An optimistic list of where to find relevant security information
- Adversarial Learning
- DCGAN - Deep Convolutional Generative Adversarial Networks (TensorFlow)
- Awesome Adversarial Machine Learning (Awesome List)
- GAN-CLS - Generative Adversarial Text to Image Synthesis (TensorFlow
- Image-to-Image Translation with Conditional Adversarial Networks
- im2im - Unsupervised Image to Image Translation with Generative Adversarial Networks (TensorFlow)
- Metta - Information security preparedness tool to do adversarial simulation
- Information security preparedness tool to do adversarial simulation (Incident Response)
- SRGAN - Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network (TensorFlow)
- Caldera - Automated adversary emulation system that performs post-compromise adversarial behavior within Windows Enterprise networks. It generates plans during operation using a planning system and a pre-configured adversary model based on the Adversarial Tactics, Techniques & Common Knowledge (ATT&CK™) project (Incident Response
- Image-to-Image Translation with Conditional Adversarial Networks - Implementation of image to image (pix2pix) translation from the paper by isola et al (ML)
- Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling - 3D-GANs for 3D model generation and fun 3D furniture arithmetics from embeddings (think like word2vec word arithmetics with 3D furniture representations)
- Censys Internet Scan Data
- Google Safe Browsing
- Quttera Malware API
- PhishTank Phishing Campaigns
- Sucuri Malware Analysis (Commercial) API
- URLScan API
- VirusTotal API
- Breaking Linear Classifiers on ImageNet , A. Karpathy et al.
- Breaking things is easy , N. Papernot & I. Goodfellow et al.
- Attacking Machine Learning with Adversarial Examples , N. Papernot, I. Goodfellow, S. Huang, Y. Duan, P. Abbeel, J. Clark.
- Robust Adversarial Examples , Anish Athalye.
- A Brief Introduction to Adversarial Examples , A. Madry et al.
- Training Robust Classifiers (Part 1) , A. Madry et al.
- Adversarial Machine Learning Reading List , N. Carlini
- Recommendations for Evaluating Adversarial Example Defenses , N. Carlini
- darknet
- darkreading
- Google Online Security Blog
- lawfareblog
- Graham Cluley
- Krebs on Security
- Lawfare - Hard National Security Choices
- Schneier on Security
- Security Affairs
- The Hacker News - Cybersecurity News and Analysis
- TaoSecurity Blog
- TrendLabs Security Intelligence Blog
- AWS re:Inforce 2021
- BlackHat USA
- DEF CON
- Flocon
- Gartner Security & Risk Management Summit
- InfoSec World 2021
- RSA
- ShmooCon
- Bug Bounty Hunters
- CSEC
- Cybersecurity Jobs/Lifestyle 💻
- Cyber Syndicates
- Hackers
- HackTheBox - Discord
- Level iv Security
- NOOB SEC
- Technical Sapien
- The White Circle
- Business Insider - Defense
- Defence-In-Depth
- Defense One - All Content
- Defense.gov Explore Feed
- Janes news RSS
- Breaking Defense
- War Is Boring
- War on the Rocks
- Modern War Institute
- [Alibi Detect Python library](GitHub - SeldonIO/alibi-detect: Algorithms for outlier and adversarial instance detection, concept drift and metrics.) - outlier, adversarial and drift detection. The package aims to cover both online and offline detectors for tabular data, text, images and time series. The outlier detection methods should allow the user to identify global, contextual and collective outliers.
- DCGAN - Deep Convolutional Generative Adversarial Networks (TensorFlow)
- GAN-CLS - Generative Adversarial Text to Image Synthesis (TensorFlow
- im2im - Unsupervised Image to Image Translation with Generative Adversarial Networks (TensorFlow)
- Metta - Information security preparedness tool to do adversarial simulation
- Information security preparedness tool to do adversarial simulation (Incident Response)
- SRGAN - Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network (TensorFlow)
- Caldera - Automated adversary emulation system that performs post-compromise adversarial behavior within Windows Enterprise networks. It generates plans during operation using a planning system and a pre-configured adversary model based on the Adversarial Tactics, Techniques & Common Knowledge (ATT&CK™) project (Incident Response
- Image-to-Image Translation with Conditional Adversarial Networks - Implementation of image to image (pix2pix) translation from the paper by isola et al (ML)
- Application Security
- Security
- CTF - Capture The Flag.
- Malware Analysis
- Android Security
- Hacking
- Honeypots - Deception trap, designed to entice an attacker into attempting to compromise the information systems in an organization.
- Incident Response
- Vehicle Security and Car Hacking
- Web Security - Security of web apps & services.
- Lockpicking - The art of unlocking a lock by manipulating its components without the key.
- Cybersecurity Blue Team - Groups of individuals who identify security flaws in information technology systems.
- Fuzzing - Automated software testing technique that involves feeding pseudo-randomly generated input data.
- Embedded and IoT Security
- GDPR - Regulation on data protection and privacy for all individuals within EU.
- DevSecOps - Integration of security practices into DevOps .
sei.social.cybersecurity/adversarial_ai zotero group
sei.social.cybersecurity zotero group
- Adversarial Learning
- Brakeing Security
- Darknet Diaries
- Hidden Forces
- Irregular Warfare Podcast
- Malicious Life
- Purple Squad Security
- Rational Security
- SEI Cyber Talks
- Do Statistical Models Understand the World? , I. Goodfellow, 2015
- Classifiers under Attack , David Evans, 2017
- Adversarial Examples in Machine Learning , Nicolas Papernot, 2017
- Poisoning Behavioral Malware Clustering , Biggio. B, Rieck. K, Ariu. D, Wressnegger. C, Corona. I. Giacinto, G. Roli. F, 2014
- Is Data Clustering in Adversarial Settings Secure? , BBiggio. B, Pillai. I, Rota Bulò. S, Ariu. D, Pelillo. M, Roli. F, 2015
- Poisoning complete-linkage hierarchical clustering , Biggio. B, Rota Bulò. S, Pillai. I, Mura. M, Zemene Mequanint. E, Pelillo. M, Roli. F, 2014
- Is Feature Selection Secure against Training Data Poisoning? , Xiao. H, Biggio. B, Brown. G, Fumera. G, Eckert. C, Roli. F, 2015
- Adversarial Feature Selection Against Evasion Attacks , Zhang. F, Chan. PPK, Biggio. B, Yeung. DS, Roli. F, 2016
(search for these via site:twitter.com lists/infosec)