We provide a methodology, resilient feature engineering, for creating adversarially resilient classifiers. According to existing work, adversarial attacks identify weakly correlated or non-predictive features learned by the classifier during training and design the adversarial noise to utilize these features. Therefore…
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48 results for “Adversarial Resilience”
Paper benchmarks DRL policies' resilience to state transitions.
problem Measuring DRL policies' resilience to state perturbations.
method Disentangled representation learning and RL-based techniques.
result Demonstrated feasibility of resilience benchmarking in DQN, A2C, and PPO2.
Game-Theoretic Design of Secure and Resilient Distributed Support Vector Machines with Adversariesstat.ML
With a large number of sensors and control units in networked systems, distributed support vector machines (DSVMs) play a fundamental role in scalable and efficient multi-sensor classification and prediction tasks. However, DSVMs are vulnerable to adversaries who can modify and generate data to deceive the system to mi…
The resilience of low-degree Rademacher chaos is studied, providing probabilistic lower bounds.
problem Understanding how much a Rademacher chaos can withstand adversarial sign-flips without significant probability changes.
method Probabilistic lower-bound guarantees for the resilience of Rademacher chaos of arbitrary degree.
result Probabilistic lower-bound guarantees for the resilience of Rademacher chaos of arbitrary degree, especially meaningful for constant degree.
This paper defines resilience in knowledge graph embeddings and surveys existing works.
problem Challenges in knowledge graph embedding models, including noise, missing information, distribution shift, and adversarial attacks.
method Unified definition of resilience, formalization in the context of knowledge graphs, systematic survey of existing works.
result Most existing works focus on robustness, leaving other aspects of resilience unexplored.
New method creates powerful, transferable adversarial attacks for deep learning networks.
problem Vulnerability of deep learning networks to adversarial attacks.
method Developed a method to generate strong, transferable adversarial attacks.
result Demonstrated that the proposed method can easily transfer strong adversarial attacks between different models.
RESIST improves decentralized learning resilience against MITM attacks.
problem Decentralized learning's vulnerability to MITM attacks.
method Multistep consensus gradient descent framework with robust statistics-based screening methods.
result Achieves algorithmic and statistical convergence for various ERM problems.
Deep Neural Networks are built to generalize outside of training set in mind by using techniques such as regularization, early stopping and dropout. But considerations to make them more resilient to adversarial examples are rarely taken. As deep neural networks become more prevalent in mission-critical and real-time sy…
Enhances deep learning models' robustness against adversarial attacks.
problem Lack of reliable uncertainty estimates and robust defenses for deep learning models.
method Integrates Conformal Prediction principles with adversarial training.
result Introduces OPSA-AT, a defense strategy that enhances robustness and reliability.
Secure federated learning framework resists adversarial users.
problem Resilience against adversarial (Byzantine) users in federated learning.
method Integrated stochastic quantization, verifiable outlier detection, and secure model aggregation.
result First single-server Byzantine-resilient secure aggregation framework (BREA) for secure federated learning.
Bayesian explanations are more resilient to adversarial attacks than deterministic ones.
problem Stability of saliency-based explanations under adversarial attacks in Neural Networks.
method Empirical and theoretical analysis of Bayesian vs deterministic Neural Networks.
result Bayesian explanations are more stable under adversarial perturbations and direct attacks.
Distributed Support Vector Machines (DSVM) have been developed to solve large-scale classification problems in networked systems with a large number of sensors and control units. However, the systems become more vulnerable as detection and defense are increasingly difficult and expensive. This work aims to develop secu…
Researchers develop PAIN to improve self-driving safety through adversarial training.
problem Overfitting and poor generalizability of neural networks in self-driving vehicles.
method PAIN combines adversarial training in CARLA simulation to generate edge cases.
result Trained self-driving vehicles are more resilient to environmental uncertainty and less prone to collisions.
Reprogram deep models to resist adversarial attacks without changing parameters.
problem Improving deep learning models' robustness against adversarial and noisy inputs.
method Proposes a non-linear robust pattern matching technique and three reprogramming paradigms.
result Demonstrates effective reprogramming of deep models for robustness without altering parameters.
Study improves resilience against adversarial clean-label attacks in real and noisy settings.
problem Ensuring accurate predictions in the presence of adversarial clean-label samples.
method Sequential learning from a stream of i.i.d. data, allowing abstention for uncertain predictions.
result Theoretical analysis and adaptations for the agnostic setting with a clean-label adversary and noise.
