24 citations · 65 across the 18 of their papers we have counts for
18 papers
Naming the Concepts Classifiers Rely On: Language-Anchored Decomposition for Faithful Explanation
Ahsan Habib Akash, Dipkamal Bhusal, Stacey Jones +3
Deep neural networks are widely deployed in high-stakes visual applications where interpretability is critical, yet existing explanations face a trade-off: post-hoc concept methods…
H-Sets: Hessian-Guided Discovery of Set-Level Feature Interactions in Image Classifiers
Ayushi Mehrotra, Dipkamal Bhusal, Michael Clifford +1
Feature attribution methods explain the predictions of deep neural networks by assigning importance scores to individual input features. However, most existing methods focus solely…
Training for Trustworthy Saliency Maps: Adversarial Training Meets Feature-Map Smoothing
Dipkamal Bhusal, Md Tanvirul Alam, Nidhi Rastogi
Gradient-based saliency methods such as Vanilla Gradient (VG) and Integrated Gradients (IG) are widely used to explain image classifiers, yet the resulting maps are often noisy and…
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…
FACE: Faithful Automatic Concept Extraction
Dipkamal Bhusal, Michael Clifford, Sara Rampazzi +1
Interpreting deep neural networks through concept-based explanations offers a bridge between low-level features and high-level human-understandable semantics. However, existing aut…
Concept-Based Masking: A Patch-Agnostic Defense Against Adversarial Patch Attacks
Ayushi Mehrotra, Derek Peng, Dipkamal Bhusal +1
Adversarial patch attacks pose a practical threat to deep learning models by forcing targeted misclassifications through localized perturbations, often realized in the physical wor…
Do Sparse Subnetworks Exhibit Cognitively Aligned Attention? Effects of Pruning on Saliency Map Fidelity, Sparsity, and Concept Coherence
Sanish Suwal, Dipkamal Bhusal, Michael Clifford +1
Prior works have shown that neural networks can be heavily pruned while preserving performance, but the impact of pruning on model interpretability remains unclear. In this work, w…
Smaller is Better: Enhancing Transparency in Vehicle AI Systems via Pruning
Sanish Suwal, Shaurya Garg, Dipkamal Bhusal +2
Connected and autonomous vehicles continue to heavily rely on AI systems, where transparency and security are critical for trust and operational safety. Post-hoc explanations provi…
Learning to Look: Cognitive Attention Alignment with Vision-Language Models
Ryan L. Yang, Dipkamal Bhusal, Nidhi Rastogi
Convolutional Neural Networks (CNNs) frequently "cheat" by exploiting superficial correlations, raising concerns about whether they make predictions for the right reasons. Inspired…
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…
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…
Looking Beyond IoCs: Automatically Extracting Attack Patterns from External CTI
Md Tanvirul Alam, Dipkamal Bhusal, Youngja Park +1
Public and commercial organizations extensively share cyberthreat intelligence (CTI) to prepare systems to defend against existing and emerging cyberattacks. However, traditional C…
SoK: Modeling Explainability in Security Analytics for Interpretability, Trustworthiness, and Usability
Dipkamal Bhusal, Rosalyn Shin, Ajay Ashok Shewale +4
Interpretability, trustworthiness, and usability are key considerations in high-stake security applications, especially when utilizing deep learning models. While these models are…
CyNER: A Python Library for Cybersecurity Named Entity Recognition
Md Tanvirul Alam, Dipkamal Bhusal, Youngja Park +1
Open Cyber threat intelligence (OpenCTI) information is available in an unstructured format from heterogeneous sources on the Internet. We present CyNER, an open-source python libr…
Adversarial Patterns: Building Robust Android Malware Classifiers
Dipkamal Bhusal, Nidhi Rastogi
Machine learning models are increasingly being adopted across various fields, such as medicine, business, autonomous vehicles, and cybersecurity, to analyze vast amounts of data, d…
Multi-Label Classification of Thoracic Diseases using Dense Convolutional Network on Chest Radiographs
Dipkamal Bhusal, Sanjeeb Prasad Panday
Traditional methods of identifying pathologies in X-ray images rely heavily on skilled human interpretation and are often time-consuming. The advent of deep learning techniques has…