Deep learning enhances jamming and defense in wireless communications.
problem Improving wireless communication security against jamming attacks.
method Adversarial machine learning for jamming and a GAN for training data augmentation.
result Deep learning significantly reduces transmitter performance compared to random jamming.
New algorithms secure IoT edge computing from jamming attacks.
problem Secure mobile edge computing in IoT under jamming attacks.
method Online learning tools for developing SAVE-S and SAVE-A algorithms.
result Achieves sublinear regret without extra resources.
DL-based radio signal classification is vulnerable to adversarial attacks.
problem Vulnerability of DL to adversarial attacks in radio signal classification.
method Crafted white-box and universal black-box adversarial attacks.
result Adversarial attacks can reduce classification performance with small perturbations.
Adversarial attacks reduce deep learning beam selection performance in mmWave 5G.
problem Adversarial attacks degrade deep learning-based beam selection in mmWave 5G.
method Generates adversarial perturbations to RSS inputs to manipulate DNN predictions.
result Significant reduction in IA performance due to adversarial perturbations.
Improved sample-efficient learning for non-coherent digital jamming.
problem Learning optimal jamming strategies in non-coherent digital modulation schemes without prior knowledge.
method Introduced a linear bandit algorithm that accounts for action similarities and integrates context features.
result Significantly improved convergence behavior compared to prior art.
Backdoor attacks on DRL-based traffic controllers cause stop-and-go waves or crashes.
problem Vulnerability of DRL-based traffic controllers to machine learning attacks.
method Developed a trigger design methodology based on traffic physics principles.
result Backdoored models can cause stop-and-go traffic waves or AV crashes when triggered.
Paper introduces timing-based adversarial attacks on DRL-based navigation systems.
problem Vulnerability of DRL-based navigation systems to adversarial attacks.
method Timing-based adversarial strategies using physical noise patterns.
result Adversarial timing attacks significantly degrade DRL-based navigation performance.
Black-box attacks on RL agents using temporal information.
problem Vulnerability of RL agents to adversarial samples.
method Sequence-to-sequence models for predicting future actions.
result Adversarial samples can trigger RL agents to misbehave after a delay.
This paper improves radar performance against jammers using RL.
problem Improving radar performance against jammers.
method Reinforcement Learning (RL) with Deep Q-Network (DQN) and Long Short Term Memory (LSTM) networks.
result Softmax operator improves RL algorithm performance.
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…
TinyML models detect RF and cyber threats in spacecraft with low latency.
problem Detecting cyber-RF threats in autonomous spacecraft with low latency.
method Analysis of classical models (RF, LR, SVM, MLP) for latency-accuracy trade-offs.
result Logistic Regression achieves microsecond-level inference with minimal accuracy loss.
Automated vehicles need efficient testing, which this method addresses.
problem Current testing practices are insufficient for automated vehicles.
method Gaussian Process Classification to identify performance boundaries.
result It is feasible to test for challenging scenarios more efficiently.
In this paper, we show an approach to build deep learning algorithms for recognizing signals in distributed fiber optic monitoring and security systems for long perimeters. Synthesizing such detection algorithms poses a non-trivial research and development challenge, because these systems face stringent error (type I a…
CLAMP uses neural manifold packing to improve self-supervised learning.
problem Improving self-supervised learning for vision tasks.
method CLAMP recasts representation learning as a manifold packing problem, introducing a loss function inspired by particle systems.
result CLAMP achieves competitive performance with state-of-the-art models and separates neural manifolds effectively.
We investigate the problem of reliable communication between two legitimate parties over deletion channels under an active eavesdropping (aka jamming) adversarial model. To this goal, we develop a theoretical framework based on probabilistic finite-state automata to define novel encoding and decoding schemes that ensur…
We propose a novel approach for the generation of polyphonic music based on LSTMs. We generate music in two steps. First, a chord LSTM predicts a chord progression based on a chord embedding. A second LSTM then generates polyphonic music from the predicted chord progression. The generated music sounds pleasing and harm…
Study uses neural networks to predict travel times for public transportation.
problem Inaccurate travel time predictions due to road traffic irregularities.
method Developed two neural network models (MLP and LSTM) using OD travel time matrix.
result Both models can make near-accurate predictions, but LSTM is more susceptible to noise.
This paper detects multi-stage Feint Attacks using Bi-RNN and few-shot learning.
problem Detecting multi-stage Feint Attacks due to lack of professional datasets and semantic relationships.
method Fuzzy clustering for attack chain mining, few-shot deep learning, Bi-RNN for feature extraction.
result Accurately detected Feint Attacks using Bi-RNN and few-shot learning.
