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The Role of Machine Learning in Cybersecurity
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SAFER: Social Capital-Based Friend Recommendation to Defend against Phishing Attacks
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SoK: The Impact of Unlabelled Data in Cyberthreat Detection
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CrawlPhish: Large-Scale Analysis of Client-Side Cloaking Techniques in Phishing
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PWDGAN: Generating Adversarial Malicious URL Examples for Deceiving Black-Box Phishing Website Detector using GANs
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Catching Transparent Phish: Analyzing and Detecting MITM Phishing Toolkits
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Generating Optimal Attack Paths in Generative Adversarial Phishing
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Black-box Adversarial Attacks on Commercial Speech Platforms with Minimal Information
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“I Never Thought About Securing My Machine Learning Systems”: A Study of Security and Privacy Awareness of Machine Learning Practitioners
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Generative Adverserial Analysis of Phishing Attacks on Static and Dynamic Content of Webpages
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A Hard Label Black-box Adversarial Attack Against Graph Neural Networks
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A Survey of Machine Learning-Based Solutions for Phishing Website Detection
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Combining Text and Visual Features to Improve the Identification of Cloned Webpages for Early Phishing Detection
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Feature Importance Guided Attack: A Model Agnostic Adversarial Attack
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Modeling Realistic Adversarial Attacks against Network Intrusion Detection Systems
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Towards Lightweight URL-Based Phishing Detection
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Security Analysis on Practices of Certificate Authorities in the HTTPS Phishing Ecosystem
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Poisoning the Unlabeled Dataset of Semi-Supervised Learning
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A novel approach for phishing URLs detection using lexical based machine learning in a real-time environment
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Practical No-box Adversarial Attacks against DNNs
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Almost Tight L0-norm Certified Robustness of Top-k Predictions against Adversarial Perturbations
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VisualPhishNet: Zero-Day Phishing Website Detection by Visual Similarity
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Towards Benchmark Datasets for Machine Learning Based Website Phishing Detection: An experimental study
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Development of anti-phishing browser based on random forest and rule of extraction framework
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Accurate and fast URL phishing detector: A convolutional neural network approach
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Towards Adversarial Phishing Detection
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A Feature Selection Comparative Study for Web Phishing Datasets
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AdvMind: Inferring Adversary Intent of Black-Box Attacks
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An Evasion Attack against ML-based Phishing URL Detectors
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Blind Backdoors in Deep Learning Models
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Advanced evasion attacks and mitigations on practical ML‐based phishing website classifiers
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Bypassing Detection of URL-based Phishing Attacks Using Generative Adversarial Deep Neural Networks
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Adversarial Machine Learning-Industry Perspectives
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Intriguing Properties of Adversarial ML Attacks in the Problem Space
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Detecting and Characterizing Lateral Phishing at Scale
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Adversarial Sampling Attacks Against Phishing Detection
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A Novel Visual Similarity-based Phishing Detection Scheme using Hue Information with Auto Updating Database
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Doppelgängers on the Dark Web: A Large-scale Assessment on Phishing Hidden Web Services
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Addressing Adversarial Attacks Against Security Systems Based on Machine Learning
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On the Top Threats to Cyber Systems
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Malware Detection Using Machine Learning and Deep Learning
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Embedding Training Within Warnings Improves Skills of Identifying Phishing Webpages
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AdVersarial: Perceptual Ad Blocking meets Adversarial Machine Learning
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PhishMon: A Machine Learning Framework for Detecting Phishing Webpages
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Evading Botnet Detectors Based on Flows and Random Forest with Adversarial Samples
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Needle in a Haystack: Tracking Down Elite Phishing Domains in the Wild
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Why Do Adversarial Attacks Transfer? Explaining Transferability of Evasion and Poisoning Attacks
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Towards Measuring the Role of Phone Numbers in Twitter-Advertised Spam
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A machine learning based approach for phishing detection using hyperlinks information
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SoK: Security and Privacy in Machine Learning
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Server-Based Manipulation Attacks Against Machine Learning Models
