{"id":21396190,"url":"https://github.com/secdr/sec-paper","last_synced_at":"2026-01-02T18:09:37.539Z","repository":{"id":92450099,"uuid":"42373282","full_name":"secdr/sec-paper","owner":"secdr","description":"awesome security 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sec-paper\n\nawesome security papers.\n    \n    ├── APT\n    │   ├── A-Formal-Understanding-about-APT-Infection.pdf\n    │   └── Intelligence-Driven Computer Network Defense Informed by Analysis of Adversary Campaigns and Intrusion Kill Chains.pdf\n    ├── Binvul\n    │   ├── Some Vulnerabilities Are Different Than Others Studying Vulnerabilities and Attack Surfaces in the Wild.pdf\n    │   ├── The Attack of the Clones- A Study of the Impact of Shared Code on Vulnerability Patching.pdf\n    │   └── ZigZag- Automatically Hardening Web Applications Against Client-side Validation Vulnerabilities.pdf\n    ├── Code review\n    │   ├── Automatic Detection and Repair of Input Validation and Sanitization Bugs.pdf\n    │   ├── Behind an Application Firewall, Are We Safe from SQL Injection Attacks.pdf\n    │   ├── Code Reuse Attacks in PHP- Automated POP Chain Generation.pdf\n    │   ├── Dynamic PHP web-application analysis.pdf\n    │   ├── EKHUNTER- A Counter-Offensive Toolkit for Exploit Kit Infiltration.pdf\n    │   ├── Experience Report- An Empirical Study of PHP Security Mechanism Usage.pdf\n    │   ├── Saner- Composing Static and Dynamic Analysis to Validate Sanitization in Web Applications.pdf\n    │   ├── Simulation of Built-in PHP Features for Precise Static Code Analysis.pdf\n    │   ├── Software Verification and Validation Laboratory- Black-box SQL Injection Testing- Technical Report.pdf\n    │   ├── Static Detection of Second-Order Vulnerabilities in Web Applications.pdf\n    │   ├── Static and Dynamic Analysis for PHP Security.pdf\n    │   └── WAFA- Fine-grained Dynamic Analysis of Web Applications.pdf\n    ├── Machine learning\n    │   ├── ASwatch- An AS Reputation System to Expose Bulletproof Hosting ASes.pdf\n    │   ├── An Empirical Analysis of Malware Blacklists.pdf\n    │   ├── An SVM-based machine learning method for accurate internet traffic classification.pdf\n    │   ├── Anagram- A Content Anomaly Detector Resistant to Mimicry Attack.pdf\n    │   ├── Characterizing Google Hacking- A First Large-Scale Quantitative Study.pdf\n    │   ├── Classification of Malicious Domain Names using Support Vector Machine and Bi-gram Method.pdf\n    │   ├── Detecting Malicious Landing Pages in Malware Distribution Networks.pdf\n    │   ├── Detection of Early-Stage Enterprise Infection by Mining Large-Scale Log Data.pdf\n    │   ├── Developing Security Reputation Metrics for Hosting Providers.pdf\n    │   ├── From Throw-Away Traffic to Bots- Detecting the Rise of DGA-Based Malware.pdf\n    │   ├── Machine Learning Classification over Encrypted Data.pdf\n    │   ├── PoisonAmplifier- A Guided Approach of Discovering Compromised Websites through Reversing Search Poisoning Attacks.pdf\n    │   ├── Stickler- Defending Against Malicious CDNs in an Unmodified Browser.pdf\n    │   └── TrueClick- Automatically Distinguishing Trick Banners from Genuine Download Links.pdf\n    ├── Mobile\n    │   ├── A Study of Android Application Security.pdf\n    │   ├── Finding Unknown Malice in 10 Seconds- Mass Vetting for New Threats at the Google-Play Scale.pdf\n    │   ├── Privacy Implications of Presence Sharing in Mobile Messaging Applications.pdf\n    │   └── What is Wrecking Your Data Plan? A Measurement Study of Mobile Web Overhead.pdf\n    ├── NLP\n    │   ├── A Close Look on n-Grams in Intrusion Detection- Anomaly Detection vs. Classification.pdf\n    │   ├── Breaking Bad- Detecting malicious domains using word segmentation.pdf\n    │   ├── DSpin- Detecting Automatically Spun Content on the Web.pdf\n    │   ├── Detecting Unknown Network Attacks Using Language Models.pdf\n    │   ├── Detection of Malware by using Sequence Alignment Strategy and Data Mining Techniques.pdf\n    │   └── Metaphor Detection in Discourse.pdf\n    ├── Password\n    │   └── OMEN- Faster Password Guessing Using an Ordered Markov Enumerator.pdf\n    ├── Phishing\n    │   ├── A Framework for Predicting Phishing Websites using Neural Networks  .pdf\n    │   ├── A Lexical Approach for Classifying Malicious URLs.pdf\n    │   ├── An Approach to Predict Drive-by-Download Attacks by Vulnerability Evaluation and Opcode.pdf\n    │   ├── An efficacious method for detecting phishing webpages through target domain identification.pdf\n    │   ├── Beyond Blacklists- Learning to Detect Malicious Web Sites from Suspicious URLs.pdf\n    │   ├── Cluster-Oriented Ensemble Classifiers for Intelligent Malware