This paper studies adversarial attacks on Gaussian process bandits.
problem Adversarial attacks on Gaussian process bandits to manipulate optimal function regions.
method Proposes various adversarial attack methods on GP bandits, including white-box and black-box attacks.
result Adversarial attacks can force GP bandits to optima in target regions even with low attack budgets.
Subpopulation attacks poison data to misclassify naturally distributed points.
problem Improving accuracy of machine learning predictions through adversarial data modification.
method Introducing a novel subpopulation attack framework, using influence functions and gradient optimization.
result Subpopulation attacks are effective and stealthy, making them difficult to defend against.
Efficient attacks on DRL models without model access and low computation.
problem Vulnerabilities of DRL models to adversarial attacks.
method Adapting black-box attacks, introducing efficient online sequential attacks, exploring perturbations in environment dynamics, and generating robust physical perturbations.
result Demonstrated the effectiveness of proposed attacks on real-world robots.
Reward-poisoning attacks can force RL agents to learn bad policies, and we categorize and quantify their feasibility.
problem Reward-poisoning attacks can manipulate RL agents to learn undesirable policies.
method Categorize attacks by infinity-norm constraint, provide thresholds for feasibility, and develop adaptive attack strategies.
result Adaptive reward-poisoning attacks can achieve the nefarious policy in polynomial steps, while non-adaptive attacks require exponential steps.
Spanning attack improves black-box attacks with unlabeled data.
problem Query inefficiency in black-box attacks due to high input space dimensionality.
method Proposes spanning attack by constraining adversarial perturbations in a low-dimensional subspace via an auxiliary unlabeled dataset.
result Significantly improves query efficiency of black-box attacks.
Headless attacks bypass classification heads to fool transfer learning models.
problem Adversarial attacks against transfer learning models without access to the classification head.
method Label-blind adversarial attacks that do not require class-label information.
result Transfer attack lowers ResNet18 accuracy on CIFAR10 by over 40%.
New attack manipulates UCB algorithm, new defense algorithm reduces pseudo-regret.
problem Adversarial attacks on stochastic bandit algorithms.
method Introducing action-manipulation attacks and proposing a robust defense algorithm.
result Proposed defense algorithm reduces pseudo-regret to O(max{log T, A}).
Adversarial attacks pose a threat to deep neural networks, especially in safety-critical applications.
problem Adversarial attacks can misclassify deep neural networks, leading to safety issues.
method Adversarial attacks are categorized into white-box and black-box attacks based on the attacker's knowledge. They can be targeted or non-targeted.
result Adversarial attacks are effective and can transfer between different models and real-world scenarios.
This paper explores evasion attacks against Bayesian models.
problem Bayesian predictive models are vulnerable to evasion attacks.
method Developed gradient-based attacks for specific point predictions and entire posterior distributions.
result Optimal evasion attacks can be designed against Bayesian models.
A new Frank-Wolfe framework improves efficiency and effectiveness of adversarial attacks.
problem Develop efficient and effective optimization-based adversarial attack algorithms.
method Proposes a Frank-Wolfe algorithm variant for both white-box and black-box adversarial attacks.
result Demonstrates improved efficiency and effectiveness compared to existing methods.
A new adversarial attack improves model perturbation efficiency.
problem Improving adversarial attacks to better perturb images.
method LogBarrier method for solving constrained minimization problem.
result LogBarrier attack performs better on challenging images.
Adversarial attacks hide cyber-physical attacks in ICS.
problem Hiding cyber-physical attacks in industrial control systems.
method Modeling an attacker compromising sensors, manipulating data, and evaluating attacks on both continuous and mixed data.
result Successfully hides cyber-physical attacks with 2.87 out of 12 sensors compromised on average.
New approach deflects adversarial attacks by causing them to resemble target classes.
problem Ongoing cycle of stronger defenses being broken by more advanced attacks.
method Combines three detection mechanisms in Capsule Networks to achieve state-of-the-art performance on both standard and defense-aware attacks. Uses human study to show attacks can no longer be called adversarial.
result Attack images can no longer be called adversarial because they are classified the same way as humans do.
This paper optimizes attacks on reinforcement learning policies, reducing their effectiveness.
problem Optimizing adversarial attacks on reinforcement learning policies to minimize rewards.
method Designing optimal attacks for both white-box and black-box scenarios using Markov Decision Processes and Reinforcement Learning.
result Optimal attacks can reduce the effectiveness of reinforcement learning policies, especially for smooth policies.
