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arxiv: 2605.16589 · v1 · pith:I7WZDKX2new · submitted 2026-05-15 · 💻 cs.CR

STRIKE: A Structured Taxonomy of Cybercrime for Risk, Impact, Knowledge, and Evolution

Melissa Pappy , Linh Nguyen , Suman Kumar , Byungkwan Jung , Bernard Chen This is my paper

Pith reviewed 2026-05-20 16:27 UTC · model grok-4.3

classification 💻 cs.CR
keywords cybercrime taxonomySTRIKE frameworkthreat classificationattack vectorssocietal impactdetection techniquesmitigation strategiesemerging cyber threats
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The pith

STRIKE introduces a multi-dimensional taxonomy that organizes both conventional and emerging cybercrimes by attack vectors, tactics, impact, detection, and mitigation.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper establishes a new taxonomy called STRIKE to fill gaps left by traditional classification schemes that cannot keep pace with the scale and variety of modern cyber threats. It applies this framework to threats ranging from ransomware and phishing to deepfakes and supply chain attacks, while also surveying detection methods and offering a practical response workflow. A sympathetic reader would care because the taxonomy supplies a shared structure that researchers, security teams, and policymakers can use to compare threats, evaluate risks, and adapt defenses as attack methods change.

Core claim

We introduce STRIKE, a Structured Taxonomy for Risk, Impact, Knowledge, and Emerging Threats, which provides a unified, multi-dimensional framework for categorizing cybercrimes. STRIKE spans both conventional and emerging domains, including ransomware, phishing, network intrusion, child sexual abuse material, cryptojacking, deepfakes, and supply chain attacks. It organizes threats using criteria such as attack vectors, adversarial tactics, societal impact, detection techniques, and mitigation strategies. Alongside the taxonomy, we review recent advances in detection methodologies and present a response workflow to assist practitioners under active threat conditions. This work offers a практи

What carries the argument

The STRIKE taxonomy, a multi-dimensional system that classifies cyber threats according to attack vectors, adversarial tactics, societal impact, detection techniques, and mitigation strategies.

If this is right

  • Researchers gain a consistent structure for comparing threats across domains.
  • Security professionals receive a shared vocabulary for assessing risk and choosing responses.
  • Policymakers can map societal impacts more systematically when allocating resources.
  • Detection research can be organized around the same criteria used for classification.
  • Practitioners gain a concrete workflow for handling active incidents.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The taxonomy could serve as a backbone for automated classification tools that ingest incident reports in real time.
  • Periodic updates to the taxonomy might reveal patterns in how new threats migrate between categories.
  • Cross-referencing STRIKE with legal or regulatory frameworks could highlight gaps in current cybercrime statutes.
  • Training datasets built from the taxonomy might improve machine-learning models that predict attack evolution.

Load-bearing premise

Traditional classification schemes cannot capture the full complexity and rapid evolution of current cyber threats.

What would settle it

A newly observed cyber incident that fits none of the STRIKE dimensions or requires an entirely new top-level category would show the taxonomy is incomplete.

Figures

Figures reproduced from arXiv: 2605.16589 by Bernard Chen, Byungkwan Jung, Linh Nguyen, Melissa Pappy, Suman Kumar.

Figure 1
Figure 1. Figure 1: Cybercrime: Attack Profile [PITH_FULL_IMAGE:figures/full_fig_p007_1.png] view at source ↗
read the original abstract

