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Ashish Kundu

8 papers hereh-index 586 citations18 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author6
  • last author2

Across the 8 of 8 papers where every author was matched, so the position is known.

fields
  • cs.CR6
  • cs.AI1
  • cs.SE1
same name
  • Ashish Kundu — 11 papers, h 7
  • Ashish Kundu — 9 papers, h 3
  • Ashish Kundu — 9 papers, h 3
  • Ashish Kundu — 6 papers, h 2
  • Ashish Kundu — 2 papers, h 2
  • Ashish Kundu — 2 papers, h 5

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20232026
most citedUsing Retriever Augmented Large Language Models for Attack Graph Generation

4 citations · 8 across the 8 of their papers we have counts for

collaborators
Showing 2024Show all

4 papers · 1 filter

cs.CR2024★ 4 cited

Using Retriever Augmented Large Language Models for Attack Graph Generation

Renascence Tarafder Prapty, Ashish Kundu, Arun Iyengar

As the complexity of modern systems increases, so does the importance of assessing their security posture through effective vulnerability management and threat modeling techniques.…

cs.AI2024★ 1 cited

Code Hallucination

Mirza Masfiqur Rahman, Ashish Kundu

Generative models such as large language models are extensively used as code copilots and for whole program generation. However, the programs they generate often have questionable…

cs.CR2024

Revisiting Concept Drift in Windows Malware Detection: Adaptation to Real Drifted Malware with Minimal Samples

Adrian Shuai Li, Arun Iyengar, Ashish Kundu +1

In applying deep learning for malware classification, it is crucial to account for the prevalence of malware evolution, which can cause trained classifiers to fail on drifted malwa…

cs.CR2024★ 1 cited

Transfer Learning for Security: Challenges and Future Directions

Adrian Shuai Li, Arun Iyengar, Ashish Kundu +1

Many machine learning and data mining algorithms rely on the assumption that the training and testing data share the same feature space and distribution. However, this assumption m…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.