Results for “misinformation-detection”

Detect AI-generated audio, images, video, and text, trace synthesis sources, apply watermarks, verify speaker identity, and analyze media intelligence using the Resemble AI platform.

Detect C2 beaconing patterns in network traffic using frequency analysis, jitter detection, and domain reputation to identify compromised endpoints communicating with adversary infrastructure.

Identify command-and-control beaconing patterns in network traffic by applying statistical frequency analysis, jitter calculation, and coefficient of variation scoring to detect periodic callbacks from compromised endpoints.

Hunt for Mimikatz execution using command-line patterns, LSASS access signatures, binary indicators, and in-memory detection of known modules.

Filters top Hacker News stories by interest tags and extracts high-signal comments (top, dissenting, expert/builder) with themed clustering.

Use to monitor support conversations for conduct risk — pressure, mis-selling, unfair outcomes, obstruction and misleading statements — as distinct from whether the answer matched policy. Trigger for "monitor for mis-selling", "conduct risk in our support conversations", "are agents pressuring customers", "customers being obstructed from cancelling", conduct surveillance, or a conduct finding raised by second line.

Detects AI-generated deepfake audio used in voice phishing (vishing) attacks by extracting spectral features and classifying samples with machine learning models.

Collects publicly available information about a target organization to identify attack surfaces, social engineering targets, technology stacks, and credential exposures for authorized security testing.

Detect domain fronting C2 traffic by analyzing SNI vs HTTP Host header mismatches in proxy logs and TLS certificate discrepancies using pyOpenSSL for certificate inspection.

Detect and extract hidden data embedded in images, audio, and other media files using steganalysis tools to uncover covert communication channels.

Detect brand impersonation attacks across domains, social media, mobile apps, and dark web channels to identify phishing campaigns, fake sites, and unauthorized brand usage.

Detect Kerberoasting attacks by monitoring for anomalous Kerberos TGS requests targeting service accounts with SPNs for offline password cracking.

Detect abuse of legitimate Windows binaries (LOLBins) used for living off the land attacks by monitoring process creation, command-line arguments, and parent-child relationships.

Detect and prevent ARP spoofing attacks using ARPWatch, Dynamic ARP Inspection, Wireshark analysis, and custom Python monitoring scripts to protect against man-in-the-middle interception.

Detect abuse of legitimate Windows binaries (LOLBins) like certutil, regsvr32, mshta, and rundll32 using process telemetry, Sigma rules, and parent-child process analysis.

Detect NTFS timestamp manipulation (MITRE T1070.006) by comparing $STANDARD_INFORMATION vs $FILE_NAME timestamps in the MFT using analyzeMFT and Python.

Detects the test platform (VSTest vs Microsoft.Testing.Platform) and test framework (MSTest, xUnit, NUnit, TUnit) from .NET project files.

Proactively hunt for adversary abuse of legitimate system binaries (LOLBins) to execute malicious payloads while evading detection.

Detect data staging activity before exfiltration by monitoring for archive creation with 7-Zip/RAR, unusual temp folder access, large file consolidation, and staging directory patterns via EDR and process telemetry.

Deploys deception technology including honeypots, honeytokens, and decoy systems to detect attackers who have bypassed perimeter defenses, providing high-fidelity alerts with near-zero false positive rates.

Hunt for supply chain compromise indicators including trojanized software updates, compromised dependencies, unauthorized code modifications, and tampered build artifacts.

Hunt for web shell deployments on internet-facing servers by analyzing file creation in web directories, suspicious process spawning from web servers, and anomalous HTTP patterns.

First-principles assumption auditor. Classifies each hidden assumption (fact / convention / belief / interest-driven), ranks by fragility × impact, and rebuilds conclusions from verified premises. Bilingual: auto-detects Chinese or English.

First-principles assumption auditor. Classifies each hidden assumption (fact / convention / belief / interest-driven), ranks by fragility × impact, and rebuilds conclusions from verified premises. Bilingual: auto-detects Chinese or English.

Improve unclear UX copy, error messages, microcopy, labels, and instructions to make interfaces easier to understand. Use when the user mentions confusing text, unclear labels, bad error messages, hard-to-follow instructions, or wanting better UX writing.

SynthText: Synthetic Data for Text Detection