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Found 59 papers, 8 papers with code
CodeCloak: A Method for Evaluating and Mitigating Code Leakage by LLM Code Assistants
While beneficial, these tools might inadvertently expose the developer's proprietary code to the code assistant service provider during the development process.
Prompted Contextual Vectors for Spear-Phishing Detection
Spear-phishing attacks present a significant security challenge, with large language models (LLMs) escalating the threat by generating convincing emails and facilitating target reconnaissance.
DeSparsify: Adversarial Attack Against Token Sparsification Mechanisms in Vision Transformers
In this paper, we present DeSparsify, an attack targeting the availability of vision transformers that use token sparsification mechanisms.
GPT in Sheep's Clothing: The Risk of Customized GPTs
In November 2023, OpenAI introduced a new service allowing users to create custom versions of ChatGPT (GPTs) by using specific instructions and knowledge to guide the model's behavior.
QuantAttack: Exploiting Dynamic Quantization to Attack Vision Transformers
However, their dynamic behavior and average-case performance assumption makes them vulnerable to a novel threat vector -- adversarial attacks that target the model's efficiency and availability.
Detecting Anomalous Network Communication Patterns Using Graph Convolutional Networks
To protect an organizations' endpoints from sophisticated cyberattacks, advanced detection methods are required.
X-Detect: Explainable Adversarial Patch Detection for Object Detectors in Retail
X-Detect was evaluated in both the physical and digital space using five different attack scenarios (including adaptive attacks) and the COCO dataset and our new Superstore dataset.
ReMark: Receptive Field based Spatial WaterMark Embedding Optimization using Deep Network
Watermarking is one of the most important copyright protection tools for digital media.
CADeSH: Collaborative Anomaly Detection for Smart Homes
To overcome this, we propose a two-step collaborative anomaly detection method which first uses an autoencoder to differentiate frequent (`benign') and infrequent (possibly `malicious') traffic flows.
YolOOD: Utilizing Object Detection Concepts for Multi-Label Out-of-Distribution Detection
In this research, we propose YolOOD - a method that utilizes concepts from the object detection domain to perform OOD detection in the multi-label classification task.
Latent SHAP: Toward Practical Human-Interpretable Explanations
Model agnostic feature attribution algorithms (such as SHAP and LIME) are ubiquitous techniques for explaining the decisions of complex classification models, such as deep neural networks.
Seeds Don't Lie: An Adaptive Watermarking Framework for Computer Vision Models
In this paper, we present an adaptive framework to watermark a protected model, leveraging the unique behavior present in the model due to a unique random seed initialized during the model training.
Improving Interpretability via Regularization of Neural Activation Sensitivity
We evaluate the interpretability of models trained using our method to that of standard models and models trained using state-of-the-art adversarial robustness techniques.
Attacking Object Detector Using A Universal Targeted Label-Switch Patch
However, none of prior research proposed a misclassification attack on ODs, in which the patch is applied on the target object.
A Transferable and Automatic Tuning of Deep Reinforcement Learning for Cost Effective Phishing Detection
Many challenging real-world problems require the deployment of ensembles multiple complementary learning models to reach acceptable performance levels.
Phantom Sponges: Exploiting Non-Maximum Suppression to Attack Deep Object Detectors
Adversarial attacks against deep learning-based object detectors have been studied extensively in the past few years.
Adversarial Machine Learning Threat Analysis and Remediation in Open Radio Access Network (O-RAN)
Next, we explore the various AML threats associated with O-RAN and review a large number of attacks that can be performed to realize these threats and demonstrate an AML attack on a traffic steering model.
Adversarial Mask: Real-World Universal Adversarial Attack on Face Recognition Model
In our experiments, we examined the transferability of our adversarial mask to a wide range of FR model architectures and datasets.
Dodging Attack Using Carefully Crafted Natural Makeup
Deep learning face recognition models are used by state-of-the-art surveillance systems to identify individuals passing through public areas (e. g., airports).
Evaluating the Cybersecurity Risk of Real World, Machine Learning Production Systems
Using the extension, security practitioners can apply attack graph analysis methods in environments that include ML components; thus, providing security practitioners with a methodological and practical tool for evaluating the impact and quantifying the risk of a cyberattack targeting an ML production system.
