「ty」の検索結果

This skill covers detecting anomalies in Modbus/TCP and Modbus RTU communications in industrial control systems. It addresses function code monitoring, register range validation, timing analysis, unauthorized client detection, and deep packet inspection for malformed Modbus frames. The skill leverages Zeek with Modbus protocol analyzers, Suricata IDS with OT rules, and custom Python-based detection using Markov chain models for normal Modbus transaction sequences.

日本語の概要は準備中です。原文の説明を表示しています。

Implement User and Entity Behavior Analytics using Elasticsearch/OpenSearch to build behavioral baselines, calculate anomaly scores, perform peer group analysis, and detect insider threat indicators such as data exfiltration, privilege abuse, and unauthorized access patterns.

日本語の概要は準備中です。原文の説明を表示しています。

Detect Kerberos Golden Ticket forgery by analyzing Windows Event ID 4769 for RC4 encryption downgrades (0x17), abnormal ticket lifetimes, and krbtgt account anomalies in Splunk and Elastic SIEM

日本語の概要は準備中です。原文の説明を表示しています。

Detect lateral movement in network traffic using Zeek (formerly Bro) log analysis. Parses conn.log, smb_mapping.log, smb_files.log, dce_rpc.log, kerberos.log, and ntlm.log to identify SMB file transfers, NTLM account spray activity, remote service execution, and anomalous internal connections.

日本語の概要は準備中です。原文の説明を表示しています。

Detect insider threat behavioral indicators including unusual data access, off-hours activity, mass file downloads, privilege abuse, and resignation-correlated data theft.

日本語の概要は準備中です。原文の説明を表示しています。

Detects and analyzes malicious behavior in mobile applications through behavioral analysis, permission abuse detection, network traffic monitoring, and dynamic instrumentation. Use when analyzing suspicious mobile applications for data exfiltration, command-and-control communication, credential stealing, SMS interception, or other malware indicators. Activates for requests involving mobile malware analysis, app behavior monitoring, trojan detection, or suspicious app investigation.

日本語の概要は準備中です。原文の説明を表示しています。

Detect Pass-the-Hash attacks by analyzing NTLM authentication patterns, identifying Type 3 logons with NTLM where Kerberos is expected, and correlating with credential dumping.

日本語の概要は準備中です。原文の説明を表示しています。

Detect Kerberos Pass-the-Ticket (PtT) attacks by analyzing Windows Event IDs 4768, 4769, and 4771 for anomalous ticket usage patterns in Splunk and Elastic SIEM

日本語の概要は準備中です。原文の説明を表示しています。

Detects insider data exfiltration by analyzing DLP policy violations, file access patterns, upload volume anomalies, and off-hours activity in endpoint and cloud logs. Uses pandas for behavioral analytics and statistical baselines. Use when investigating insider threats or building user behavior analytics for data loss prevention.

日本語の概要は準備中です。原文の説明を表示しています。

Detect unusual API call patterns in AWS CloudTrail logs using boto3, statistical baselining, and behavioral analysis to identify credential compromise, privilege escalation, and unauthorized resource access.

日本語の概要は準備中です。原文の説明を表示しています。

Deploys deception-based honeytokens in Active Directory including fake privileged accounts with AdminCount=1, fake SPNs for Kerberoasting detection (honeyroasting), decoy GPOs with cpassword traps, and fake BloodHound paths. Monitors Windows Security Event IDs 4769, 4625, 4662, 5136 for honeytoken interaction. Use when implementing AD deception defenses for detecting lateral movement, credential theft, and reconnaissance.

日本語の概要は準備中です。原文の説明を表示しています。

Detecting exposed AWS credentials in source code repositories, CI/CD pipelines, and configuration files using TruffleHog, git-secrets, and AWS-native detection mechanisms to prevent credential theft and unauthorized account access.

日本語の概要は準備中です。原文の説明を表示しています。

Detect lateral movement in Azure AD/Entra ID environments using Microsoft Graph API audit logs, Azure Sentinel KQL hunting queries, and sign-in anomaly correlation to identify privilege escalation, token theft, and cross-tenant pivoting.

日本語の概要は準備中です。原文の説明を表示しています。

Deploys and monitors ransomware canary files across critical directories using Python's watchdog library for real-time filesystem event detection. Places strategically named decoy files that mimic high-value targets (financial records, credentials, database exports) in locations ransomware typically enumerates first. Monitors for any read, modify, rename, or delete operations on canary files and triggers immediate alerts via email, Slack webhook, or syslog when interaction is detected, providing early warning before full encryption begins.

日本語の概要は準備中です。原文の説明を表示しています。

This skill covers deploying anomaly detection systems for industrial control environments using machine learning models trained on OT network baselines, physics-based process models, and behavioral analysis of industrial protocol communications. It addresses building normal behavior profiles for SCADA polling patterns, detecting deviations in Modbus/DNP3/OPC UA traffic, identifying rogue devices, and correlating network anomalies with physical process data from historians.

日本語の概要は準備中です。原文の説明を表示しています。

Correlates disparate security incidents, IOCs, and adversary behaviors across time and organizations to identify unified threat campaigns, attribute them to common threat actors, and extract shared indicators for improved detection. Use when multiple incidents exhibit overlapping indicators, when sector-wide attack campaigns require cross-organizational analysis, or when building campaign-level intelligence products. Activates for requests involving campaign analysis, incident clustering, cross-organizational IOC correlation, or MISP correlation engine.

日本語の概要は準備中です。原文の説明を表示しています。

Deploying Cloudflare Access with Cloudflare Tunnel to provide zero trust access to self-hosted and private applications, configuring identity-aware access policies, device posture checks, and WARP client enrollment for VPN replacement.

日本語の概要は準備中です。原文の説明を表示しています。

Configures pfSense firewall rules, NAT policies, VPN tunnels, and traffic shaping to enforce network segmentation, control traffic flow, and protect internal network zones in enterprise and small-to-medium business environments.

日本語の概要は準備中です。原文の説明を表示しています。

Detect and prevent API enumeration attacks including BOLA and IDOR exploitation by monitoring sequential identifier access patterns and authorization failures.

日本語の概要は準備中です。原文の説明を表示しています。

Configuring Zscaler Private Access (ZPA) to replace traditional VPN with zero trust network access by deploying App Connectors, defining application segments, configuring access policies based on user identity and device posture, and integrating with IdPs.

日本語の概要は準備中です。原文の説明を表示しています。

Configure secure OAuth 2.0 authorization flows including Authorization Code with PKCE, Client Credentials, and Device Authorization Grant. This skill covers flow selection, PKCE implementation, token

日本語の概要は準備中です。原文の説明を表示しています。

Deploy Cisco Duo multi-factor authentication across enterprise applications, VPN, RDP, and SSH access points. This skill covers Duo integration methods, adaptive authentication policies, device trust

日本語の概要は準備中です。原文の説明を表示しています。

Configure microsegmentation policies to enforce least-privilege workload-to-workload access using tools like VMware NSX, Illumio, and Calico, preventing lateral movement in zero trust architectures.

日本語の概要は準備中です。原文の説明を表示しています。

Executes containment strategies to stop active adversary operations and prevent lateral movement during a confirmed security breach. Implements short-term and long-term containment using network segmentation, endpoint isolation, credential revocation, and access control modifications. Activates for requests involving breach containment, lateral movement prevention, network isolation, active threat containment, or live incident response.

日本語の概要は準備中です。原文の説明を表示しています。