New defense method against physical attacks on image classification models.
problem Defending against physically realizable attacks on image classification models.
method Proposed a new abstract adversarial model, rectangular occlusion attacks, and developed two approaches for efficiently computing adversarial examples.
result Adversarial training using the new attack yields robust image classification models against physical attacks.
RayS attack improves hard-label adversarial attacks by reducing query complexity and identifying false robust models.
problem Challenges in hard-label adversarial attacks, especially in terms of effectiveness and efficiency.
method Reformulates continuous problem into discrete problem without gradient estimation and uses a fast check step to eliminate unnecessary searches.
result Significantly reduces the number of queries needed for hard-label attacks and identifies false robust models.
A structured approach to generating adversarial attacks for ML systems.
problem Vulnerability of ML systems to adversarial perturbations.
method Developed an 'attack generator' to systematically create adversarial attacks.
result Summarized and extended existing adversarial perturbation taxonomies.
New attacks on RL agents' action space improve understanding of cyber-physical systems vulnerabilities.
problem Understanding and improving the robustness of RL agents in CPS against action space attacks.
method Proposed white-box MAS and LAS attack algorithms to optimize and temporally couple attack budgets.
result LAS attacks cause significantly more performance degradation than MAS attacks.
New method handles uncertainty in adversarial attacks using ensemble noise simulation.
problem Uncertainty in adversarial attacks on neural networks.
method Simulates attacker's noisy perturbation using various gradient-based attack algorithms and a pre-processing Denoising Autoencoder (DAE) defense.
result Significant improvements in post-attack accuracy with the proposed ensemble-trained defense.
This paper investigates why adversarial attacks can be effective against different models.
problem Understanding why adversarial attacks transfer between models.
method Developed a unifying optimization framework and formal definition for attack transferability.
result Identified two main factors: target model's vulnerability and surrogate model complexity.
New co-membership attack method for deep generative models.
problem Check if instances were used in training data.
method Optimize attacker network to reproduce instances and use reconstruction error.
result Co-membership attacks are more powerful than single attacks.
New attacks reveal membership in label-only ML models.
problem Vulnerability of ML models to membership inference attacks.
method Developed decision-based membership inference attacks.
result Label-only exposures are vulnerable to membership leakage.
Paper creates universal adversarial attacks.
problem Creating universal, transferable, and targeted adversarial attacks.
method Learn a universal mapping to map sources to adversarial examples.
result Examples can fool networks into classifying all into one targeted class and have strong transferability.
Defends against ML inference attacks using adversarial examples.
problem Automated inference attacks using ML classifiers pose privacy and security threats.
method Turns ML classifier vulnerabilities into defenses by adding adversarial noise to public data.
result Adversarial examples can mislead ML classifiers and protect private data.
This paper proposes multi-view attack strategies for deep models.
problem Vulnerability of multi-view deep models to adversarial attacks.
method Two multi-view attack strategies: two-stage attack (TSA) and end-to-end attack (ETEA).
result Proposed multi-view attack strategies are effective on multi-view deep models.
New attacks can infer model training membership using only label predictions, not confidence.
problem Inferring whether a data point was used to train a machine learning model.
method Evaluate model's predicted labels under perturbations to infer membership.
result Label-only attacks perform as well as confidence-based attacks and break defenses that rely on confidence masking.
Paper studies attacks on bandit algorithms and shows how attackers can manipulate data to hijack behavior.
problem Potential attacks on bandit algorithms can cause catastrophic loss in real-world applications.
method Proposes a framework of offline and online attacks on bandit algorithms using convex optimization and adaptive strategies.
result Attackers can force bandit algorithms to pull target arms with high probability by manipulating data.
Efficiently attacks large-scale graphs without using the whole graph.
problem Vulnerability of graph neural networks to adversarial attacks.
method Simplified Gradient-based Attack (SGA) method for large-scale graphs.
result SGA achieves significant time and memory efficiency improvements.
Pixle attacks images by rearranging pixels, bypassing neural networks.
problem Vulnerability of neural networks to black-box adversarial attacks.
method A novel attack that rearranges a small number of pixels in images.
result Successfully attacks a high percentage of samples on various datasets and models.
Adversarial training makes models more vulnerable to privacy attacks.
problem Privacy attacks on robust models become feasible.
method Demonstrated through model inversion attacks on robustly trained models.
result Privacy attacks on robust models are now feasible.