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07/05/2023
Information-Based Heavy Hitters for Real-Time DNS Data Exfiltration Detection and Prevention
Data exfiltration over the DNS protocol and its detection have been rese...
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06/14/2023
X-Detect: Explainable Adversarial Patch Detection for Object Detectors in Retail
Object detection models, which are widely used in various domains (such ...
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05/11/2023
ReMark: Receptive Field based Spatial WaterMark Embedding Optimization using Deep Network
Watermarking is one of the most important copyright protection tools for...
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03/02/2023
D-Score: An Expert-Based Method for Assessing the Detectability of IoT-Related Cyber-Attacks
IoT devices are known to be vulnerable to various cyber-attacks, such as...
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03/02/2023
CADeSH: Collaborative Anomaly Detection for Smart Homes
Although home IoT (Internet of Things) devices are typically plain and t...
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12/05/2022
YolOOD: Utilizing Object Detection Concepts for Out-of-Distribution Detection
Out-of-distribution (OOD) detection has attracted a large amount of atte...
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11/27/2022
Latent SHAP: Toward Practical Human-Interpretable Explanations
Model agnostic feature attribution algorithms (such as SHAP and LIME) ar...
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11/24/2022
Seeds Don't Lie: An Adaptive Watermarking Framework for Computer Vision Models
In recent years, various watermarking methods were suggested to detect c...
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11/16/2022
Attacking Object Detector Using A Universal Targeted Label-Switch Patch
Adversarial attacks against deep learning-based object detectors (ODs) h...
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11/16/2022
Improving Interpretability via Regularization of Neural Activation Sensitivity
State-of-the-art deep neural networks (DNNs) are highly effective at tac...
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09/19/2022
A Transferable and Automatic Tuning of Deep Reinforcement Learning for Cost Effective Phishing Detection
Many challenging real-world problems require the deployment of ensembles...
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09/08/2022
Evaluating the Security of Aircraft Systems
The sophistication and complexity of cyber attacks and the variety of ta...
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08/11/2022
A Survey of MulVAL Extensions and Their Attack Scenarios Coverage
Organizations employ various adversary models in order to assess the ris...
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05/26/2022
Denial-of-Service Attack on Object Detection Model Using Universal Adversarial Perturbation
Adversarial attacks against deep learning-based object detectors have be...
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02/14/2022
AnoMili: Spoofing Prevention and Explainable Anomaly Detection for the 1553 Military Avionic Bus
MIL-STD-1553, a standard that defines a communication bus for interconne...
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01/16/2022
Adversarial Machine Learning Threat Analysis in Open Radio Access Networks
The Open Radio Access Network (O-RAN) is a new, open, adaptive, and inte...
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01/16/2022
Evaluating the Security of Open Radio Access Networks
The Open Radio Access Network (O-RAN) is a promising RAN architecture, a...
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11/21/2021
Adversarial Mask: Real-World Adversarial Attack Against Face Recognition Models
Deep learning-based facial recognition (FR) models have demonstrated sta...
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09/23/2021
On The Vulnerability of Anti-Malware Solutions to DNS Attacks
Anti-malware agents typically communicate with their remote services to ...
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09/14/2021
Dodging Attack Using Carefully Crafted Natural Makeup
Deep learning face recognition models are used by state-of-the-art surve...
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07/05/2021
A Framework for Evaluating the Cybersecurity Risk of Real World, Machine Learning Production Systems
Although cyberattacks on machine learning (ML) production systems can be...
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06/15/2021
CAN-LOC: Spoofing Detection and Physical Intrusion Localization on an In-Vehicle CAN Bus Based on Deep Features of Voltage Signals
The Controller Area Network (CAN) is used for communication between in-v...
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06/13/2021
RadArnomaly: Protecting Radar Systems from Data Manipulation Attacks
Radar systems are mainly used for tracking aircraft, missiles, satellite...
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03/24/2021
TRADE: TRusted Anonymous Data Exchange: Threat Sharing Using Blockchain Technology
Cyber attacks are becoming more frequent and sophisticated, introducing ...
