2 citations · 2 across the 28 of their papers we have counts for
31 papers
RGLD: Randomized Global-Local Density Estimation for Tabular Anomaly Detection
Quanling Zhao, Jiaying Yang, Ye Tian +5
Unsupervised tabular anomaly detection requires methods that are accurate, robust across heterogeneous datasets, and computationally efficient. Classical statistical detectors are…
CyberMaskQA: A Privacy-Aware Benchmark for Evaluating Large Language Models in Cybersecurity Question Answering
Matilda Gaddi, Jin Noh, Onat Gungor +1
Large language models (LLMs) are increasingly applied to cybersecurity question answering (QA) for critical tasks such as incident response and vulnerability analysis. However, rea…
FoodCHA: Multi-Modal LLM Agent for Fine-Grained Food Analysis
Woojin Lee, Pranav Mekkoth, Ye Tian +2
The widespread adoption of camera-equipped mobile devices and wearables has enabled convenient capture of meal images, making food recognition a key component for real time dietary…
CAN-QA: A Question-Answering Benchmark for Reasoning over In-Vehicle CAN Traffic
Jing Chen, Abhijay Deevi, Onat Gungor +1
The Controller Area Network (CAN) is a safety-critical in-vehicle communication protocol that lacks built-in security mechanisms, making intrusion detection essential. Existing app…
A KL Lens on Quantization: Fast, Forward-Only Sensitivity for Mixed-Precision SSM-Transformer Models
Jason Kong, Nilesh Prasad Pandey, Flavio Ponzina +1
Deploying Large Language Models (LLMs) on edge devices faces severe computational and memory constraints, limiting real-time processing and on-device intelligence. Hybrid architect…
GEN-Graph: Heterogeneous PIM Accelerator for General Computational Patterns in Graph-based Dynamic Programming
Yanru Chen, Runyang Tian, Zheyu Li +3
While graph-based dynamic programming (DP) is a cornerstone of genomics and network analytics, its efficiency is hampered by fundamentally conflicting computational patterns. Matri…
INTARG: Informed Real-Time Adversarial Attack Generation for Time-Series Regression
Gamze Kirman Tokgoz, Onat Gungor, Tajana Rosing +1
Time-series forecasting aims to predict future values by modeling temporal dependencies in historical observations. It is a critical component of many real-world systems, where acc…
KLDrive: Fine-Grained 3D Scene Reasoning for Autonomous Driving based on Knowledge Graph
Ye Tian, Jingyi Zhang, Zihao Wang +4
Autonomous driving requires reliable reasoning over fine-grained 3D scene facts. Fine-grained question answering over multi-modal driving observations provides a natural way to eva…
ACORN-IDS: Adaptive Continual Novelty Detection for Intrusion Detection Systems
Sean Fuhrman, Onat Gungor, Tajana Rosing
Intrusion Detection Systems (IDS) must maintain reliable detection performance under rapidly evolving benign traffic patterns and the continual emergence of cyberattacks, including…
QMC: Efficient SLM Edge Inference via Outlier-Aware Quantization and Emergent Memories Co-Design
Nilesh Prasad Pandey, Jangseon Park, Onat Gungor +2
Deploying Small Language Models (SLMs) on edge platforms is critical for real-time, privacy-sensitive generative AI, yet constrained by memory, latency, and energy budgets. Quantiz…
FaTRQ: Tiered Residual Quantization for LLM Vector Search in Far-Memory-Aware ANNS Systems
Tianqi Zhang, Flavio Ponzina, Tajana Rosing
Approximate Nearest-Neighbor Search (ANNS) is a key technique in retrieval-augmented generation (RAG), enabling rapid identification of the most relevant high-dimensional embedding…
SpANNS: Optimizing Approximate Nearest Neighbor Search for Sparse Vectors Using Near Memory Processing
Tianqi Zhang, Flavio Ponzina, Tajana Rosing
Approximate Nearest Neighbor Search (ANNS) is a fundamental operation in vector databases, enabling efficient similarity search in high-dimensional spaces. While dense ANNS has bee…
