This paper addresses detection of a reverse engineering (RE) attack targeting a deep neural network (DNN) image classifier; by querying, RE's aim is to discover the classifier's decision rule. RE can enable test-time evasion attacks, which require knowledge of the classifier. Recently, we proposed a quite effective app…
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48 results for “attack detection”
"Feint Attack", as a new type of APT attack, has become the focus of attention. It adopts a multi-stage attacks mode which can be concluded as a combination of virtual attacks and real attacks. Under the cover of virtual attacks, real attacks can achieve the real purpose of the attacker, as a result, it often caused hu…
Early detection of cyber-attacks is crucial for a safe and reliable operation of the smart grid. In the literature, outlier detection schemes making sample-by-sample decisions and online detection schemes requiring perfect attack models have been proposed. In this paper, we formulate the online attack/anomaly detection…
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.
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.
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.
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.
Most state-of-the-art machine learning (ML) classification systems are vulnerable to adversarial perturbations. As a consequence, adversarial robustness poses a significant challenge for the deployment of ML-based systems in safety- and security-critical environments like autonomous driving, disease detection or unmann…
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…
Neural networks are increasingly used for intrusion detection on industrial control systems (ICS). With neural networks being vulnerable to adversarial examples, attackers who wish to cause damage to an ICS can attempt to hide their attacks from detection by using adversarial example techniques. In this work we address…
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.
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.
Federated learning systems are vulnerable to attacks from malicious clients. As the central server in the system cannot govern the behaviors of the clients, a rogue client may initiate an attack by sending malicious model updates to the server, so as to degrade the learning performance or enforce targeted model poisoni…
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.
It is well-known that many machine learning models are susceptible to adversarial attacks, in which an attacker evades a classifier by making small perturbations to inputs. This paper discusses how industrial copyright detection tools, which serve a central role on the web, are susceptible to adversarial attacks. We di…
2DSig-Detect: a semi-supervised framework for anomaly detection on image data using 2D-signaturescs.CV
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.
Recently, Convolutional Neural Networks (CNNs) demonstrate a considerable vulnerability to adversarial attacks, which can be easily misled by adversarial perturbations. With more aggressive methods proposed, adversarial attacks can be also applied to the physical world, causing practical issues to various CNN powered a…
Recently, advances in machine learning techniques have attracted the attention of the research community to build intrusion detection systems (IDS) that can detect anomalies in the network traffic. Most of the research works, however, do not differentiate among different types of attacks. This is, in fact, necessary fo…
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.
Adversarial examples are considered a serious issue for safety critical applications of AI, such as finance, autonomous vehicle control and medicinal applications. Though significant work has resulted in increased robustness of systems to these attacks, systems are still vulnerable to well-crafted attacks. To address t…
Recent advances in smart cities applications enforce security threads such as node replication attacks. Such attack is take place when the attacker plants a replicated network node within the network. Vehicular Ad hoc networks are connecting sensors that have limited resources and required the response time to be as lo…
Recently, a special type of data poisoning (DP) attack targeting Deep Neural Network (DNN) classifiers, known as a backdoor, was proposed. These attacks do not seek to degrade classification accuracy, but rather to have the classifier learn to classify to a target class whenever the backdoor pattern is present in a tes…
Machine learning models have been widely used in security applications such as intrusion detection, spam filtering, and virus or malware detection. However, it is well-known that adversaries are always trying to adapt their attacks to evade detection. For example, an email spammer may guess what features spam detection…
Early detection of the advanced persistent threat attack using performance analysis of deep learningcs.CR
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.
Investigating Resistance of Deep Learning-based IDS against Adversaries using min-max Optimizationcs.LG
With the growth of adversarial attacks against machine learning models, several concerns have emerged about potential vulnerabilities in designing deep neural network-based intrusion detection systems (IDS). In this paper, we study the resilience of deep learning-based intrusion detection systems against adversarial at…
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.
An attacker may use a variety of techniques to fool an automatic speaker verification system into accepting them as a genuine user. Anti-spoofing methods meanwhile aim to make the system robust against such attacks. The ASVspoof 2017 Challenge focused specifically on replay attacks, with the intention of measuring the …
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.
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.
There has been an ongoing cycle where stronger defenses against adversarial attacks are subsequently broken by a more advanced defense-aware attack. We present a new approach towards ending this cycle where we "deflect'' adversarial attacks by causing the attacker to produce an input that semantically resembles the att…
Low-rate application layer distributed denial of service (LDDoS) attacks are both powerful and stealthy. They force vulnerable webservers to open all available connections to the adversary, denying resources to real users. Mitigation advice focuses on solutions that potentially degrade quality of service for legitimate…
Label manipulation attacks are a subclass of data poisoning attacks in adversarial machine learning used against different applications, such as malware detection. These types of attacks represent a serious threat to detection systems in environments having high noise rate or uncertainty, such as complex networks and I…
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.
We present a novel adversarial detection and correction method for machine learning classifiers.The detector consists of an autoencoder trained with a custom loss function based on the Kullback-Leibler divergence between the classifier predictions on the original and reconstructed instances.The method is unsupervised, …
In recent years, a variety of effective neural network-based methods for anomaly and cyber attack detection in industrial control systems (ICSs) have been demonstrated in the literature. Given their successful implementation and widespread use, there is a need to study adversarial attacks on such detection methods to b…
An Intrusion Detection System (IDS) is a key cybersecurity tool for network administrators as it identifies malicious traffic and cyberattacks. With the recent successes of machine learning techniques such as deep learning, more and more IDS are now using machine learning algorithms to detect attacks faster. However, t…
The detection of Denial of Service (DoS) attacks remains a challenge for the cloud environment, affecting a massive number of services and applications hosted by such virtualized infrastructures. Typically, in the literature, the detection of DoS attacks is performed solely by analyzing the traffic of packets in the ne…
As the prevalence and everyday use of machine learning algorithms, along with our reliance on these algorithms grow dramatically, so do the efforts to attack and undermine these algorithms with malicious intent, resulting in a growing interest in adversarial machine learning. A number of approaches have been developed …
The rapid digital transformation without security considerations has resulted in the rise of global-scale cyberattacks. The first line of defense against these attacks are Network Intrusion Detection Systems (NIDS). Once deployed, however, these systems work as blackboxes with a high rate of false positives with no mea…
A novel method using saliency maps detects adversarial attacks effectively.
problem Vulnerability of neural networks to adversarial attacks.
method Combining saliency maps with additional noises and inconsistency strategy.
result High detection success rate of adversarial attacks on various datasets.
The convolutional neural network (CNN) architecture is increasingly being applied to new domains, such as malware detection, where it is able to learn malicious behavior from raw bytes extracted from executables. These architectures reach impressive performance with no feature engineering effort involved, but their rob…
In this paper, we demonstrate a physical adversarial patch attack against object detectors, notably the YOLOv3 detector. Unlike previous work on physical object detection attacks, which required the patch to overlap with the objects being misclassified or avoiding detection, we show that a properly designed patch can s…