Results for “anomaly-detection”

Detects anomalies in Modbus/TCP and Modbus RTU communications in industrial control systems using Zeek, Suricata, and custom Python analysis.

Builds a 3D volatility surface for any optionable ticker, mapping implied volatility across strike price and time to expiration to identify cheap, expensive, or anomalous options.

Hunt for unusual network connections by analyzing outbound traffic patterns, rare destinations, non-standard ports, and anomalous connection frequencies from endpoints.

Parse NetFlow v9 and IPFIX records to detect volumetric anomalies, port scanning, data exfiltration, and C2 beaconing patterns using the Python netflow library.

Detects unusual options activity and classifies smart money signals to help follow institutional positioning, including volume/OI ratio spikes, block trades, sweep orders, and net premium flow.

Detect anomalies in DNP3 protocol communications used in SCADA systems by monitoring for unauthorized control commands, firmware update attempts, protocol violations, and deviations from baseline traffic patterns using deep packet inspection and machine learning approaches.

Detect suspicious Windows service installations (MITRE ATT&CK T1543.003) by parsing System event logs for Event ID 7045, analyzing service binary paths, and identifying indicators of persistence mechanisms.

Provides a structured debugging workflow for diagnosing errors, test failures, and unexpected behavior, including root cause analysis and verification steps.

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

Detects DNS tunneling by computing Shannon entropy of DNS query names, analyzing query length distributions, inspecting TXT record payloads, and identifying high subdomain cardinality using scapy for packet capture analysis.

当需要获取独特信息源以产生差异化想法时

Debugging specialist for errors, test failures, and unexpected behavior. Use proactively when encountering any issues.

Detect abuse of service accounts through anomalous interactive logons, privilege escalation, lateral movement, and unauthorized access patterns.

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

Analyze Zeek network logs to detect lateral movement techniques including SMB admin share access, DCE/RPC remote service creation, NTLM account spray, Kerberos anomalies, and large internal data transfers.

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.

Debugging specialist for errors, test failures, and unexpected behavior. Use proactively when encountering any issues.

Provides structured debugging guidance for errors, test failures, and unexpected behavior, including root cause analysis and verification steps.

Debugging specialist for errors, test failures, and unexpected behavior. Use proactively when encountering any issues.

Provides a structured debugging workflow for errors, test failures, and unexpected behavior, including root cause analysis and verification steps.

Runs time series machine learning with the aeon library — classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search via scikit-learn compatible APIs. Use when working with temporal data, sequential patterns, or time-indexed observations (univariate or multivariate) that need specialized algorithms beyond standard ML approaches. Part of the AlterLab Academic Skills suite.

N+1 Query Detection and Resolution

Debugging specialist for errors, test failures, and unexpected behavior. Use proactively when encountering any issues.

Use to scan support conversations for a harm nobody has categorised yet — a new failure mode, a scam pattern, or a product change hurting a group of customers before it shows in any metric. Trigger for "is anything new going wrong", "scan for emerging issues", "early warning from support", horizon scanning, a new scam pattern, or preparing for a question about what you might be missing.

This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.

This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.

This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.

This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.

This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.

AI-powered financial analysis suite. Generates executive CFO briefings from QuickBooks exports (P&L, Balance Sheet, General Ledger, Cash Flow, etc.) with anomaly detection, burn rate, runway analysis, and scenario modeling. Also estimates codebase development costs with organizational overhead and AI ROI analysis. Triggers on: 'CFO briefing', 'financial analysis', 'cost briefing', 'expense review', 'runway analysis', 'burn rate', 'cost estimate', 'how much would this cost to build', 'development cost', 'Claude ROI'.