CAN-LOC: Spoofing Detection and Physical Intrusion Localization on an In-Vehicle CAN Bus Based on Deep Features of Voltage Signals
This mechanism's effectiveness (100% accuracy) is demonstrated in a wide variety of insertion scenarios on a CAN bus prototype.
RadArnomaly: Protecting Radar Systems from Data Manipulation Attacks
The proposed technique, which allows the detection of malicious manipulation of critical fields in the data stream, is complemented by a timing-interval anomaly detection mechanism proposed for the detection of message dropping attempts.
TANTRA: Timing-Based Adversarial Network Traffic Reshaping Attack
Prior research on bypassing NIDSs has mainly focused on perturbing the features extracted from the attack traffic to fool the detection system, however, this may jeopardize the attack's functionality.
The Translucent Patch: A Physical and Universal Attack on Object Detectors
Therefore, in our experiments, which are conducted on state-of-the-art object detection models used in autonomous driving, we study the effect of the patch on the detection of both the selected target class and the other classes.
BENN: Bias Estimation Using Deep Neural Network
The need to detect bias in machine learning (ML) models has led to the development of multiple bias detection methods, yet utilizing them is challenging since each method: i) explores a different ethical aspect of bias, which may result in contradictory output among the different methods, ii) provides an output of a different range/scale and therefore, can't be compared with other methods, and iii) requires different input, and therefore a human expert needs to be involved to adjust each method according to the examined model.
Poisoning Attacks on Cyber Attack Detectors for Industrial Control Systems
With this research, we are the first to demonstrate such poisoning attacks on ICS cyber attack online NN detectors.
Being Single Has Benefits. Instance Poisoning to Deceive Malware Classifiers
The performance of a machine learning-based malware classifier depends on the large and updated training set used to induce its model.
Approximating Aggregated SQL Queries With LSTM Networks
We use LSTM network to learn the relationship between queries and their results, and to provide a rapid inference layer for predicting query results.
Dynamic Adversarial Patch for Evading Object Detection Models
In order to demonstrate our attack in a real-world setup, we implemented the patches by attaching flat screens to the target object; the screens are used to present the patches and switch between them, depending on the current camera location.
Stop Bugging Me! Evading Modern-Day Wiretapping Using Adversarial Perturbations
Our results in the real world suggest that our approach is a feasible solution for privacy protection.
Taking Over the Stock Market: Adversarial Perturbations Against Algorithmic Traders
In this study, we present a realistic scenario in which an attacker influences algorithmic trading systems by using adversarial learning techniques to manipulate the input data stream in real time.
Not All Datasets Are Born Equal: On Heterogeneous Data and Adversarial Examples
We, however, argue that machine learning models trained on heterogeneous tabular data are as susceptible to adversarial manipulations as those trained on continuous or homogeneous data such as images.
Adversarial robustness via stochastic regularization of neural activation sensitivity
Recent works have shown that the input domain of any machine learning classifier is bound to contain adversarial examples.
Fair and accurate age prediction using distribution aware data curation and augmentation
One of the main causes of unfair behavior in age prediction methods lies in the distribution and diversity of the training data.
FOOD: Fast Out-Of-Distribution Detector
Deep neural networks (DNNs) perform well at classifying inputs associated with the classes they have been trained on, which are known as in distribution inputs.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
An Automated, End-to-End Framework for Modeling Attacks From Vulnerability Descriptions
Given a description of a security vulnerability, the proposed framework first extracts the relevant attack entities required to model the attack, completes missing information on the vulnerability, and derives a new interaction rule that models the attack; this new rule is integrated within MulVAL attack graph tool.
MORTON: Detection of Malicious Routines in Large-Scale DNS Traffic
In this paper, we present MORTON, a method that identifies compromised devices in enterprise networks based on the existence of routine DNS communication between devices and disreputable host names.
Cryptography and Security
Hierarchical Deep Reinforcement Learning Approach for Multi-Objective Scheduling With Varying Queue Sizes
In this study we present MERLIN, a robust, modular and near-optimal DRL-based approach for multi-objective task scheduling.
Adversarial Machine Learning Attacks and Defense Methods in the Cyber Security Domain
In recent years machine learning algorithms, and more specifically deep learning algorithms, have been widely used in many fields, including cyber security.
Autosploit: A Fully Automated Framework for Evaluating the Exploitability of Security Vulnerabilities
The results show that Autosploit is able to automatically identify the system properties that affect the ability to exploit a vulnerability in both noiseless and noisy environments.
