2 citations · 2 across the 28 of their papers we have counts for
18 papers · 1 filter
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…
Client Selection in Federated Learning with Data Heterogeneity and Network Latencies
Harsh Vardhan, Xiaofan Yu, Tajana Rosing +1
Federated learning (FL) is a distributed machine learning paradigm where multiple clients conduct local training based on their private data, then the updated models are sent to a…
Offload Rethinking by Cloud Assistance for Efficient Environmental Sound Recognition on LPWANs
Le Zhang, Quanling Zhao, Run Wang +4
Learning-based environmental sound recognition has emerged as a crucial method for ultra-low-power environmental monitoring in biological research and city-scale sensing systems. T…
ReLATE: Resilient Learner Selection for Multivariate Time-Series Classification Against Adversarial Attacks
Cagla Ipek Kocal, Onat Gungor, Aaron Tartz +2
Minimizing computational overhead in time-series classification, particularly in deep learning models, presents a significant challenge. This challenge is further compounded by adv…
TS-OOD: Evaluating Time-Series Out-of-Distribution Detection and Prospective Directions for Progress
Onat Gungor, Amanda Sofie Rios, Nilesh Ahuja +1
Detecting out-of-distribution (OOD) data is a fundamental challenge in the deployment of machine learning models. From a security standpoint, this is particularly important because…
CND-IDS: Continual Novelty Detection for Intrusion Detection Systems
Sean Fuhrman, Onat Gungor, Tajana Rosing
Intrusion detection systems (IDS) play a crucial role in IoT and network security by monitoring system data and alerting to suspicious activities. Machine learning (ML) has emerged…
SAFE: Self-Supervised Anomaly Detection Framework for Intrusion Detection
Elvin Li, Zhengli Shang, Onat Gungor +1
The proliferation of IoT devices has significantly increased network vulnerabilities, creating an urgent need for effective Intrusion Detection Systems (IDS). Machine Learning-base…