Cybercrime has grown exponentially in both scale and sophistication, posing significant threats. As attack methods evolve rapidly, traditional classification schemes often fail to capture the complexity and diversity of modern threats. To address this gap, we introduce STRIKE,a Structured Taxonomy for Risk, Impact, Knowledge, and Emerging Threats, which provides a unified, multi-dimensional framework for categorizing cybercrimes. STRIKE spans both conventional and emerging domains, including ransomware, phishing, network intrusion, child sexual abuse material (CSAM), cryptojacking, deepfakes, and supply chain attacks. It organizes threats using criteria such as attack vectors, adversarial tactics, societal impact, detection techniques, and mitigation strategies. Alongside the taxonomy, we review recent advances in detection methodologies and present a response workflow to assist practitioners under active threat conditions. This work offers researchers, security professionals, and policymakers a practical foundation for threat analysis, comparative evaluation, and adaptive cyber defense.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper introduces STRIKE, a multi-dimensional taxonomy for categorizing cybercrimes that spans conventional and emerging threats including ransomware, phishing, network intrusion, CSAM, cryptojacking, deepfakes, and supply chain attacks. It organizes threats according to attack vectors, adversarial tactics, societal impact, detection techniques, and mitigation strategies, reviews recent detection advances, and presents a practitioner response workflow.

Significance. If the taxonomy is equipped with explicit, non-redundant classification rules and is demonstrated with concrete worked examples, it could supply a practical foundation for comparative threat analysis and adaptive defense, addressing documented limitations in existing single-dimension schemes.

major comments (2)
  1. [Abstract and §3] Abstract and §3 (Taxonomy Structure): the central claim that STRIKE supplies a 'unified, multi-dimensional framework' is not supported by any explicit rules or decision procedure for resolving cross-dimensional overlaps. A supply-chain ransomware incident, for example, simultaneously matches network-intrusion, supply-chain, and societal-impact categories; without stated precedence or disambiguation criteria the taxonomy risks inconsistent application.
  2. [§4] §4 (Validation and Examples): no concrete classification examples, inter-rater agreement metrics, or empirical validation data are provided to demonstrate that the proposed dimensions produce coherent, reproducible assignments. This absence directly undermines the utility claim for 'comparative evaluation and adaptive cyber defense'.
minor comments (2)
  1. [Abstract] Abstract: 'STRIKE,a' is missing a space after the comma.
  2. [Title and Abstract] Title vs. abstract: the title ends with 'Evolution' while the abstract expands the acronym as 'Emerging Threats'; the two should be aligned for consistency.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback, which identifies key opportunities to strengthen the presentation and applicability of the STRIKE taxonomy. We address each major comment below and outline targeted revisions.

read point-by-point responses

  1. Referee: [Abstract and §3] Abstract and §3 (Taxonomy Structure): the central claim that STRIKE supplies a 'unified, multi-dimensional framework' is not supported by any explicit rules or decision procedure for resolving cross-dimensional overlaps. A supply-chain ransomware incident, for example, simultaneously matches network-intrusion, supply-chain, and societal-impact categories; without stated precedence or disambiguation criteria the taxonomy risks inconsistent application.

    Authors: We agree that the absence of explicit disambiguation rules limits the taxonomy's consistency in practice. The current manuscript describes the dimensions but does not formalize precedence or overlap resolution. In the revised manuscript we will add a new subsection to §3 that specifies a decision procedure, including priority ordering (e.g., attack vector first, then tactics, then impact) and a worked example of the supply-chain ransomware case. This addition will directly support the unified-framework claim. revision: yes

  2. Referee: [§4] §4 (Validation and Examples): no concrete classification examples, inter-rater agreement metrics, or empirical validation data are provided to demonstrate that the proposed dimensions produce coherent, reproducible assignments. This absence directly undermines the utility claim for 'comparative evaluation and adaptive cyber defense'.

    Authors: We acknowledge that §4 currently contains no worked examples. We will revise this section to include multiple concrete classification examples (ransomware, supply-chain attack, deepfake-enabled fraud) that walk through assignment to each STRIKE dimension. Inter-rater agreement metrics and large-scale empirical validation, however, would require new annotation studies that fall outside the scope of the present conceptual paper; we will therefore add an explicit limitations paragraph and a future-work statement rather than claim such validation has been performed. revision: partial

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on domain assumptions about how cyber threats should be categorized rather than on new mathematical parameters or invented physical entities.