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Towards detection of phishing websites on client-side using machine learning based approach
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Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning
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One Pixel Attack for Fooling Deep Neural Networks
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Adversarial Machine Learning in Malware Detection: Arms Race between Evasion Attack and Defense
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Improving Robustness of ML Classifiers against Realizable Evasion Attacks Using Conserved Features
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DeltaPhish: Detecting Phishing Webpages in Compromised Websites
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Ensemble Adversarial Training: Attacks and Defenses
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Yes, Machine Learning Can Be More Secure! A Case Study on Android Malware Detection
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PhishWHO: Phishing webpage detection via identity keywords extraction and target domain name finder
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Defensive Distillation is Not Robust to Adversarial Examples
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EmailProfiler: Spearphishing Filtering with Header and Stylometric Features of Emails
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Cracking Classifiers for Evasion: A Case Study on the Google's Phishing Pages Filter
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AI^2: Training a Big Data Machine to Defend
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Practical Black-Box Attacks against Machine Learning
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Intelligent rule-based Phishing Websites Classification
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Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks
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Building better data protection with SIEM
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Machine learning: Trends, perspectives, and prospects
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On the Character of Phishing URLs: Accurate and Robust Statistical Learning Classifiers
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Practical Evasion of a Learning-Based Classifier: A Case Study
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Some Fundamental Cybersecurity Concepts
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Malicious PDF detection using metadata and structural features
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The economics of cybersecurity: Principles and policy options
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PhishNet: Predictive Blacklisting to Detect Phishing Attacks
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Protecting users against phishing attacks with AntiPhish
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This paper is included in the Proceedings of the 31st USENIX Security Symposium.
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Additional Comments on the “White Paper: On Artificial Intelligence - A European approach to excellence and trust”
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All Adversarial Examples Papers
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State of the Phish 2022
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Machine Learning Security Evasion Competition
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Stakeholder perspectives and requirements on cybersecurity in Europe
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Phishpedia: A Hybrid Deep Learning Based Approach to Visually Identify Phishing Webpages
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PhishPrint: Evading Phishing Detection Crawlers by Prior Profiling
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Defeating DNN-Based Traffic Analysis Systems in Real-Time With Blind Adversarial Perturbations
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S&T Artificial Intelligence and Machine Learning Strategic Plan . Technical Report
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Detecting Spam in Twitter Microblogging Services: A Novel Machine Learning Approach based on Domain Popularity
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Sunrise to Sunset: Analyzing the End-to-end Life Cycle and Effectiveness of Phishing Attacks at Scale
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Building Robust Phishing Detection System: an Empirical Analysis
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PhishTime: Continuous Longitudinal Measurement of the Effectiveness of Anti-phishing Blacklists
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Interet Crime Report . Technical Report
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commission-white-paper-artificial-intelligence-feb2020_en.pdf [3] 2021. S&T Artificial Intelligence and Machine Learning Strategic Plan
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Adversarial Preprocessing: Understanding and Preventing Image-Scaling Attacks in Machine Learning
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Onevaluatingadversarialrobustness
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DeepPhish : Simulating Malicious AI
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Going Spear Phishing: Exploring Embedded Training and Awareness
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Machine Learning in the Age of Cyber AI A Review of Machine Learning Approaches for
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they also require a few lines of code to implement. However, determining the exact thresholds requires a detailed intelligence gathering campaign (or many queries to reverseengineer the ML-PWD
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For example, introducing a special 'backdoor' rule that "if a given URL is visited, then do not compute its length and return that the URL is short
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All features in Table 1 are used by both the ML-PWD targeted in our pragmatic use-case (cf. §B), as well as by the ‘true baselines’ ML-PWD (i.e.
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For this reason, in our evaluation we will put a greater emphasis on WA, because 'cheaper' attacks are more likely to occur in the wild: while WA can be associated with "horizontal phishing