We show that when a third party, the adversary, steps into the two-party setting (agent and operator) of safely interruptible reinforcement learning, a trade-off has to be made between the probability of following the optimal policy in the limit, and the probability of escaping a dangerous situation created by the adve…
Generalizes robust statistics to various perturbations under Wasserstein distance.
problem Robust statistics for datasets corrupted by various perturbations.
method Generalizes robust statistics to any Wasserstein distance, showing robust estimation under certain perturbations.
result Generalized resilience property holds under moment or hypercontractive conditions, simplifying and improving known results.
Adversarial examples have been shown to exist for a variety of deep learning architectures. Deep reinforcement learning has shown promising results on training agent policies directly on raw inputs such as image pixels. In this paper we present a novel study into adversarial attacks on deep reinforcement learning polic…
RS-DQN protects RL agents from adversarial attacks.
problem Adversarial attacks can disrupt deep RL training and evaluation.
method Online robustness training with RS-DQN combining state-of-the-art adversarial and provably robust training.
result RS-DQN makes RL agents resilient to strong attacks.
CEB enhances model resilience through simple entropy bottleneck.
problem Improving model robustness against adversarial attacks.
method Conditional Entropy Bottleneck (CEB) combined with data augmentation.
result CEB significantly boosts adversarial robustness on various benchmarks.
New method makes neural networks more resilient to location-optimized adversarial patches.
problem Neural networks' vulnerability to adversarial patches that are visible but still effective.
method Developed a practical approach to optimize patch locations and applied adversarial training.
result Significantly improved robustness against adversarial patches on CIFAR10 and GTSRB.
Adversarial training improves graph autoencoder generalization.
problem Improving graph autoencoder generalization.
method Formulated L2 and L1 adversarial training for graph autoencoders and variational graph autoencoders.
result Adversarial training boosts graph autoencoder and variational graph autoencoder generalization.
Adversarial audio attacks can be considered as a small perturbation unperceptive to human ears that is intentionally added to the audio signal and causes a machine learning model to make mistakes. This poses a security concern about the safety of machine learning models since the adversarial attacks can fool such model…
Heavy-tailed outliers are more resilient to robust estimation than adversarial ones.
problem Developing robust estimators for data with outliers.
method Analyzing the relationship between adversarial and heavy-tailed outlier models.
result Optimal estimators for heavy-tailed outliers are also optimal for adversarial settings, but not vice versa.
Paper robustifies reinforcement learning agents against action space perturbations.
problem Vulnerability of reinforcement learning agents to action space perturbations (e.g. actuator attacks).
method Adversarial training to robustify DRL agents against perturbations.
result DRL agents can be robustified against action space perturbations through adversarial training.
There has been a surge of interest in using machine learning (ML) to automatically detect malware through their dynamic behaviors. These approaches have achieved significant improvement in detection rates and lower false positive rates at large scale compared with traditional malware analysis methods. ML in threat dete…
Adversarial examples are maliciously tweaked images that can easily fool machine learning techniques, such as neural networks, but they are normally not visually distinguishable for human beings. One of the main approaches to solve this problem is to retrain the networks using those adversarial examples, namely adversa…
GNNs robustness in community detection is studied with various perturbations.
problem Understanding GNNs robustness in community detection tasks.
method Systematic computational evaluation of six GNN architectures on synthetic and real-world networks.
result Supervised GNNs achieve higher baseline accuracy, while DMoN shows stronger resilience to perturbations.
Applications in machine learning, optimization, and control require the sequential selection of a few system elements, such as sensors, data, or actuators, to optimize the system performance across multiple time steps. However, in failure-prone and adversarial environments, sensors get attacked, data get deleted, and a…
Byzantine-resilient federated learning with local iterations and robust mean estimation.
problem Byzantine clients disrupt federated learning with local iterations.
method Local SGD iterations, robust mean estimation, and matrix concentration result.
result Convergence analysis for strongly-convex and non-convex smooth objectives in heterogeneous data settings.
Applications of safety, security, and rescue in robotics, such as multi-robot target tracking, involve the execution of information acquisition tasks by teams of mobile robots. However, in failure-prone or adversarial environments, robots get attacked, their communication channels get jammed, and their sensors may fail…
Adversarial learning is one of the most successful approaches to modelling high-dimensional probability distributions from data. The quantum computing community has recently begun to generalize this idea and to look for potential applications. In this work, we derive an adversarial algorithm for the problem of approxim…
Evaluation of Adversarial Training on Different Types of Neural Networks in Deep Learning-based IDSscs.LG
Study evaluates adversarial training for deep learning IDSs against various attacks.
problem Evasion attacks against deep learning-based IDSs.
method Investigated adversarial training using min-max approach on CNN and RNN.
result Adversarial training improves robustness against five attack methods.