axioms (1)
  • domain assumption Cybercrimes can be usefully organized along the dimensions of attack vectors, adversarial tactics, societal impact, detection techniques, and mitigation strategies.
    This assumption underpins the entire multi-dimensional structure described in the abstract.

pith-pipeline@v0.9.0 · 5700 in / 1152 out tokens · 46577 ms · 2026-05-20T16:27:25.749334+00:00 · methodology

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Works this paper leans on

103 extracted references · 103 canonical work pages · 1 internal anchor

  1. [1]

    Bullying definition, https://www.stopbullying.gov/bullying/ what-is-bullying

    work page

  2. [2]

    A new way to detect digital child pornography, http://abcnews.go.com/ Technology/detect-digital-child-pornography/story?id=10362616

    work page

  3. [3]

    Report on phishing, https://www.justice.gov/sites/default/files/opa/ legacy/2006/11/21/report_on_phishing.pdf

    work page 2006

  4. [4]

    https://www.gao.gov/blog/ solarwinds-cyberattack-demands-significant-federal-and-private-sector\ -response-infographic, accessed: 2024-07-12

    The solarwinds cyberattack. https://www.gao.gov/blog/ solarwinds-cyberattack-demands-significant-federal-and-private-sector\ -response-infographic, accessed: 2024-07-12

    work page 2024

  5. [5]

    uk/blog/verizons-2017-data-breach-investigations-report

    Verizon: 2017 data breach investigations report,https://www.itgovernance.co. uk/blog/verizons-2017-data-breach-investigations-report

    work page 2017

  6. [6]

    https://blog.avast.com/ ransomware-that-infected-telefonica-and-nhs-hospitals-is-spreading-\ aggressively-with-over-50000-attacks-so-far-today , accessed: 2024-07-12

    Wannacry ransomware attack. https://blog.avast.com/ ransomware-that-infected-telefonica-and-nhs-hospitals-is-spreading-\ aggressively-with-over-50000-attacks-so-far-today , accessed: 2024-07-12

    work page 2024

  7. [7]

    What is cyberbullying, https://www.stopbullying.gov/cyberbullying/ what-is-it/index.html

    work page

  8. [8]

    What is cyberbullying, exactly?,http://www.stopcyberbullying.org/what_is_ cyberbullying_exactly.html Title Suppressed Due to Excessive Length 19

    work page

  9. [9]

    What legally makes it child pornography?,https://www.hg.org/article.asp? id=38082

    work page

  10. [10]

    SANS Institute InfoSec Read- ing Room (Jan 2007), https://www.sans.org/reading-room/whitepapers/ threats/phishing-analysis-growing-problem-1417

    Phishing: An analysis of a growing problem. SANS Institute InfoSec Read- ing Room (Jan 2007), https://www.sans.org/reading-room/whitepapers/ threats/phishing-analysis-growing-problem-1417

    work page 2007

  11. [11]

    In: International Conference on Information Security Applications

    Adeniran,A.,Human,K.,Mohaisen,D.:Dissectingtheinfrastructureusedinweb- based cryptojacking: A measurement perspective. In: International Conference on Information Security Applications. pp. 270–283. Springer (2024)

    work page 2024

  12. [12]

    In: Proceedings of the ACM Workshop on Information Hiding and Multimedia Security

    Agarwal, S., Farid, H., Gu, Y., He, M., Nagano, K., Li, H.: Detecting deep-fake videos from appearance and behavior. In: Proceedings of the ACM Workshop on Information Hiding and Multimedia Security. pp. 81–87. ACM (2020)

    work page 2020

  13. [13]

    IEEE Access10, 85493–85517 (2022)

    Agarwal, U., Rishiwal, V., Tanwar, S., Chaudhary, R., Sharma, G., Bokoro, P.N., Sharma, R.: Blockchain technology for secure supply chain management: A com- prehensive review. IEEE Access10, 85493–85517 (2022)

    work page 2022

  14. [14]

    Transactions on Emerging Telecommunications Technologies 32(1), e4150 (2021)

