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Description
Three out of four businesses face the threat of computer breaches or hacking incidents. Despite this alarming statistic, a staggering 90% of these organizations rely on critical security solutions that fail to identify such malicious intrusions. Advanced Persistent Threats (APTs), harmful behaviors, viruses, and crypto lockers are capable of bypassing existing security protocols, with no current method able to effectively recognize these threats. Nevertheless, these cyber attacks leave behind traces that signify their occurrence. The challenge lies in identifying these harmful indicators amidst vast data sets, a task that current security tools struggle to accomplish. Reveelium addresses this issue by correlating and aggregating various logs from an organization's information system, enabling the detection of ongoing attacks or harmful activities. As a vital asset in the battle against cyber threats, Reveelium's SIEM can function independently or be enhanced with tools like Ikare, Reveelium UEBA, or ITrust’s Acsia EDR, creating a comprehensive next-generation Security Operations Center (SOC). Furthermore, organizations can have their practices evaluated by an external party to gain an impartial assessment of their security posture, ensuring a more robust defense against cyber threats. This holistic approach not only strengthens security measures but also provides valuable insights for ongoing improvement.
Description
TILDE (Term Independent Likelihood moDEl) serves as a framework for passage re-ranking and expansion, utilizing BERT to boost retrieval effectiveness by merging sparse term matching with advanced contextual representations. The initial version of TILDE calculates term weights across the full BERT vocabulary, which can result in significantly large index sizes. To optimize this, TILDEv2 offers a more streamlined method by determining term weights solely for words found in expanded passages, leading to indexes that are 99% smaller compared to those generated by the original TILDE. This increased efficiency is made possible by employing TILDE as a model for passage expansion, where passages are augmented with top-k terms (such as the top 200) to enhance their overall content. Additionally, it includes scripts that facilitate the indexing of collections, the re-ranking of BM25 results, and the training of models on datasets like MS MARCO, thereby providing a comprehensive toolkit for improving information retrieval tasks. Ultimately, TILDEv2 represents a significant advancement in managing and optimizing passage retrieval systems.
API Access
Has API
API Access
Has API
Integrations
Hugging Face
Python
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
No price information available.
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
ITrust.fr
Founded
2007
Country
France
Website
www.itrust.fr/en/reveelium-siem/
Vendor Details
Company Name
ielab
Country
United States
Website
github.com/ielab/TILDE/tree/main
Product Features
Artificial Intelligence
Chatbot
For Healthcare
For Sales
For eCommerce
Image Recognition
Machine Learning
Multi-Language
Natural Language Processing
Predictive Analytics
Process/Workflow Automation
Rules-Based Automation
Virtual Personal Assistant (VPA)
Cybersecurity
AI / Machine Learning
Behavioral Analytics
Endpoint Management
IOC Verification
Incident Management
Tokenization
Vulnerability Scanning
Whitelisting / Blacklisting
Machine Learning
Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization