensics Security201914112916292610.1109/TIFS.2019.2911156
     [Google Scholar]
  32. HartS. MargheriA. PaciF. SassoneV. Riskio: A serious game for cyber security awareness and education.Comput. Secur.20209510182710.1016/j.cose.2020.101827
     [Google Scholar]
  33. LiuP. ChenW. WangZ. FuL. Prudent practices for designing virtual desktop experiments.The 31st International Conference on Software Engineering and Knowledge Engineering201910.18293/SEKE2019‑086
     [Google Scholar]
  34. RhodeM. BurnapP. JonesK. Early-stage malware prediction using recurrent neural networks.Comput. Secur.20187757859410.1016/j.cose.2018.05.010
     [Google Scholar]
  35. DemetrioL. CoullS.E. BiggioB. LagorioG. ArmandoA. RoliF. Adversarial EXEmples: A survey and experimental evaluation of practical attacks on machine learning for windows malware detection.ACM Trans. Priv. Secur.202124413110.1145/3473039
     [Google Scholar]
  36. VermaR. Security analytics: Adapting data science for security challenges.Proceedings of the fourth ACM international workshop on security and privacy analytics4041201810.1145/3180445.3180456
     [Google Scholar]
  37. El-KomyA. ShahinO.R. Abd El-AzizR.M. TalobaA.I. Integration of computer vision and natural language processing in multimedia robotics application.Inf. Sci.202276
     [Google Scholar]
  38. VenkatramanS. AlazabM. VinayakumarR. A hybrid deep learning image-based analysis for effective malware detection.J. Inf. Secur. Appl.20194737738910.1016/j.jisa.2019.06.006
     [Google Scholar]
  39. MosaviA. ArdabiliS. KoczyV.A.R. List of deep learning models.Preprints202010.1007/978‑3‑030‑36841‑8_20
     [Google Scholar]
  40. ZhangY.D. SatapathyS.C. GutteryD.S. GórrizJ.M. WangS.H. Improved breast cancer classification through combining graph convolutional network and convolutional neural network.Inf. Process. Manage.202158210243910.1016/j.ipm.2020.102439
     [Google Scholar]
  41. RoyS.K. KrishnaG. DubeyS.R. ChaudhuriB.B. HybridSN: Exploring 3-D–2-D CNN feature hierarchy for hyperspectral image classification.IEEE Geosci. Remote Sens. Lett.202017227728110.1109/LGRS.2019.2918719
     [Google Scholar]
  42. DixitP. SilakariS. Deep learning algorithms for cybersecurity applications: A technological and status review.Comput. Sci. Rev.20213910031710.1016/j.cosrev.2020.100317
     [Google Scholar]
  43. NedeljkovicD. JakovljevicZ. CNN based method for the development of cyber-attacks detection algorithms in industrial control systems.Comput. Secur.202211410258510.1016/j.cose.2021.102585
     [Google Scholar]
  44. ZhanD. HuY. LiW. ChenJ. GuoS. PanZ. Towards robust CNN-based malware classifiers using adversarial examples generated based on two saliency similarities.Neural Comput. Appl.20233523171291714610.1007/s00521‑023‑08590‑1
     [Google Scholar]
  45. RohithG. KumarL.S. Design of deep convolution neural networks for categorical signature classification of raw panchromatic satellite images.Multimedia Tools Appl.20228120283672840410.1007/s11042‑022‑12928‑7
     [Google Scholar]
  46. YamashitaR. NishioM. DoR.K.G. TogashiK. Convolutional neural networks: An overview and application in radiology.Insights Imag.20189461162910.1007/s13244‑018‑0639‑929934920
     [Google Scholar]
  47. ShaabanA.R. ElwanisA.E. HusseinM. DDoS attack detection and classification via Convolutional Neural Network (CNN).2019 Ninth International Conference on Intelligent Computing and Information Systems (ICICIS)pp. 233-238, 2019.10.1109/ICICIS46948.2019.9014826
     [Google Scholar]
  48. ZhangM. ShiW. A feature difference convolutional neural network-based change detection method.IEEE Trans. Geosci. Remote Sens.202058107232724610.1109/TGRS.2020.2981051
     [Google Scholar]
  49. McLaughlinN. Malceiver: Perceiver with hierarchical and multi-modal features for android malware detection.arXiv:2204.059942022
  50. VoloshinC. LeH.M. JiangN. YueY. Empirical study of off-policy policy evaluation for reinforcement learning.arXiv:1911.068542019
  51. KhampariaA. SainiG. GuptaD. KhannaA. TiwariS. de AlbuquerqueV.H.C. Seasonal crops disease prediction and classification using deep convolutional encoder network.Circuits Syst. Signal Process.202039281883610.1007/s00034‑019‑01041‑0
     [Google Scholar]
  52. ChallaS.K. KumarA. SemwalV.B. A multibranch CNN-BiLSTM model for human activity recognition using wearable sensor data.Vis. Comput.202238124095410910.1007/s00371‑021‑02283‑3
     [Google Scholar]
  53. KumarA. SinghS.K. SaxenaS. LakshmananK. SangaiahA.K. ChauhanH. ShrivastavaS. SinghR.K. Deep feature learning for histopathological image classification of canine mammary tumors and human breast cancer.Inf. Sci.202050840542110.1016/j.ins.2019.08.072
     [Google Scholar]
  54. OlimovB. KarshievS. JangE. DinS. PaulA. KimJ. Weight initialization based‐rectified linear unit activation function to improve the performance of a convolutional neural network model.Concurr. Comput.20213322e614310.1002/cpe.6143
     [Google Scholar]
  55. JahanI. AhmedM.F. AliM.O. JangY.M. Self-gated rectified linear unit for performance improvement of deep neural networks.ICT Express2022
     [Google Scholar]
  56. MathewJ. Ajay KumaraM.A. API call based malware detection approach using recurrent neural network—LSTM.Advances in Intelligent Systems and Computing AbrahamA. CherukuriA.K. MelinP. GandhiN. SpringerCham202010.1007/978‑3‑030‑16657‑1_9
     [Google Scholar]
  57. de SouzaP.V.C. GuimarãesA.J. RezendeT.S. AraujoV.S. do NascimentoL.A.F. BatistaL.O. An intelligent hybrid model for the construction of expert systems in malware detection.2020 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS)27-29 May 2020, Bari, Italy, 2020.
     [Google Scholar]
  58. DubeyA.K. JainV. Comparative study of convolution neural network’s relu and leaky-relu activation functions.Proceedings of MARC 2018873880201910.1007/978‑981‑13‑6772‑4_76
     [Google Scholar]
  59. RoyS.K. PaolettiM.E. HautJ.M. A new max-min convolutional network for hyperspectral image classification.2021 11th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)24-26 March 2021, Amsterdam, Netherlands, 2021.
     [Google Scholar]
  60. MourtajiY. BouhormaM. AlghazzawiD. Intelligent framework for malware detection with convolutional neural network.Proceedings of the 2nd International Conference on Networking, Information Systems & Securitypp.1-6, 2019.10.1145/3320326.3320333
     [Google Scholar]
  61. PoonguzhaliN. Identification of malware using CNN and bio-inspired technique.2019 IEEE International Conference on System, Computation, Automation and Networking (ICSCAN)pp.1-5, 2019.
     [Google Scholar]
  62. LiD. ZhaoL. ChengQ. LuN. ShiW. Opcode sequence analysis of Android malware by a convolutional neural network.Concurr. Comput.20203218e530810.1002/cpe.5308
     [Google Scholar]
  63. ZhaoY. XuC. BoB. FengY. Maldeep: A deep learning classification framework against malware variants based on texture visualization.Secur. Commun. Netw.2019201911110.1155/2019/4895984
     [Google Scholar]
  64. SinghP. BorgohainS.K. SarkarA.K. KumarJ. SharmaL.D. Feed-forward deep neural network (FFDNN)-based deep features for static malware detection.Int. J. Intell. Syst.2023202312010.1155/2023/9544481
     [Google Scholar]
  65. SrivastavaN. Dropout: A simple way to prevent neural networks from overfitting.J. Mach. Learn. Res.201415119291958
     [Google Scholar]
  66. KimmelJ.C. McdoleA.D. AbdelsalamM. GuptaM. SandhuR. Recurrent neural networks based online behavioural malware detection techniques for cloud infrastructure.IEEE Access20219680666808010.1109/ACCESS.2021.3077498
     [Google Scholar]
  67. JianY. KuangH. RenC. MaZ. WangH. A novel framework for image-based malware detection with a deep neural network.Comput. Secur.202110910240010.1016/j.cose.2021.102400
     [Google Scholar]
  68. WoźniakM. SiłkaJ. WieczorekM. AlrashoudM. Recurrent neural network model for IoT and networking malware threat detection.IEEE Trans. Industr. Inform.20211785583559410.1109/TII.2020.3021689
     [Google Scholar]
  69. ChamouD. ToupasP. KetzakiE. PapadopoulosS. GiannoutakisK.M. DrosouA. TzovarasD. Intrusion detection system based on network traffic using deep neural networks.2019 IEEE 24th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD)11-13 September 2019, Limassol, Cyprus, 2019.10.1109/CAMAD.2019.8858475
     [Google Scholar]
  70. EbrahimiM. ZhangN. HuJ. RazaM.T. ChenH. Binary black-box evasion attacks against deep learning-based static malware detectors with adversarial byte-level language model.arXiv:2012.079942020
     [Google Scholar]
  71. LeeW.Y. SaxeJ. HarangR. SeqDroid: Obfuscated android malware detection using stacked convolutional and recurrent neural networks.Deep Learning Applications for Cyber Security. Advanced Sciences and Technologies for Security Applications AlazabM. TangM. SpringerCham201910.1007/978‑3‑030‑13057‑2_9
     [Google Scholar]
  72. PrayogoN. AmjadianE. McDonnellS. AbidM.R. Context-aware attended-over distributed specificity for information extraction in cybersecurity.2022 IEEE 13th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)12-15 October 2022, Vancouver, BC, Canada, 2022.10.1109/IEMCON56893.2022.9946567
     [Google Scholar]
  73. YazdinejadA. HaddadPajouhH. DehghantanhaA. PariziR.M. SrivastavaG. ChenM-Y. Cryptocurrency malware hunting: A deep Recurrent Neural Network approach.Appl. Soft Comput.20209610663010.1016/j.asoc.2020.106630
     [Google Scholar]
  74. HalimM.A. AbdullahA. KhairulA.Z.A. Recurrent neural network for malware detection.Int. J. Advance Soft Compu. Appl20191114363
     [Google Scholar]
  75. JeonS. MoonJ. Malware-detection method with a convolutional recurrent neural network using opcode sequences.Inf. Sci.202053511510.1016/j.ins.2020.05.026
     [Google Scholar]
  76. FrihaO. FerragM.A. ShuL. MaglarasL. ChooK.K.R. NafaaM. FELIDS: Federated learning-based intrusion detection system for agricultural Internet of Things.J. Parallel Distrib. Comput.2022165173110.1016/j.jpdc.2022.03.003
     [Google Scholar]
  77. HarunM.B.R. VinayakumarR. SomanK.P. RNNSecureNet: Recurrent neural networks for Cyber security use-cases.arXiv:1901.042812019
     [Google Scholar]
  78. What are some recent and potentially upcoming breakthroughs in deep learning?.Available from: https://www.quora.com/What-are-some-recent-and-potentially-upcoming-breakthroughs-in-deep-learning
  79. GoodfellowI. NIPS 2016 tutorial: Generative adversarial networks.arXiv:1701.001602016
     [Google Scholar]
  80. HitajB. Passgan: A deep learning approach for password guessing.arxiv 1709.004402017
     [Google Scholar]
  81. HuW. TanY. Generating adversarial malware examples for black-box attacks based on GAN.arXiv:1702.059832017
     [Google Scholar]
  82. MirjaliliV. Semi-adversarial networks: Convolutional autoencoders for imparting privacy to face images.2018 International Conference on Biometrics (ICB)20-23 February 2018, Gold Coast, QLD, Australia, 2018.201810.1109/ICB2018.2018.00023
     [Google Scholar]
  83. Research from MetaAvailable from: https://research.fb.com/publications/deepface-closing-the-gap-to-human-level-performance-in-face-verification/
  84. ZhuJ-Y. Unpaired image-to-image translation using cycle-consistent adversarial networks.2017 IEEE International Conference on Computer Vision (ICCV)22-29 October 2017, Venice, Italy, 2017.10.1109/ICCV.2017.244
     [Google Scholar]
  85. HsuC-C. LeeC-Y. ZhuangY-X. Learning to detect fake face images in the wild.2018 International Symposium on Computer, Consumer and Control (IS3C)06-08 December 2018, Taichung, Taiwan, 2018.10.1109/IS3C.2018.00104
     [Google Scholar]
  86. YadavP. MenonN. RaviV. VishvanathanS. PhamT.D. EfficientNet convolutional neural networks-based Android malware detection.Comput. Secur.202211510262210.1016/j.cose.2022.102622
     [Google Scholar]
  87. AkhtarM.S. FengT. Detection of malware by deep learning as CNN-LSTM machine learning techniques in real time.Symmetry20221411230810.3390/sym14112308
     [Google Scholar]
  88. NaeemH. UllahF. NaeemM.R. KhalidS. VasanD. JabbarS. SaeedS. Malware detection in industrial internet of things based on hybrid image visualization and deep learning model.Ad Hoc Networks202010510215410.1016/j.adhoc.2020.102154
     [Google Scholar]
  89. BibiI. AkhunzadaA. MalikJ. AhmedG. RazaM. An effective android ransomware detection through multi-factor feature filtration and recurrent neural network.2019 UK/ China Emerging Technologies (UCET)21-22 August 2019, Glasgow, UK, pp.1-4, 2019.10.1109/UCET.2019.8881884
     [Google Scholar]
  90. LuR. Malware detection with LSTM using opcode language.ArXiv abs/1906.045932019
     [Google Scholar]
  91. YangZ. DengF. HanL. Flexible android malware detection model based on generative adversarial networks with code tensor.arXiv:2210.14225202210.1109/CyberC55534.2022.00015
     [Google Scholar]
  92. HuangY. LiX. QiaoM. TangK. ZhangC. GuiH. WangP. LiuF. Android-SEM: Generative adversarial network for android malware semantic enhancement model based on transfer learning.Electronics202211567210.3390/electronics11050672
     [Google Scholar]
  93. LiH. ZhouS. YuanW. LiJ. LeungH. Adversarial-example attacks toward android malware detection system.IEEE Syst. J.202014165365610.1109/JSYST.2019.2906120
     [Google Scholar]
  94. LinC.J. HuangM.S. LeeC.L. Malware classification using convolutional fuzzy neural networks based on feature fusion and the taguchi method.Appl. Sci.202212241293710.3390/app122412937
     [Google Scholar]
  95. KhodaM.E. KamruzzamanJ. GondalI. ImamT. RahmanA. Malware detection in edge devices with fuzzy oversampling and dynamic class weighting.Appl. Soft Comput.202111210778310.1016/j.asoc.2021.107783
     [Google Scholar]
  96. FarhinF. SultanaI. IslamN. KaiserM.S. RahmanM.S. MahmudM. Attack detection in internet of things using software defined network and fuzzy neural network2020 Joint 9th International Conference on Informatics, Electronics & Vision (ICIEV) and 2020 4th International Conference on Imaging, Vision & Pattern Recognition (icIVPR)26-29 August 2020, Kitakyushu, Japan, 2020.10.1109/ICIEVicIVPR48672.2020.9306666
     [Google Scholar]
  97. YumlembamR. IssacB. JacobS. M. YangL. IoT-based android malware detection using graph neural network with adversarial defense.IEEE Internet Things J.202310108432844410.1109/JIOT.2022.3188583
     [Google Scholar]
  98. BuschJ. KocheturovA. TrespV. SeidlT. NF-GNN: Network flow graph neural networks for malware detection and classification.Proceedings of the 33rd International Conference on Scientific and Statistical Database Managementpp.121-132, 2021.10.1145/3468791.3468814
     [Google Scholar]
  99. ZhangL. LiuP. ChoiY-H. ChenP. Semantics-preserving reinforcement learning attack against graph neural networks for malware detection.arXiv:2009.05602202310.1109/TDSC.2022.3153844
     [Google Scholar]
  100. ÇayırA. ÜnalU. DağH. Random CapsNet forest model for imbalanced malware type classification task.Comput. Secur.202110210213310.1016/j.cose.2020.102133
     [Google Scholar]
  101. ZhangX. WuK. ChenZ. ZhangC. MalCaps: A capsule network based model for the malware classification.Processes20219692910.3390/pr9060929
     [Google Scholar]
  102. KhanS. NaumanM. AlsaifA.S. SyedA.T. ElerakyA.H. Using capsule networks for android malware detection through orientation-based features.Comput. Mater. Continua20227035345536210.32604/cmc.2022.021271
     [Google Scholar]
/content/journals/rascs/10.2174/0126662558279673240515054741
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Exploring Deep Learning Approaches for Ransomware Detection: A Comprehensive Survey
Recent Advances in Computer Science and Communications 18, E290524230472 (2025); https://doi.org/10.2174/0126662558279673240515054741
/content/journals/rascs/10.2174/0126662558279673240515054741
/content/journals/rascs/10.2174/0126662558279673240515054741
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  • Article Type:     Review Article
Keyword(s): CNN; deep learning; GAN; machine learning; malware; Ransomware; RNN

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