    Ahmad, Z., Shahid Khan, A., Wai Shiang, C., Abdullah, J., Ahmad, F.: Network intrusion detection system: A systematic study of machine learning and deep learning approaches. Transactions on Emerging Telecommunications Technologies 32(1), e4150 (2021)

    work page 2021

  15. [15]

    IEEE Access8, 137293– 137311 (2020)

    Al-Khater, W.A., Al-Maadeed, S., Ahmed, A.A., Sadiq, A.S., Khan, M.K.: Com- prehensive review of cybercrime detection techniques. IEEE Access8, 137293– 137311 (2020). https://doi.org/10.1109/ACCESS.2020.3011259

    work page doi:10.1109/access.2020.3011259 2020

  16. [16]

    Journal of Information Security and Applications78, 103529 (2025)

    Ali, F., Zhang, W.: Phishing attacks in 2025: Evolution, trends, and countermea- sures. Journal of Information Security and Applications78, 103529 (2025)

    work page 2025

  17. [17]

    Journal of Imaging11(3), 73 (2025)

    Amerini, I., Barni, M., Battiato, S., Bestagini, P., Boato, G., Bruni, V., Caldelli, R., De Natale, F., De Nicola, R., Guarnera, L., et al.: Deepfake media forensics: Status and future challenges. Journal of Imaging11(3), 73 (2025)

    work page 2025

  18. [18]

    Child Pornography (Jun 2011), http://research.universalessays.com/ criminal-justice-research-paper/child-abuse-research-paper/ child-pornography-research-paper/

    Babchishin KM, Hanson RK, H.: Child pornography. Child Pornography (Jun 2011), http://research.universalessays.com/ criminal-justice-research-paper/child-abuse-research-paper/ child-pornography-research-paper/

    work page 2011

  19. [19]

    Macmillan Technical Publishing (2000)

    Bace, R.: Intrusion Detection. Macmillan Technical Publishing (2000)

    work page 2000

  20. [20]

    Aggression and Violent Behavior45, 93–101 (2019)

    Barlett, C.P.: Cyberbullying among youth: A comprehensive review of current international research and its implications and application to policy and practice. Aggression and Violent Behavior45, 93–101 (2019)

    work page 2019

  21. [21]

    In: 2021 IEEE Symposium on Security and Privacy (SP)

    Barr-Smith, F., Ugarte-Pedrero, X., Graziano, M., Spolaor, R., Martinovic, I.: Survivalism: Systematic analysis of windows malware living-off-the-land. In: 2021 IEEE Symposium on Security and Privacy (SP). pp. 1557–1574. IEEE (2021)

    work page 2021

  22. [22]

    In: Proceedings of the 2024 7th International Conference on Informa- tion Science and Systems

    Blancaflor, E., Dionisio, P.J., Juacalla, D.J., Oraya, A., Yabut, J.A.: Cryptojack- ing:Acomprehensivereviewofattacktechniques,victims,andeffectivemitigation strategies. In: Proceedings of the 2024 7th International Conference on Informa- tion Science and Systems. pp. 42–48 (2024)

    work page 2024

  23. [23]

    Boyens, J., Paulsen, C., Moorthy, R., Bartol, N.: Key practices in cyber supply chain risk management: Observations from industry. Tech. Rep. NISTIR 8276, NIST (2020), https://doi.org/10.6028/NIST.IR.8276

    work page doi:10.6028/nist.ir.8276 2020

  24. [24]

    arXiv preprint arXiv:1802.07228 (2018)

    Brundage, M., Avin, S., Clark, J., Toner, H., Eckersley, P., Garfinkel, B.: The malicious use of artificial intelligence: Forecasting, prevention, and mitigation. arXiv preprint arXiv:1802.07228 (2018)

    work page arXiv 2018

  25. [25]