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12/23/2020
Poisoning Attacks on Cyber Attack Detectors for Industrial Control Systems
Recently, neural network (NN)-based methods, including autoencoders, hav...
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12/23/2020
BENN: Bias Estimation Using Deep Neural Network
The need to detect bias in machine learning (ML) models has led to the d...
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12/23/2020
The Translucent Patch: A Physical and Universal Attack on Object Detectors
Physical adversarial attacks against object detectors have seen increasi...
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10/30/2020
Being Single Has Benefits. Instance Poisoning to Deceive Malware Classifiers
The performance of a machine learning-based malware classifier depends o...
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10/25/2020
Approximating Aggregated SQL Queries With LSTM Networks
Despite continuous investments in data technologies, the latency of quer...
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10/25/2020
Dynamic Adversarial Patch for Evading Object Detection Models
Recent research shows that neural networks models used for computer visi...
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10/24/2020
Stop Bugging Me! Evading Modern-Day Wiretapping Using Adversarial Perturbations
Mass surveillance systems for voice over IP (VoIP) conversations pose a ...
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10/19/2020
When Bots Take Over the Stock Market: Evasion Attacks Against Algorithmic Traders
In recent years, machine learning has become prevalent in numerous tasks...
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10/07/2020
Not All Datasets Are Born Equal: On Heterogeneous Data and Adversarial Examples
Recent work on adversarial learning has focused mainly on neural network...
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09/23/2020
Adversarial robustness via stochastic regularization of neural activation sensitivity
Recent works have shown that the input domain of any machine learning cl...
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09/11/2020
Fairness Matters – A Data-Driven Framework Towards Fair and High Performing Facial Recognition Systems
Facial recognition technologies are widely used in governmental and indu...
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08/16/2020
GLOD: Gaussian Likelihood Out of Distribution Detector
Discriminative deep neural networks (DNNs) do well at classifying input ...
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08/10/2020
An Automated, End-to-End Framework for Modeling Attacks From Vulnerability Descriptions
Attack graphs are one of the main techniques used to automate the risk a...
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08/05/2020
MORTON: Detection of Malicious Routines in Large-Scale DNS Traffic
In this paper, we present MORTON, a system that identifies compromised e...
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07/17/2020
Hierarchical Deep Reinforcement Learning Approach for Multi-Objective Scheduling With Varying Queue Sizes
Multi-objective task scheduling (MOTS) is the task scheduling while opti...
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07/05/2020
Adversarial Learning in the Cyber Security Domain
In recent years, machine learning algorithms, and more specially, deep l...
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06/30/2020
Autosploit: A Fully Automated Framework for Evaluating the Exploitability of Security Vulnerabilities
The existence of a security vulnerability in a system does not necessari...
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03/05/2020
DANTE: A framework for mining and monitoring darknet traffic
Trillions of network packets are sent over the Internet to destinations ...
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02/07/2020
Can't Boil This Frog: Robustness of Online-Trained Autoencoder-Based Anomaly Detectors to Adversarial Poisoning Attacks
In recent years, a variety of effective neural network-based methods for...
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02/06/2020
Neural Network Representation Control: Gaussian Isolation Machines and CVC Regularization
In many cases, neural network classifiers are likely to be exposed to in...
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10/23/2019
The Security of IP-based Video Surveillance Systems
IP-based Surveillance systems protect industrial facilities, railways, g...
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09/08/2019
When Explainability Meets Adversarial Learning: Detecting Adversarial Examples using SHAP Signatures
State-of-the-art deep neural networks (DNNs) are highly effective in sol...
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08/19/2019
3D Marketplace: Distributed Attestation of 3D Designs on Blockchain
Additive manufacturing (AM), or 3D printing, is an emerging manufacturin...
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07/02/2019
Efficient Cyber Attacks Detection in Industrial Control Systems Using Lightweight Neural Networks
Industrial control systems (ICSs) are widely used and vital to industry ...
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06/26/2019
Heuristic Approach Towards Countermeasure Selection using Attack Graphs
Selecting the optimal set of countermeasures is a challenging task that ...
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06/26/2019