Nowadays more and more data are gathered for detecting and preventing cyber attacks. In cyber security applications, data analytics techniques have to deal with active adversaries that try to deceive the data analytics models and avoid being detected. The existence of such adversarial behavior motivates the development…
ARL makes market makers resilient to adversarial conditions.
problem Creating resilient market makers against adversarial attacks.
method Transformed single-agent RL into a zero-sum game between market maker and adversary.
result ARL leads to risk-averse behavior and significant performance improvements.
Paper introduces a new index to measure financial and workplace resilience of firms.
problem Corporate resilience and its types in turbulent markets.
method Quantitative analysis of earnings expectations and implied discount rates.
result Evidence of workplace resilience amplification by financial status in the COVID-19 era.
Paper studies autoencoder-based anomaly detectors' robustness to adversarial poisoning attacks.
problem Adversarial poisoning attacks on online-trained autoencoder-based anomaly detectors in ICSs.
method Proposes two algorithms for generating poison samples and evaluates them on synthetic and real-world ICS data.
result Autoencoder detectors are resilient to poisoning in the face of all relevant attacks in the SWaT dataset.
Investigating Resistance of Deep Learning-based IDS against Adversaries using min-max Optimizationcs.LG
Study on deep learning IDS resistance against adversarial attacks.
problem Vulnerabilities in deep learning-based IDS against adversarial attacks.
method Apply min-max optimization to train IDS against adversarial samples.
result Adversarial attack methods can be used in continuous domains and boost IDS robustness.
PRISM-FCP improves federated prediction robustness against Byzantine attacks.
problem Byzantine attacks in federated learning.
method Partial model sharing and distance-based maliciousness scores.
result Maintains nominal coverage guarantees under Byzantine attacks.
Measures financial resilience using BSDEs and their properties.
problem Measuring financial resilience in dynamic risk environments.
method Developed stochastic calculus for BSDEs with jumps, revealing resilience rate as expectation of generator.
result Resilience rate can be represented as expectation of BSDE generator, revealing properties of dynamic risk measures.
Time series classification models have been garnering significant importance in the research community. However, not much research has been done on generating adversarial samples for these models. These adversarial samples can become a security concern. In this paper, we propose utilizing an adversarial transformation …
Machine Learning (ML) and Deep Learning (DL) models have achieved state-of-the-art performance on multiple learning tasks, from vision to natural language modelling. With the growing adoption of ML and DL to many areas of computer science, recent research has also started focusing on the security properties of these mo…
A new model for sequential prediction handles adversarial examples by allowing abstention.
problem Sequential prediction algorithms fail with adversarial examples, leading to incorrect predictions.
method Proposes a new model that allows abstention from predictions on adversarial examples, scaling error with VC dimension.
result A learner's error scales with the VC dimension of the hypothesis class, matching the stochastic setting.
While machine learning is going through an era of celebrated success, concerns have been raised about the vulnerability of its backbone: stochastic gradient descent (SGD). Recent approaches have been proposed to ensure the robustness of distributed SGD against adversarial (Byzantine) workers sending poisoned gradients …
Python tool assesses European agricultural production resilience.
problem Estimating resilience of European agricultural production systems.
method Python software to compute annual production resilience indicator.
result Demonstrates resilience of European agricultural production.
RILA learns HQMMs robustly against adversarial corruption.
problem Robustness of HQMM learning algorithms under adversarial perturbations.
method Adversarially Corrupted HQMM (AC-HQMM) and Robust Iterative Learning Algorithm (RILA).
result RILA outperforms existing algorithms in convergence stability, corruption resilience, and physical validity.
This paper measures financial market resilience in China and identifies key uncertainties.
problem Measuring financial market resilience in China.
method Quantitative analysis of total financial market and sub-markets, Diebold-Yilmaz connectedness approach.
result Financial market resilience in China is event-driven and influenced by geopolitical risks, economic and trade policy uncertainty, and U.S.-China tensions.
AI attacks threaten insurance systems, requiring new defenses.
problem Adversarial attacks on AI in insurance.
method Categorize and discuss various types of attacks and defense methods.
result Need for improved AI systems to resist attacks.