    Multidisciplinary Reviews8(1), 2025024– 2025024 (2025) 20 M.Pappy et al

    Buçaj, E., Idrizaj, K.: The need for cybercrime regulation on a global scale by the international law and cyber convention. Multidisciplinary Reviews8(1), 2025024– 2025024 (2025) 20 M.Pappy et al

    work page 2025

  26. [26]

    IEEE Security Privacy12(1), 28–38 (Jan 2014)

    Caputo,D.D.,Pfleeger,S.L.,Freeman,J.D.,Johnson,M.E.:Goingspearphishing: Exploring embedded training and awareness. IEEE Security Privacy12(1), 28–38 (Jan 2014)

    work page 2014

  27. [27]

    IEEE Commu- nications Surveys and Tutorials21(3), 2671–2701 (2019)

    Chaabouni, N., Mosbah, M., Zemmari, A., Sauvignac, C., Faruki, P.: Network intrusion detection for iot security based on learning techniques. IEEE Commu- nications Surveys and Tutorials21(3), 2671–2701 (2019)

    work page 2019

  28. [28]

    In- ternational Journal of Accounting Information Systems38, 100467 (2020), 2019 UW CISA Symposium

    Chandra, A., Snowe, M.J.: A taxonomy of cybercrime: Theory and design. In- ternational Journal of Accounting Information Systems38, 100467 (2020), 2019 UW CISA Symposium

    work page 2020

  29. [29]

    Inter- national Journal of Accounting Information Systems38, 100467 (2020)

    Chandra, A., Snowe, M.J.: A taxonomy of cybercrime: Theory and design. Inter- national Journal of Accounting Information Systems38, 100467 (2020)

    work page 2020

  30. [30]

    https://doi.org/10.13189/ujer.2013.010301,http://www

    CharlesE.Notar,SharonPadgett,J.R.:Cyberbullying:Resourcesforintervention and prevention (2013). https://doi.org/10.13189/ujer.2013.010301,http://www. hrpub.org

    work page doi:10.13189/ujer.2013.010301 2013

  31. [31]

    In: 2016 Dig- ital Media Industry Academic Forum (DMIAF)

    Chatzis, V., Panagiotopoulos, F., Mardiris, V.: Face to iris area ratio as a feature for children detection in digital forensics applications. In: 2016 Dig- ital Media Industry Academic Forum (DMIAF). pp. 121–124 (July 2016). https://doi.org/10.1109/DMIAF.2016.7574915

    work page doi:10.1109/dmiaf.2016.7574915 2016

  32. [32]

    International Journal of Computer Applications (0975 – 8887)93(3) (May 2014)

    Chawla, M., Chouhan, S.S.: A survey of phishing attack techniques. International Journal of Computer Applications (0975 – 8887)93(3) (May 2014)

    work page 2014

  33. [33]

    Foreign Affairs 98(1), 147–155 (2019)

    Chesney, R., Citron, D.K.: Deepfakes and the new disinformation war. Foreign Affairs 98(1), 147–155 (2019)

    work page 2019

  34. [34]

    International Journal of Advanced Research in Computer Science and Software Engineering3(5) (May 2013)

    Chhikara, J., Dahiya, R., Garg, N., Rani, M.: Phishing and anti-phishing tech- niques: Case study. International Journal of Advanced Research in Computer Science and Software Engineering3(5) (May 2013)

    work page 2013

  35. [35]

    In: Proceedings of the 19th International Conference on Availability, Reliability and Security

    Chmiel, K., Rajba, P.: How to evade modern web cryptojacking detection tools? a review of practical findings. In: Proceedings of the 19th International Conference on Availability, Reliability and Security. pp. 1–10 (2024)

    work page 2024

  36. [36]

    ACM Computing Surveys55(3), 1–38 (2022)

    Conti, M., Cozzo, D., Gangwal, S., Kumar, N.: A survey on ransomware detection techniques. ACM Computing Surveys55(3), 1–38 (2022)

    work page 2022

  37. [37]

