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Found 16 papers, 4 papers with code
Genetic Algorithm-Based Dynamic Backdoor Attack on Federated Learning-Based Network Traffic Classification
In this paper, we propose GABAttack, a novel genetic algorithm-based backdoor attack against federated learning for network traffic classification.
Multi-Instance Adversarial Attack on GNN-Based Malicious Domain Detection
In this work, we call the attack that manipulates several nodes in the DMG concurrently a multi-instance evasion attack.
Semi-decentralized Inference in Heterogeneous Graph Neural Networks for Traffic Demand Forecasting: An Edge-Computing Approach
This approach minimizes inter-cloudlet communication thereby alleviating the communication overhead of the decentralized approach while promoting scalability due to cloudlet-level decentralization.
Heterogeneous Randomized Response for Differential Privacy in Graph Neural Networks
Graph neural networks (GNNs) are susceptible to privacy inference attacks (PIAs), given their ability to learn joint representation from features and edges among nodes in graph data.
An Adaptive Black-box Defense against Trojan Attacks (TrojDef)
Through mathematical analysis, we show that if the attacker is perfect in injecting the backdoor, the Trojan infected model will be trained to learn the appropriate prediction confidence bound, which is used to distinguish Trojan and benign inputs under arbitrary perturbations.
How to Backdoor HyperNetwork in Personalized Federated Learning?
This paper explores previously unknown backdoor risks in HyperNet-based personalized federated learning (HyperNetFL) through poisoning attacks.
Adversarial Attacks and Defenses for Social Network Text Processing Applications: Techniques, Challenges and Future Research Directions
These vulnerabilities allow adversaries to launch a diversified set of adversarial attacks on these algorithms in different applications of social media text processing.
Smart Traffic Monitoring System using Computer Vision and Edge Computing
In this paper, we focus on two common traffic monitoring tasks, congestion detection, and speed detection, and propose a two-tier edge computing based model that takes into account of both the limited computing capability in cloudlets and the unstable network condition to the TMC.
A Synergetic Attack against Neural Network Classifiers combining Backdoor and Adversarial Examples
In this work, we show how to jointly exploit adversarial perturbation and model poisoning vulnerabilities to practically launch a new stealthy attack, dubbed AdvTrojan.
Trojans and Adversarial Examples: A Lethal Combination
In this work, we naturally unify adversarial examples and Trojan backdoors into a new stealthy attack, that is activated only when 1) adversarial perturbation is injected into the input examples and 2) a Trojan backdoor is used to poison the training process simultaneously.
ManiGen: A Manifold Aided Black-box Generator of Adversarial Examples
Through extensive set of experiments on different datasets, we show that (1) adversarial examples generated by ManiGen can mislead standalone classifiers by being as successful as the state-of-the-art white-box generator, Carlini, and (2) adversarial examples generated by ManiGen can more effectively attack classifiers with state-of-the-art defenses.
Using Single-Step Adversarial Training to Defend Iterative Adversarial Examples
Single-Step adversarial training methods have been proposed as computationally viable solutions, however they still fail to defend against iterative adversarial examples.
Using Intuition from Empirical Properties to Simplify Adversarial Training Defense
Due to the surprisingly good representation power of complex distributions, neural network (NN) classifiers are widely used in many tasks which include natural language processing, computer vision and cyber security.
ZK-GanDef: A GAN based Zero Knowledge Adversarial Training Defense for Neural Networks
Neural Network classifiers have been used successfully in a wide range of applications.
GanDef: A GAN based Adversarial Training Defense for Neural Network Classifier
Machine learning models, especially neural network (NN) classifiers, are widely used in many applications including natural language processing, computer vision and cybersecurity.
Indoor Localization Using Visible Light Via Fusion Of Multiple Classifiers
A multiple classifiers fusion localization technique using received signal strengths (RSSs) of visible light is proposed, in which the proposed system transmits different intensity modulated sinusoidal signals by LEDs and the signals received by a Photo Diode (PD) placed at various grid points.
