Paper proposes RL for real-time smart grid cyber attack detection.
problem Real-time detection of cyber-attacks in smart grids.
method Formulated as POMDP, uses model-free reinforcement learning.
result Effective in timely and accurate detection of cyber-attacks.
Paper extends method to detect reverse engineering attacks on DNN classifiers.
problem Detecting reverse engineering attacks on deep neural network image classifiers.
method Extend ADA approach to detect RE attacks (ADA-RE).
result Successfully detects 'stealthy' RE attacks before they launch test-time evasion attacks.
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.
A new method uses adversarial attacks to detect other adversarial attacks.
problem Detecting iterative adversarial attacks on deep neural networks.
method Using the Carlini-Wagner (CW) attack as a detector itself, under certain assumptions.
result The method provides asymptotically optimal separation of original and attacked images.
Adversarial attacks can fool copyright detection systems.
problem Vulnerability of copyright detection systems to adversarial attacks.
method Used gradient methods to create adversarial music that fooled detection systems.
result Adversarial attacks can successfully deceive industrial copyright detection tools.
Study detects and mitigates stealthy DDoS attacks in IoT networks.
problem Stealthy DDoS attacks in IoT networks.
method Anomaly-based IDS for timely detection and mitigation.
result Demonstrated capability of detecting and mitigating small attack size per source.
Study on detecting backdoor attacks using adversarial detection methods.
problem Mitigating backdoor attacks in deep neural networks.
method Comprehensive studies on adversarial and backdoor examples, revising adversarial defense methods.
result Adversarial defense methods can be effective in detecting backdoor attacks.
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.
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.
DoPa detects various physical adversarial attacks on CNNs.
problem Vulnerability of CNNs to physical adversarial attacks.
method Interprets CNN's vulnerability, adds self-verification stage.
result Achieves 90% success rate for image attacks and 92% for audio attacks.
SynGAN generates synthetic network attacks to improve NIDS effectiveness.
problem Low effectiveness of NIDS due to high false positives and lack of continuous testing.
method SynGAN uses GANs to generate adversarial network attacks based on real attack traffic.
result SynGAN-generated attacks improve NIDS detection rates compared to real attacks.
Paper presents attacks on real-time object detection systems.
problem Adversarial attacks on real-time object detection systems.
method Three targeted adversarial Objectness Gradient attacks (TOG).
result Adversarial attacks can cause object-vanishing, object-fabrication, and object-mislabeling.
AI detects LDDoS attacks by analyzing TCP connection parameters.
problem Detecting low-rate LDDoS attacks that overwhelm server connections.
method AI algorithms trained on simulated and real-world datasets using TCP flow features.
result Decision trees and k-NN achieved high accuracy in classifying attacks, with low false positives and negatives.
PAD offers a principled approach to malware detection against evasion attacks.
problem Machine Learning techniques for malware detection are vulnerable to evasion attacks.
method PAD proposes a new adversarial training framework with convergence guarantees for robust optimization.
result PAD significantly outperforms state-of-the-art defenses and can harden ML-based malware detection against 27 evasion attacks.
Paper proposes a new method to detect node replication attacks in Vehicular Ad-hoc Networks.
problem Node replication attacks in Vehicular Ad-hoc Networks.
method Adapted statistical methodology for detecting node replication attacks.
result New method outperforms state-of-the-art techniques in detecting node replication attacks.
New framework detects and removes malicious model updates in federated learning.
problem Vulnerability of federated learning systems to malicious client attacks.
method Central server learns to detect and remove malicious model updates using a detection model.
result Ensures robust federated learning resilient to Byzantine and targeted model poisoning attacks.
This paper improves machine learning models' robustness against evasion attacks using randomness.
problem Evasion attacks that adapt to machine learning models to avoid detection.
method Incorporates randomness into both training and application phases of machine learning models.
result The proposed randomization-based approach further improves the robustness of machine learning models, especially random forest.