    Cordey, S.: Software supply chain attacks: An illustrated typological review. Tech. rep., ETH Zurich (2023)

    work page 2023

  38. [38]

    Network Security2021(8), 7–11 (2021)

    Davidson, R.: The fight against malware as a service. Network Security2021(8), 7–11 (2021)

    work page 2021

  39. [39]

    Ieee Access 10, 36429–36463 (2022)

    Do, N.Q., Selamat, A., Krejcar, O., Herrera-Viedma, E., Fujita, H.: Deep learning for phishing detection: Taxonomy, current challenges and future directions. Ieee Access 10, 36429–36463 (2022)

    work page 2022

  40. [41]

    Computers in Human Behavior92, 403–418 (2019)

    Donalds, C., Osei-Bryson, K.M.: Toward a cybercrime classification ontology: A knowledge-based approach. Computers in Human Behavior92, 403–418 (2019)

    work page 2019

  41. [42]

    153–183 (2024)

    Dsouza, D.S., Hajjar, A.E., Jahankhani, H.: Deepfakes in social engineering at- tacks pp. 153–183 (2024)

    work page 2024

  42. [43]

    A first look at browser-based Cryptojacking

    Eskandari, S., Leoutsarakos, A., Mursch, T., Clark, J.: A first look at browser- based cryptojacking. arXiv preprint arXiv:1803.02887 (2018)

    work page internal anchor Pith review Pith/arXiv arXiv 2018

  43. [44]

    Europol: Internet organised crime threat assessment (iocta) 2023 (2023), https://www.europol.europa.eu/publications-events/main-reports/ iocta-report

    work page 2023

  44. [45]

    IEEE Access (2024) Title Suppressed Due to Excessive Length 21

    Ferdous,J.,Islam,R.,Mahboubi,A.,Islam,M.Z.:Ai-basedransomwaredetection: A comprehensive review. IEEE Access (2024) Title Suppressed Due to Excessive Length 21

    work page 2024

  45. [46]

    Computers in Human Behavior129, 107141 (2022)

    Fido, D., Rao, J., Harper, C.A.: Celebrity status, sex, and variation in psychopa- thy predict judgements of and proclivity to generate and distribute deepfake pornography. Computers in Human Behavior129, 107141 (2022)

    work page 2022

  46. [47]

    World Economic Forum Reports (2023), https://www.weforum.org/reports/global-risks-report-2023

    Forum, W.E.: Global risks report 2023. World Economic Forum Reports (2023), https://www.weforum.org/reports/global-risks-report-2023

    work page 2023

  47. [48]

    In: International Con- ference on Machine Learning

    Frank, J., Eisenhofer, T., Schönherr, L., Fischer, A., Kolossa, D., Holz, T.: Lever- aging frequency analysis for deep fake image recognition. In: International Con- ference on Machine Learning. pp. 3247–3258. PMLR (2020)

    work page 2020

  48. [49]

    Smith, Maria A

    Gina S. Smith, Maria A. Minor, H.M.B.: Cyberbullying in higher education: Im- plications and solutions (2014). https://doi.org/10.5590/JERAP.2014.04.1.05

    work page doi:10.5590/jerap.2014.04.1.05 2014

  49. [50]

    WIRED (2018), https://www.wired.com/story/ notpetya-cyberattack-ukraine-russia-code-crashed-the-world

    Greenberg, A.: The untold story of notpetya, the most devastating cyberattack in history. WIRED (2018), https://www.wired.com/story/ notpetya-cyberattack-ukraine-russia-code-crashed-the-world

    work page 2018

  50. [51]

    Cluster Computing26(6), 3753– 3780 (2023)

    Heidari, A., Jabraeil Jamali, M.A.: Internet of things intrusion detection systems: a comprehensive review and future directions. Cluster Computing26(6), 3753– 3780 (2023)

    work page 2023

  51. [52]

    Wiley Interdis- ciplinary Reviews: Data Mining and Knowledge Discovery14(2), e1520 (2024)