DANTE: A framework for mining and monitoring darknet traffic
In this paper, we present DANTE: a framework and algorithm for mining darknet traffic.
Can't Boil This Frog: Robustness of Online-Trained Autoencoder-Based Anomaly Detectors to Adversarial Poisoning Attacks
This finding suggests that neural network-based attack detectors used in the cyber-physical domain are more robust to poisoning than in other problem domains, such as malware detection and image processing.
GIM: Gaussian Isolation Machines
In many cases, neural network classifiers are likely to be exposed to input data that is outside of their training distribution data.
When Explainability Meets Adversarial Learning: Detecting Adversarial Examples using SHAP Signatures
We evaluate our method by building an extensive dataset of adversarial examples over the popular CIFAR-10 and MNIST datasets, and training a neural network-based detector to distinguish between normal and adversarial inputs.
Efficient Cyber Attacks Detection in Industrial Control Systems Using Lightweight Neural Networks and PCA
Finally, we study the proposed method's robustness against adversarial attacks, that exploit inherent blind spots of neural networks to evade detection while achieving their intended physical effect.
Privacy-Preserving Detection of IoT Devices Connected Behind a NAT in a Smart Home Setup
Today, telecommunication service providers (telcos) are exposed to cyber-attacks executed by compromised IoT devices connected to their customers' networks.
Transferable Cost-Aware Security Policy Implementation for Malware Detection Using Deep Reinforcement Learning
This security policy is then implemented, and for each inspected file, a different set of detectors is assigned and a different detection threshold is set.
MaskDGA: A Black-box Evasion Technique Against DGA Classifiers and Adversarial Defenses
Domain generation algorithms (DGAs) are commonly used by botnets to generate domain names through which bots can establish a resilient communication channel with their command and control servers.
Cryptography and Security
Defense Methods Against Adversarial Examples for Recurrent Neural Networks
Using our methods we were able to decrease the effectiveness of such attack from 99. 9% to 15%.
Cryptography and Security
MDGAN: Boosting Anomaly Detection Using \\Multi-Discriminator Generative Adversarial Networks
Anomaly detection is often considered a challenging field of machine learning due to the difficulty of obtaining anomalous samples for training and the need to obtain a sufficient amount of training data.
Detecting Cyberattacks in Industrial Control Systems Using Convolutional Neural Networks
This paper presents a study on detecting cyberattacks on industrial control systems (ICS) using unsupervised deep neural networks, specifically, convolutional neural networks.
N-BaIoT: Network-based Detection of IoT Botnet Attacks Using Deep Autoencoders
The proliferation of IoT devices which can be more easily compromised than desktop computers has led to an increase in the occurrence of IoT based botnet attacks.
Query-Efficient Black-Box Attack Against Sequence-Based Malware Classifiers
In this paper, we present a generic, query-efficient black-box attack against API call-based machine learning malware classifiers.
Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection
In this paper, we present Kitsune: a plug and play NIDS which can learn to detect attacks on the local network, without supervision, and in an efficient online manner.
Detection of Unauthorized IoT Devices Using Machine Learning Techniques
Based on the classification of 20 consecutive sessions and the use of majority rule, IoT device types that are not on the white list were correctly detected as unknown in 96% of test cases (on average), and white listed device types were correctly classified by their actual types in 99% of cases.
Generic Black-Box End-to-End Attack Against State of the Art API Call Based Malware Classifiers
In this paper, we present a black-box attack against API call based machine learning malware classifiers, focusing on generating adversarial sequences combining API calls and static features (e. g., printable strings) that will be misclassified by the classifier without affecting the malware functionality.
SIPHON: Towards Scalable High-Interaction Physical Honeypots
Based on this setup, six physical IP cameras, one NVR and one IP printer are presented as 85 real IoT devices on the Internet, attracting a daily traffic of 700MB for a period of two months.
Cryptography and Security
Classification of Smartphone Users Using Internet Traffic
Today, smartphone devices are owned by a large portion of the population and have become a very popular platform for accessing the Internet.
Anomaly Detection Using the Knowledge-based Temporal Abstraction Method
According to the proposed method a temporal pattern mining process is applied on a dataset of basic temporal abstraction database in order to extract patterns representing normal behavior.