Detection.pdf\n    │   ├── Cross-project Defect Prediction.pdf\n    │   ├── Detecting Phishing Emails the Natural Language Way.pdf\n    │   ├── Gangeshwari_Phising_Review+Paper.pdf\n    │   ├── Geo-Phisher- The Design of a Global Phishing Trend Visualization Tool.pdf\n    │   ├── Large-Scale Automatic Classification of Phishing Pages.pdf\n    │   ├── Lexical Feature Based Phishing URL Detection Using Online Learning.pdf\n    │   ├── Multi-label rules for phishing classification.pdf\n    │   ├── On the Character of Phishing URLs- Accurate and Robust Statistical Learning Classifiers�\\210\\227.pdf\n    │   ├── PREDICTION OF PHISHING WEBSITES USING CLASSIFICATION ALGORITHMS BASED ON WEIGHT OF WEB PAGES CHARACTERISTICS (1).doc\n    │   ├── PREDICTION OF PHISHING WEBSITES USING CLASSIFICATION ALGORITHMS BASED ON WEIGHT OF WEB PAGES CHARACTERISTICS.doc\n    │   ├── Parameters of Genetic Algorithm with Optimization for Phishing Detection.pdf\n    │   ├── PhishAri- Automatic Realtime Phishing Detection on Twitter.pdf\n    │   ├── PhishDef- URL Names Say It All.pdf\n    │   ├── PhishNet- Predictive Blacklisting to Detect Phishing Attacks.pdf\n    │   ├── Phishing Detection Using Traffic Behavior, Spectral Clustering, and Random Forests .pdf\n    │   ├── Phishing URL detection using URL Ranking .pdf\n    │   ├── Phishing Website Detection Fuzzy System Modelling.pdf\n    │   ├── Predicting Phishing Websites using Classification Mining Techniques with Experimental Case Studies.pdf\n    │   ├── Text-Based Phishing Detection Using A Simulation Model.pdf\n    │   ├── Towards Building a Word Similarity Dictionary for Personality Bias Classification of Phishing Email Contents .pdf\n    │   ├── Towards building a word similarity dictionary for personality bias classification of phishing email contents.pdf\n    │   ├── Using Uncleanliness to Predict Future Botnet Addresses.pdf\n    │   ├── Utilisation of website logo for phishing detection.pdf\n    │   └── Visual-Similarity-Based Phishing Detection.pdf\n    ├── Social\n    │   ├── Algorithmically Bypassing Censorship on Sina Weibo with Nondeterministic Homophone Substitutions.pdf\n    │   ├── Are You Sure You Want to Contact Us.pdf\n    │   ├── Real-Time Entity-Based Event Detection for Twitter.pdf\n    │   └── Vulnerability Disclosure in the Age of Social Media- Exploiting Twitter for Predicting Real-World Exploits.pdf\n    ├── Spam\n    │   ├── Drops for Stuff- An Analysis of Reshipping Mule Scams.pdf\n    │   ├── That Ain't You- Blocking Spearphishing Emails Before They Are Sent.pdf\n    │   ├── Transductive Link Spam Detection.pdf\n    │   └── WE KNOW IT BEFORE YOU DO- PREDICTING MALICIOUS DOMAINS.pdf\n    ├── WAF\n    │   └── Reliable Machine Learning Algorithms for Intrusion Detection Systems.pdf\n    ├── Web malware\n    │   ├── Ad Injection at Scale- Assessing Deceptive Advertisement Modifications.pdf\n    │   ├── Analyzing and Defending Against Web-based Malware.pdf\n    │   ├── AutoBLG- Automatic URL Blacklist Generator Using Search Space Expansion and Filters.pdf\n    │   ├── Comparisons of machine learning techniques for detecting malicious webpages.pdf\n    │   ├── EKHUNTER- A Counter-Offensive Toolkit for Exploit Kit Infiltration.pdf\n    │   ├── Eyes of a Human, Eyes of a Program- Leveraging Different Views of the Web for Analysis and Detection.pdf\n    │   ├── JSOD- JavaScript obfuscation detector.pdf\n    │   ├── Measuring Drive-by Download Defense in Depth.pdf\n    │   ├── Meerkat-  Detecting Website Defacements through Image-based Object Recognition.pdf\n    │   ├── Paint it Black- Evaluating the Effectiveness of Malware Blacklists.pdf\n    │   ├── The Ghost In The Browser Analysis of Web-based Malware.pdf\n    │   ├── Understanding Malvertising Through Ad-Injecting Browser Extensions.pdf\n    │   ├── WebWinnow- Leveraging Exploit Kit Workflows to Detect Malicious URLs.pdf\n    │   ├── WebWitness- Investigating, Categorizing, and Mitigating Malware Download Paths.pdf\n    │   └── Your Reputation Precedes You- History, Reputation, and the Chrome Malware Warning.pdf\n    └── Websec\n        ├── Detecting Logic Vulnerabilities in E-Commerce Applications.pdf\n        ├── High-speed web attack detection through extracting exemplars from HTTP traffic.pdf\n        ├── May I? - Content Security Policy Endorsement for Browser Extensions.pdf\n        ├── Web Attack Detection Using IDS*.pdf\n        └── Why Is CSP Failing? Trends and Challenges in CSP Adoption .pdf    \n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsecdr%2Fsec-paper","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsecdr%2Fsec-paper","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsecdr%2Fsec-paper/lists"}