8 citations · 18 across the 20 of their papers we have counts for
7 papers · 1 filter
AthenaBench: A Dynamic Benchmark for Evaluating LLMs in Cyber Threat Intelligence
Md Tanvirul Alam, Dipkamal Bhusal, Salman Ahmad +2
Large Language Models (LLMs) have demonstrated strong capabilities in natural language reasoning, yet their application to Cyber Threat Intelligence (CTI) remains limited. CTI anal…
Adapting Large Language Models to Emerging Cybersecurity using Retrieval Augmented Generation
Arnabh Borah, Md Tanvirul Alam, Nidhi Rastogi
Security applications are increasingly relying on large language models (LLMs) for cyber threat detection; however, their opaque reasoning often limits trust, particularly in decis…
R+R: Revisiting Static Feature-Based Android Malware Detection using Machine Learning
Md Tanvirul Alam, Dipkamal Bhusal, Nidhi Rastogi
Static feature-based Android malware detection using machine learning (ML) remains critical due to its scalability and efficiency. However, existing approaches often overlook secur…
Actionable Cyber Threat Intelligence using Knowledge Graphs and Large Language Models
Romy Fieblinger, Md Tanvirul Alam, Nidhi Rastogi
Cyber threats are constantly evolving. Extracting actionable insights from unstructured Cyber Threat Intelligence (CTI) data is essential to guide cybersecurity decisions. Increasi…
CTIBench: A Benchmark for Evaluating LLMs in Cyber Threat Intelligence
Md Tanvirul Alam, Dipkamal Bhusal, Le Nguyen +1
Cyber threat intelligence (CTI) is crucial in today's cybersecurity landscape, providing essential insights to understand and mitigate the ever-evolving cyber threats. The recent r…
SECURE: Benchmarking Large Language Models for Cybersecurity
Dipkamal Bhusal, Md Tanvirul Alam, Le Nguyen +7
Large Language Models (LLMs) have demonstrated potential in cybersecurity applications but have also caused lower confidence due to problems like hallucinations and a lack of truth…
PASA: Attack Agnostic Unsupervised Adversarial Detection using Prediction & Attribution Sensitivity Analysis
Dipkamal Bhusal, Md Tanvirul Alam, Monish K. Veerabhadran +3
Deep neural networks for classification are vulnerable to adversarial attacks, where small perturbations to input samples lead to incorrect predictions. This susceptibility, combin…