Search Results for author:
Found 21 papers, 13 papers with code
A Tale of Evil Twins: Adversarial Inputs versus Poisoned Models
Specifically, (i) we develop a new attack model that jointly optimizes adversarial inputs and poisoned models; (ii) with both analytical and empirical evidence, we reveal that there exist intriguing "mutual reinforcement" effects between the two attack vectors -- leveraging one vector significantly amplifies the effectiveness of the other; (iii) we demonstrate that such effects enable a large design spectrum for the adversary to enhance the existing attacks that exploit both vectors (e. g., backdoor attacks), such as maximizing the attack evasiveness with respect to various detection methods; (iv) finally, we discuss potential countermeasures against such optimized attacks and their technical challenges, pointing to several promising research directions.
AdvMind: Inferring Adversary Intent of Black-Box Attacks
Deep neural networks (DNNs) are inherently susceptible to adversarial attacks even under black-box settings, in which the adversary only has query access to the target models.
TrojanZoo: Towards Unified, Holistic, and Practical Evaluation of Neural Backdoors
To bridge this gap, we design and implement TROJANZOO, the first open-source platform for evaluating neural backdoor attacks/defenses in a unified, holistic, and practical manner.
TFE-GNN: A Temporal Fusion Encoder Using Graph Neural Networks for Fine-grained Encrypted Traffic Classification
Encrypted traffic classification is receiving widespread attention from researchers and industrial companies.
A Fine-grained Chinese Software Privacy Policy Dataset for Sequence Labeling and Regulation Compliant Identification
Privacy protection raises great attention on both legal levels and user awareness.
Programmable In-Network Security for Context-aware BYOD Policies
Recent work has developed SDN solutions to collect device context for network-wide access control in a central controller.
Networking and Internet Architecture Cryptography and Security
On the Security Risks of AutoML
Neural Architecture Search (NAS) represents an emerging machine learning (ML) paradigm that automatically searches for models tailored to given tasks, which greatly simplifies the development of ML systems and propels the trend of ML democratization.
Vehicle Traffic Driven Camera Placement for Better Metropolis Security Surveillance
Security surveillance is one of the most important issues in smart cities, especially in an era of terrorism.
A Sink-driven Approach to Detecting Exposed Component Vulnerabilities in Android Apps
We implement our sink-driven approach in a tool called ECVDetector and evaluate it with the top 1K Android apps.
Cryptography and Security
Beyond the Virus: A First Look at Coronavirus-themed Mobile Malware
To facilitate future research, we have publicly released all the well-labelled COVID-19 themed apps (and malware) to the research community.
Cryptography and Security
BinarizedAttack: Structural Poisoning Attacks to Graph-based Anomaly Detection
Graph-based Anomaly Detection (GAD) is becoming prevalent due to the powerful representation abilities of graphs as well as recent advances in graph mining techniques.
Large Language Models for Software Engineering: A Systematic Literature Review
Nevertheless, a comprehensive understanding of the application, effects, and possible limitations of LLMs on SE is still in its early stages.
On the Security Risks of Knowledge Graph Reasoning
Knowledge graph reasoning (KGR) -- answering complex logical queries over large knowledge graphs -- represents an important artificial intelligence task, entailing a range of applications (e. g., cyber threat hunting).
Interpretable Deep Learning under Fire
The improved interpretability is believed to offer a sense of security by involving human in the decision-making process.
Model-Reuse Attacks on Deep Learning Systems
By empirically studying four deep learning systems (including both individual and ensemble systems) used in skin cancer screening, speech recognition, face verification, and autonomous steering, we show that such attacks are (i) effective - the host systems misbehave on the targeted inputs as desired by the adversary with high probability, (ii) evasive - the malicious models function indistinguishably from their benign counterparts on non-targeted inputs, (iii) elastic - the malicious models remain effective regardless of various system design choices and tuning strategies, and (iv) easy - the adversary needs little prior knowledge about the data used for system tuning or inference.
Cryptography and Security
Boros: Secure Cross-Channel Transfers via Channel Hub
In this paper, we design and develop a novel off-chain system to shorten the routing path for the payment network.
Cryptography and Security
CLUE: Towards Discovering Locked Cryptocurrencies in Ethereum
Because the locked cryptocurrencies can never be controlled by users, avoid interacting with the accounts discovered by CLUE and repeating the same mistakes again can help users to save money.
Cryptography and Security
Smart Contract Security: a Practitioners' Perspective
Given numerous research efforts in addressing the security issues of smart contracts, we wondered how software practitioners build security into smart contracts in practice.
Software Engineering
FocusedCleaner: Sanitizing Poisoned Graphs for Robust GNN-based Node Classification
Graph Neural Networks (GNNs) are vulnerable to data poisoning attacks, which will generate a poisoned graph as the input to the GNN models.
Graph Anomaly Detection at Group Level: A Topology Pattern Enhanced Unsupervised Approach
Graph anomaly detection (GAD) has achieved success and has been widely applied in various domains, such as fraud detection, cybersecurity, finance security, and biochemistry.
Towards Function Space Mesh Watermarking: Protecting the Copyright of Signed Distance Fields
Our method can recover the message with high-resolution meshes extracted from SDFs and detect the watermark even when mesh vertices are extremely sparse.
