https://winbuzzer.com/2026/01/21/openai-deploys-ai-powered-age-detection-on-chatgpt-xcxwbn/
OpenAI Deploys AI-Powered Age Detection on ChatGPT
#AI #ChatGPT #OpenAI #AIEthics #GenerativeAI #AIModels #ChildSafety #TeenSafety #ContentModeration #BehavioralAnalysis
Trust Is Dead: Welcome to the Age of Account-Based Infiltration
https://vigilante.tv/videos/watch/920b7f47-e01a-434c-9ca3-9c9eeff6a8e3
DAMM, Detect Any #MouseModel ๐ญ, has just been released ๐ Itโs a robustly validated mouse detector that excels in generalization, tracking mice in complex social and experimental settings with zero to minimal setup-specific training
๐ http://web.eecs.umich.edu/gkaul/DAMM
๐ GitHub: http://github.com/backprop64/DAMM
๐ Kaul et al. (2024) http://biorxiv.org/content/10.1101/2024.01.18.576153v1
#BehavioralScience #behavioralanalysis #neuroscience #machinelearning
๐ ๐ฃ๐ฟ๐ผ๐๐ฒ๐ฐ๐๐ถ๐ป๐ด ๐ฌ๐ผ๐๐ฟ ๐ช๐ฒ๐ฏ ๐ฅ๐ฒ๐๐ผ๐๐ฟ๐ฐ๐ฒ๐ ๐ณ๐ฟ๐ผ๐บ ๐จ๐ป๐ฒ๐๐ต๐ถ๐ฐ๐ฎ๐น ๐๐ผ๐ ๐๐ฐ๐๐ถ๐๐ถ๐๐ถ๐ฒ๐ ๐
The continuous integration of bots to simulate human engagement, especially for unethical activities in web applications, poses security risks and diverts engagement with web resources. With the emergence of new AI projects and Large Language Models (LLMs), vulnerabilities such as prompt injections, data leakage, training data poisoning, and unauthorized code execution have become more prevalent.
To mitigate these risks, it is crucial to grant appropriate access to bots on your websites. Microsoft Bot Manager Ruleset, in combination with a Web Application Firewall, offers effective measures to reduce illegitimate non-human access. These measures include verified labels, static analysis (rate limiting), and behavioral analysis.
Find out more details: https://techcommunity.microsoft.com/t5/azure-network-security-blog/azure-web-application-firewall-bot-manager-scenarios/ba-p/3855731
#microsoft #security #ai #bot #waf #webapplicationfirewall #bot #llm #seo #azure #azuresecurity #microsoftsecurity #soc #siem #soar #badbot #goodbot #applicationsecurity #azurenetworking #networksecurity #behavioralanalysis
This article is part of our ongoing efforts to continually develop strategies against malicious bots. The continuous integration of bots to simulate human engagement, especially for unethical activities in web applications lead to both security incidents and diversion of engagement with web resour...
#Inserm announced an upcoming workshop on โNew #machinelearning methods for animal and human #behavioralanalysisโ ๐
โฐ October 16-18, 2023 (phase I)
๐ Bordeaux, France ๐ซ๐ท
๐ https://ateliersinserm.dakini-pco.com/en/home-66-35.php
#ml #neuroscience #BehavioralScience
๐ขโ We are extremely happy to announce the release of Slips v1.0.0! Slips is our Stratosphere machine learning-based behavioural intrusion detection and prevention system!
We will be celebrating this milestone by presenting Slips in Black Hat Europe Arsenal!
Slips is the first machine learning-based free software IDS, born at Stratosphere in the Czech Technical University in Prague 7 years ago!
We have been working hard to get to the 1.0.0 version, bringing more stability and detection capabilities to our tool. We hope you enjoy this version and we are happy to hear about any feedback through our GitHub repository (https://github.com/stratosphereips/StratosphereLinuxIPS).
#FreeSoftware #Cybersecurity #machinelearning #infosec #behavioralanalysis #trafficanalysis #malwaredetection #homeIDS #IDS #IPS #cybersec #toolshare #Slips #Stratosphere #networkpacketanalysis #PacketAnalysis
https://www.stratosphereips.org/blog/2022/12/2/new-slips-version-100-is-here
Slips, a free software behavioral Python intrusion prevention system (IDS/IPS) that uses machine learning to detect malicious behaviors in the network traffic. Stratosphere Laboratory, AIC, FEL, CV...