Fast and Accurate Causal Parallel Decoding using Jacobi Forcing
Lanxiang Hu, Siqi Kou, Yichao Fu +5
Multi-token generation has emerged as a promising paradigm for accelerating transformer-based large model inference. Recent efforts primarily explore diffusion Large Language Model…
EAGER: Edge-Aligned LLM Defense for Robust, Efficient, and Accurate Cybersecurity Question Answering
Onat Gungor, Roshan Sood, Jiasheng Zhou +1
Large Language Models (LLMs) are highly effective for cybersecurity question answering (QA) but are difficult to deploy on edge devices due to their size. Quantization reduces memo…
HDDB: Efficient In-Storage SQL Database Search Using Hyperdimensional Computing on Ferroelectric NAND Flash
Quanling Zhao, Yanru Chen, Runyang Tian +6
Hyperdimensional Computing (HDC) encodes information and data into high-dimensional distributed vectors that can be manipulated using simple bitwise operations and similarity searc…
DroneFL: Federated Learning for Multi-UAV Visual Target Tracking
Xiaofan Yu, Yuwei Wu, Katherine Mao +3
Multi-robot target tracking is a fundamental problem that requires coordinated monitoring of dynamic entities in applications such as precision agriculture, environmental monitorin…
AQUA-LLM: Evaluating Accuracy, Quantization, and Adversarial Robustness Trade-offs in LLMs for Cybersecurity Question Answering
Onat Gungor, Roshan Sood, Harold Wang +1
Large Language Models (LLMs) have recently demonstrated strong potential for cybersecurity question answering (QA), supporting decision-making in real-time threat detection and res…
LIGHT-HIDS: A Lightweight and Effective Machine Learning-Based Framework for Robust Host Intrusion Detection
Onat Gungor, Ishaan Kale, Jiasheng Zhou +1
The expansion of edge computing has increased the attack surface, creating an urgent need for robust, real-time machine learning (ML)-based host intrusion detection systems (HIDS)…
SAGE: Sample-Aware Guarding Engine for Robust Intrusion Detection Against Adversarial Attacks
Jing Chen, Onat Gungor, Zhengli Shang +1
The rapid proliferation of the Internet of Things (IoT) continues to expose critical security vulnerabilities, necessitating the development of efficient and robust intrusion detec…
ReLATE+: Unified Framework for Adversarial Attack Detection, Classification, and Resilient Model Selection in Time-Series Classification
Cagla Ipek Kocal, Onat Gungor, Tajana Rosing +1
Minimizing computational overhead in time-series classification, particularly in deep learning models, presents a significant challenge due to the high complexity of model architec…
CITADEL: Continual Anomaly Detection for Enhanced Learning in IoT Intrusion Detection
Elvin Li, Onat Gungor, Zhengli Shang +1
The Internet of Things (IoT), with its high degree of interconnectivity and limited computational resources, is particularly vulnerable to a wide range of cyber threats. Intrusion…
DailyLLM: Context-Aware Activity Log Generation Using Multi-Modal Sensors and LLMs
Ye Tian, Xiaoyuan Ren, Zihao Wang +3
Rich and context-aware activity logs facilitate user behavior analysis and health monitoring, making them a key research focus in ubiquitous computing. The remarkable semantic unde…
DPQ-HD: Post-Training Compression for Ultra-Low Power Hyperdimensional Computing
Nilesh Prasad Pandey, Shriniwas Kulkarni, David Wang +3
Hyperdimensional Computing (HDC) is emerging as a promising approach for edge AI, offering a balance between accuracy and efficiency. However, current HDC-based applications often…
DYNAMITE: Dynamic Defense Selection for Enhancing Machine Learning-based Intrusion Detection Against Adversarial Attacks
Jing Chen, Onat Gungor, Zhengli Shang +2
The rapid proliferation of the Internet of Things (IoT) has introduced substantial security vulnerabilities, highlighting the need for robust Intrusion Detection Systems (IDS). Mac…