    Heidari, A., Jafari Navimipour, N., Dag, H., Unal, M.: Deepfake detection using deep learning methods: A systematic and comprehensive review. Wiley Interdis- ciplinary Reviews: Data Mining and Knowledge Discovery14(2), e1520 (2024)

    work page 2024

  52. [53]

    Hernandez-Castro, Julio, Cartwright, Edward, Stepanova, A.: Economic analysis of ransomware (Mar 2017)

    work page 2017

  53. [54]

    University of Michigan Press (2016)

    Hessick, C.B.: Refining child pornography law. University of Michigan Press (2016)

    work page 2016

  54. [55]

    Archives of sui- cide research 14(3), 206–221 (2010)

    Hinduja, S., Patchin, J.W.: Bullying, cyberbullying, and suicide. Archives of sui- cide research 14(3), 206–221 (2010)

    work page 2010

  55. [56]

    Prevention and Re- sponse, Cyberbullying Research Center (2014)

    Hinduja, S., Patchin, J.W.: Cyberbullying: identification. Prevention and Re- sponse, Cyberbullying Research Center (2014)

    work page 2014

  56. [57]

    Communications of the ACM 55(1), 74–81 (2012)

    Hong, J.: The state of phishing attacks. Communications of the ACM 55(1), 74–81 (2012)

    work page 2012

  57. [58]

    Leading Issues in Information Warfare and Security Research1(1), 80–106 (2011)

    Hutchins, E.M., et al.: Intelligence-driven computer network defense informed by analysis of adversary campaigns and intrusion kill chains. Leading Issues in Information Warfare and Security Research1(1), 80–106 (2011)

    work page 2011

  58. [59]

    Institute, P.: Cost of a data breach report 2023 (2023),https://www.ibm.com/ reports/data-breach

    work page 2023

  59. [60]

    Interpol: Interpol: Crimes against children (2021),https://www.interpol.int

    work page 2021

  60. [61]

    211–219 (2019)

    Islam, M., Mahmood, A.N., Watters, P., Alazab, M.: Forensic detection of child exploitation material using deep learning pp. 211–219 (2019)

    work page 2019

  61. [62]

    Journal of Applied Security Research pp

    Kaur,K.,Jain,A.K.:Asurveyonphishingattacktaxonomy,detectiontechniques, datasets, and security measures. Journal of Applied Security Research pp. 1–52 (2025)

    work page 2025

  62. [63]

    1–6 (2021)

    Khalid, A., Zainal, A., Maarof, M.A., Ghaleb, F.A.: Advanced persistent threat detection: A survey pp. 1–6 (2021)

    work page 2021

  63. [64]

    arXiv preprint arXiv:2507.21157 (2025)

    Khan, N., Nguyen, T., Bermak, A., Khalil, I.: Unmasking synthetic realities in generative ai: A comprehensive review of adversarially robust deepfake detection systems. arXiv preprint arXiv:2507.21157 (2025)

    work page arXiv 2025

  64. [65]

    In: Proceedings of the 25th USENIX Security Symposium

    Kharraz, A., Robertson, W., Arshad, S., Kirda, E., Mulliner, C.: Unveil: A large- scale, automated approach to detecting ransomware. In: Proceedings of the 25th USENIX Security Symposium. usenix (2016)

    work page 2016

  65. [66]

    Detection of Intrusions 22 M.Pappy et al

    Kharraz, A., Robertson, W., Balzarotti, D., Bilge, L., Kirda, E.: Cutting the gor- dian knot: A look under the hood of ransomware attacks. Detection of Intrusions 22 M.Pappy et al. and Malware, and Vulnerability Assessment Lecture Notes in Computer Science 9148, 3–24 (Jun 2015)

    work page 2015

  66. [67]

    Cybersecurity2(1), 1–22 (2019)

    Khraisat, A., Gondal, I., Vamplew, P., Kamruzzaman, J.: Survey of intrusion detection systems: techniques, datasets and challenges. Cybersecurity2(1), 1–22 (2019)

    work page 2019

  67. [68]

