9 citations · 15 across the 25 of their papers we have counts for
26 papers
Synthesis Without Training: An Inference-Only Pipeline for Tabular, Temporal, and Relational Synthetic Data
Zilong Zhao, Abdul Raheem, Jiayu Li +5
Synthetic data generation is dominated by the fit-then-sample paradigm: a generative model is trained on a private dataset and then sampled from. Despite its widespread adoption, t…
CDN Tsunami: Exploiting HTTP/3-HTTP/1.1 Conversion for DoS Attacks
Ziyu Lin, Tianlong Su, Yingjie Lin +4
Content Delivery Networks (CDNs) provide high availability, accelerate content delivery for their host websites, but are also vulnerable to different types of Denial-of-Service (Do…
Fed-GAME: Personalized Federated Learning with Graph Attention Mixture-of-Experts For Time-Series Forecasting
Yi Li, Han Liu, Mingfeng Fan +3
Federated learning (FL) on graphs shows promise for distributed time-series forecasting. Yet, existing methods rely on static topologies and struggle with client heterogeneity. We…
PEANUT: Perturbations by Eigenvector Alignment for Attacking Graph Neural Networks Under Topology-Driven Message Passing
Bhavya Kohli, Biplab Sikdar
Message Passing Neural Networks (MPNNs) have achieved strong performance on tasks involving relational data. However, small perturbations to graph structure can significantly alter…
Baiting AI: Deceptive Adversary Against AI-Protected Industrial Infrastructures
Aryan Pasikhani, Prosanta Gope, Yang Yang +2
This paper explores a new cyber-attack vector targeting Industrial Control Systems (ICS), particularly focusing on water treatment facilities. Developing a new multi-agent Deep Rei…
Unified Framework for Qualifying Security Boundary of PUFs Against Machine Learning Attacks
Hongming Fei, Zilong Hu, Prosanta Gope +1
Physical Unclonable Functions (PUFs) serve as lightweight, hardware-intrinsic entropy sources widely deployed in IoT security applications. However, delay-based PUFs are vulnerable…
Towards Irreversible Machine Unlearning for Diffusion Models
Xun Yuan, Zilong Zhao, Jiayu Li +3
Diffusion models are renowned for their state-of-the-art performance in generating synthetic images. However, concerns related to safety, privacy, and copyright highlight the need…
Instruction Tuning of Large Language Models for Tabular Data Generation-in One Day
Milad Abdollahzadeh, Abdul Raheem, Zilong Zhao +5
Tabular instruction tuning has emerged as a promising research direction for improving LLMs understanding of tabular data. However, the majority of existing works only consider que…
When Smiley Turns Hostile: Interpreting How Emojis Trigger LLMs' Toxicity
Shiyao Cui, Xijia Feng, Yingkang Wang +6
Emojis are globally used non-verbal cues in digital communication, and extensive research has examined how large language models (LLMs) understand and utilize emojis across context…
VulCPE: Context-Aware Cybersecurity Vulnerability Retrieval and Management
Yuning Jiang, Feiyang Shang, Freedy Tan Wei You +6
The dynamic landscape of cybersecurity demands precise and scalable solutions for vulnerability management in heterogeneous systems, where configuration-specific vulnerabilities ar…
VulRG: Multi-Level Explainable Vulnerability Patch Ranking for Complex Systems Using Graphs
Yuning Jiang, Nay Oo, Qiaoran Meng +2
As interconnected systems proliferate, safeguarding complex infrastructures against an escalating array of cyber threats has become an urgent challenge. The increasing number of vu…
A Survey on Vulnerability Prioritization: Taxonomy, Metrics, and Research Challenges
Yuning Jiang, Nay Oo, Qiaoran Meng +2
In the highly interconnected digital landscape of today, safeguarding complex infrastructures against cyber threats has become increasingly challenging due to the exponential growt…
MITRE ATT&CK Applications in Cybersecurity and The Way Forward
Yuning Jiang, Qiaoran Meng, Feiyang Shang +4
The MITRE ATT&CK framework is a widely adopted tool for enhancing cybersecurity, supporting threat intelligence, incident response, attack modeling, and vulnerability prioritizatio…
TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer
Jiayu Li, Bingyin Zhao, Zilong Zhao +3
Transformers have shown impressive results in tabular data generation. However, they lack domain-specific inductive biases which are critical for preserving the intrinsic character…
UIBDiffusion: Universal Imperceptible Backdoor Attack for Diffusion Models
Yuning Han, Bingyin Zhao, Rui Chu +3
Recent studies show that diffusion models (DMs) are vulnerable to backdoor attacks. Existing backdoor attacks impose unconcealed triggers (e.g., a gray box and eyeglasses) that con…
Privacy-preserving Robotic-based Multi-factor Authentication Scheme for Secure Automated Delivery System
Yang Yang, Aryan Mohammadi Pasikhani, Prosanta Gope +1
Package delivery is a critical aspect of various industries, but it often incurs high financial costs and inefficiencies when relying solely on human resources. The last-mile trans…
Leveraging A New GAN-based Transformer with ECDH Crypto-system for Enhancing Energy Theft Detection in Smart Grid
Yang Yang, Xun Yuan, Arwa Alromih +3
Detecting energy theft is vital for effectively managing power grids, as it ensures precise billing and prevents financial losses. Split-learning emerges as a promising decentraliz…
TAEGAN: Generating Synthetic Tabular Data For Data Augmentation
Jiayu Li, Zilong Zhao, Kevin Yee +2
Synthetic tabular data generation has gained significant attention for its potential in data augmentation and privacy-preserving data sharing. While recent methods like diffusion a…
CombU: A Combined Unit Activation for Fitting Mathematical Expressions with Neural Networks
Jiayu Li, Zilong Zhao, Kevin Yee +2
The activation functions are fundamental to neural networks as they introduce non-linearity into data relationships, thereby enabling deep networks to approximate complex data rela…
VFLGAN-TS: Vertical Federated Learning-based Generative Adversarial Networks for Publication of Vertically Partitioned Time-Series Data
Xun Yuan, Zilong Zhao, Prosanta Gope +1
In the current artificial intelligence (AI) era, the scale and quality of the dataset play a crucial role in training a high-quality AI model. However, often original data cannot b…
Swarm-Net: Firmware Attestation in IoT Swarms using Graph Neural Networks and Volatile Memory
Varun Kohli, Bhavya Kohli, Muhammad Naveed Aman +1
The Internet of Things (IoT) is a network of billions of interconnected, primarily low-end embedded devices. Despite large-scale deployment, studies have highlighted critical secur…
VFLGAN: Vertical Federated Learning-based Generative Adversarial Network for Vertically Partitioned Data Publication
Xun Yuan, Yang Yang, Prosanta Gope +2
In the current artificial intelligence (AI) era, the scale and quality of the dataset play a crucial role in training a high-quality AI model. However, good data is not a free lunc…
PhenoAuth: A Novel PUF-Phenotype-based Authentication Protocol for IoT Devices
Hongming Fei, Owen Millwood, Gope Prosanta +2
Physical Unclonable Functions (PUFs) have been shown to be a highly promising solution for enabling high security systems tailored for low-power devices. Commonly, PUFs are utilise…
Privacy-Preserving Collaborative Split Learning Framework for Smart Grid Load Forecasting
Asif Iqbal, Prosanta Gope, Biplab Sikdar
Accurate load forecasting is crucial for energy management, infrastructure planning, and demand-supply balancing. Smart meter data availability has led to the demand for sensor-bas…