Prior Networks improve adversarial attack detection by estimating uncertainty.
problem Detecting adversarial attacks in AI systems, especially those designed to evade detection methods.
method Prior Networks estimate predictive uncertainty, outperforming Monte-Carlo dropout ensembles.
result Prior Networks significantly outperform baseline approaches in detecting adversarial attacks.
A new system detects audio replay attacks with high accuracy.
problem Detecting and preventing audio replay attacks in speaker verification systems.
method Proposes Attentive Filtering Network combining attention-based filtering and ResNet classifier.
result Achieves EER of 8.99% on ASVspoof 2017 Version 2.0 dataset.
Paper defends against malware detection attacks using clustering and deep learning.
problem Label flipping attacks on malware detection systems in IoT environments.
method Developed a Silhouette clustering-based attack mechanism and two CNN-type deep learning algorithms for defense.
result Proposed algorithms LSD and CSD improve malware detection accuracy by up to 19%.
Machine learning has become an important component for many systems and applications including computer vision, spam filtering, malware and network intrusion detection, among others. Despite the capabilities of machine learning algorithms to extract valuable information from data and produce accurate predictions, it ha…
Improved speaker verification system detects replay attacks.
problem Detecting replay attacks in speaker verification systems.
method Multi-task learning of noise classes (replay noise, recording environment, recording device noises) and spoofing detection.
result Performance improved by 30% on ASVspoof2017 datasets.
Adversarial attacks can fool ML energy theft detection models.
problem Vulnerability of ML-based energy theft detection models to adversarial attacks.
method Design of an adversarial measurement generation algorithm.
result ML models can be significantly fooled by adversarial attacks, reducing their detection accuracy.
Detects backdoors in trained classifiers without access to training data.
problem Post-training detection of backdoor attacks in DNN image classifiers.
method Purely unsupervised anomaly detection (AD) approach.
result Detects whether a classifier has been backdoor-attacked and infers source and target classes.
New approach shows backdoor attacks are indistinguishable from natural data features.
problem Defending against backdoor attacks in machine learning models.
method Developed a new primitive for detecting backdoor attacks based on the assumption that they correspond to the strongest feature in the training data.
result Backdoor attacks are indistinguishable from natural data features, making traditional detection methods ineffective.
A new method detects and corrects adversarial attacks on classifiers.
problem Detecting and correcting adversarial attacks on machine learning models.
method Unsupervised autoencoder trained with KL divergence loss to match predictions.
result Almost completely neutralizes powerful attacks on MNIST and Fashion-MNIST.
This study uses cloud telemetry to detect DoS attacks with machine learning.
problem Detecting DoS attacks in cloud environments is challenging.
method Use of machine learning algorithms on cloud telemetry data.
result k-Nearest Neighbors (kNN) and decision tree (CART) accurately identify DoS attacks.
Defense against ASR attacks using dropout uncertainty.
problem Adversarial attacks on ASR systems.
method Dropout uncertainty in neural networks.
result High detection accuracy across various ASR systems and datasets.
Bayesian Optimization improves machine learning for detecting network attacks.
problem Detecting network attacks to secure critical information.
method Bayesian Optimization for tuning machine learning algorithms (SVM-RBF, RF, k-NN).
result The proposed framework achieves high accuracy and low false alarms.
This paper explores adversarial examples in malware detection.
problem Malware detectors are vulnerable to adversarial interference.
method Training an existing model on a production-scale dataset to test adversarial attacks.
result Some previous attacks are less effective than initially reported.
2DSig-Detect detects adversarial perturbations in images.
problem Adversarial attacks degrade image model performance.
method 2D-signature embedded semi-supervised framework using rough path theory.
result 2DSig-Detect outperforms other methods in detecting adversarial perturbations.
AdvMind detects adversary intent in black-box attacks with high accuracy.
problem Detecting adversary intent in black-box adversarial attacks is challenging.
method AdvMind accounts for adversary adaptiveness and synthesizes queries to expose intent.
result AdvMind detects adversary intent with over 75% accuracy after observing less than 3 query batches.