    Soft Computing25(15), 9731–9763 (2021)

    Kocher, G., Kumar, G.: Machine learning and deep learning methods for intrusion detection systems: recent developments and challenges. Soft Computing25(15), 9731–9763 (2021)

    work page 2021

  68. [69]

    Psychological Bulletin140(4), 1073–1137 (2014)

    Kowalski, R.M., et al.: Bullying in the digital age: A critical review and meta- analysis of cyberbullying research among youth. Psychological Bulletin140(4), 1073–1137 (2014)

    work page 2014

  69. [70]

    In: 2024 2nd International Conference on Advancement in Computation and Computer Technologies (InCACCT)

    Kshetri, N., Rahman, M.M., Sayeed, S.A., Sultana, I.: cryptoran: A review on cryptojacking and ransomware attacks in the banking industry—threats, chal- lenges, and problems. In: 2024 2nd International Conference on Advancement in Computation and Computer Technologies (InCACCT). pp. 523–528. IEEE (2024)

    work page 2024

  70. [71]

    523–528 (2024)

    Kshetri, N., Rahman, M.M., Sayeed, S.A., Sultana, I.: cryptoran: A review on cryptojacking and ransomware attacks wrt banking industry-threats, challenges, and problems pp. 523–528 (2024)

    work page 2024

  71. [72]

    Ieee Access9, 157761–157779 (2021)

    Kumar, S., Gupta, S., Arora, S.: Research trends in network-based intrusion de- tection systems: A review. Ieee Access9, 157761–157779 (2021)

    work page 2021

  72. [73]

    Computers and security105, 102248 (2021)

    Lallie, H.S., Shepherd, L.A., Nurse, J.R., Erola, A., Epiphaniou, G., Maple, C., Bellekens, X.: Cyber security in the age of covid-19: A timeline and analysis of cyber-crime and cyber-attacks during the pandemic. Computers and security105, 102248 (2021)

    work page 2021

  73. [74]

    Journal of network and computer applications36(1), 16– 24 (2013)

    Liao, H.J., Lin, C.H.R., Lin, Y.C., Tung, K.Y.: Intrusion detection system: A comprehensive review. Journal of network and computer applications36(1), 16– 24 (2013)

    work page 2013

  74. [75]

    IGI Global (2019)

    Lizut, J.: Cyberbullying: Educators and Parents. IGI Global (2019)

    work page 2019

  75. [76]

    In: Nordic Conference on Secure IT Systems

    Meland, P.H., Johansen, B.H., Sindre, G.: An experimental analysis of cryptojack- ing attacks. In: Nordic Conference on Secure IT Systems. pp. 155–170. Springer (2019)

    work page 2019

  76. [77]

    Microsoft: Photodna technology (2019), https://www.microsoft.com/en-us/ photodna

    work page 2019

  77. [78]

    ACM Computing Surveys 54(1), 1–41 (2021)

    Mirsky, Y., Lee, W.: The creation and detection of deepfakes: A survey. ACM Computing Surveys 54(1), 1–41 (2021)

    work page 2021

  78. [79]

    Asian School of Cyber Laws pp

    N, R.: Evolution of cyber crimes. Asian School of Cyber Laws pp. 1–2 (2008)

    work page 2008

  79. [80]

    O’Gorman, G., McDonald, G.: Ransomware: A growing menace (Nov 2012)

    work page 2012

  80. [81]

    In: Proceedings of the 24th International Symposium on Research in Attacks, Intrusions and Defenses

    Ongun, T., Stokes, J.W., Or, J.B., Tian, K., Tajaddodianfar, F., Neil, J., Seifert, C., Oprea, A., Platt, J.C.: Living-off-the-land command detection using active learning. In: Proceedings of the 24th International Symposium on Research in Attacks, Intrusions and Defenses. pp. 442–455 (2021)

    work page 2021

Showing first 80 references.