A tree-based IDS detects cyber-attacks in AV networks.
problem Cyber-attacks in AV and IoV networks.
method Tree-structure machine learning models, ensemble learning, feature selection.
result High detection rate and low computational cost achieved.
Paper improves anomaly detection and categorization in multi-cloud environments.
problem Differentiating among different types of attacks for better defense.
method Used supervised machine learning techniques (LR and RF) on a public dataset.
result More than 99% detection accuracy and 93.6% categorization accuracy.
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.
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.
Paper proves mathematically that poisoning datasets can be detected.
problem Detecting data poisoning attacks in datasets.
method Mathematical definition and Conformal Separability Test.
result Dataset poisoning can be effectively detected.
Deep learning detects APT attacks with high accuracy and low false positives.
problem Detecting Advanced Persistent Threat (APT) attacks in network traffic.
method Used C5.0 decision tree, Bayesian network, and 6-layer deep neural network models on NSL-KDD dataset.
result Deep learning model achieved 98.85% accuracy and 1.13 false positive rate.
VarDetect monitors API queries to detect model extraction attacks.
problem Attackers can extract ML models through API queries.
method VarDetect uses a modified variational autoencoder to track and detect attacker samples.
result VarDetect successfully detects and raises alarms for model extraction attacks.
Paper detects Trojan neural networks with limited or no data.
problem Robustness of DNNs against Trojan attacks in data-scarce scenarios.
method Proposes data-limited and data-free TrojanNet detectors.
result Demonstrates effectiveness of detectors on various datasets.
Physical patches can fool object detectors anywhere in an image.
problem Physical adversarial attacks on object detection systems.
method Demonstrated a patch that suppresses all detected objects regardless of its position.
result A patch can be placed anywhere in an image to suppress all detected objects, including those far away.
Paper detects adversarial attacks in sound classification models.
problem Adversarial attacks threaten data-driven models, especially in sound classification.
method Detects adversarial subspaces in unitary vector domain using chordal distance and generalized Schur decomposition.
result Regularized logistic regression detector outperforms other approaches on benchmark datasets.
Paper proposes B3D method for black-box backdoor detection.
problem Detecting backdoor attacks in black-box models without access to training data.
method Gradient-free optimization to reverse-engineer triggers, simple strategy for reliable predictions.
result Effectiveness of B3D method corroborated on hundreds of DNN models.
New model improves classifier security against evasion attacks by selecting features.
problem Security of machine learning classifiers against evasion attacks.
method Adversary-aware feature selection model incorporating specific adversary assumptions.
result Classifier security worsens with feature selection, but new model improves it.
Study malware evasion attacks and defenses using ML classifiers.
problem Adversarial examples can mislead ML-based malware detectors.
method Investigated white-box and grey-box evasion attacks; compared defense approaches.
result Proposed a framework for grey-box and black-box attacks.
Machine learning-based IDSs in ICS are vulnerable to adversarial attacks that can bypass them.
problem Adversarial attacks on machine learning-based IDSs in ICS can lead to undetected cyber attacks.
method Used Jacobian-based Saliency Map attack to generate adversarial samples and explored adversarial training to improve model robustness.
result Classification performance of supervised models decreased by 16-20 percentage points with adversarial samples, but improved with adversarial training.
New adversarial training enhances malware detectors against various attacks.
problem Vulnerability of malware detectors to evasion attacks.
method Proposes a mixture of attacks and adversarial training to improve deep neural networks.
result Significantly enhances robustness of deep neural networks against a wide range of attacks.
New approach detects cyber-attacks in real-time.
problem Real-time detection of cyber-attacks for effective mitigation.
method Aggregates unsupervised anomaly detection algorithms and incorporates delayed feedback.
result Improves anomaly detection performance through theoretical guarantees.