“detection”的搜索结果

Detect malicious email forwarding rules created by adversaries to maintain persistent access to email communications for intelligence collection and BEC attacks.

中文简介正在准备中。当前显示原文说明。

Detect privilege escalation attempts including token manipulation, UAC bypass, unquoted service paths, kernel exploits, and sudo/doas abuse across Windows and Linux.

中文简介正在准备中。当前显示原文说明。

Detects and prevents code injection attacks targeting serverless functions (AWS Lambda, Azure Functions, Google Cloud Functions) through event source poisoning, malicious layer injection, runtime command execution, and IAM privilege escalation via function modification. The analyst combines static analysis of function code, CloudTrail event correlation, runtime behavior monitoring, and IAM policy auditing to identify injection vectors across the expanded serverless attack surface including API Gateway, S3, SQS, DynamoDB Streams, and CloudWatch event triggers. Activates for requests involving Lambda security assessment, serverless injection detection, function event poisoning analysis, or serverless privilege escalation investigation.

中文简介正在准备中。当前显示原文说明。

Detect command injection attacks against Modbus TCP/RTU protocol in ICS environments by monitoring for unauthorized write operations, anomalous function codes, malformed frames, and deviations from established communication baselines using ICS-aware IDS and protocol deep packet inspection.

中文简介正在准备中。当前显示原文说明。

Detect Living Off the Land Binaries (LOLBins/LOLBAS) abuse including certutil, regsvr32, mshta, and rundll32 via process telemetry, Sigma rules, and parent-child process analysis

中文简介正在准备中。当前显示原文说明。

Detect adversary lateral movement across networks using Splunk SPL queries against Windows authentication logs, SMB traffic, and remote service abuse.

中文简介正在准备中。当前显示原文说明。

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

中文简介正在准备中。当前显示原文说明。

Identifies lateral movement techniques in enterprise networks by analyzing authentication logs, network flows, SMB traffic, and RDP sessions using Zeek, Velociraptor, and SIEM correlation rules to detect attackers moving between systems.

中文简介正在准备中。当前显示原文说明。

Detecting data exfiltration attempts from AWS S3 buckets by analyzing CloudTrail S3 data events, VPC Flow Logs, GuardDuty findings, Amazon Macie alerts, and S3 access patterns to identify unauthorized bulk downloads and cross-account data transfers.

中文简介正在准备中。当前显示原文说明。

Detect process hollowing (T1055.012) by analyzing memory-mapped sections, hollowed process indicators, and parent-child process anomalies in EDR telemetry.

中文简介正在准备中。当前显示原文说明。

Detect and prevent privilege escalation in Kubernetes pods by monitoring security contexts, capabilities, and syscall patterns with Falco and OPA policies.

中文简介正在准备中。当前显示原文说明。

Detects and analyzes Bluetooth Low Energy (BLE) security attacks including sniffing, replay attacks, GATT enumeration abuse, and Man-in-the-Middle interception. Uses Ubertooth One and nRF52840 sniffers for packet capture, the bleak Python library for GATT service enumeration, and crackle for BLE encryption cracking. Use when assessing IoT device BLE security, monitoring for BLE-based attacks on wireless infrastructure, or performing authorized BLE penetration testing. Activates for requests involving BLE security assessment, Ubertooth sniffing, GATT enumeration, or BLE replay detection.

中文简介正在准备中。当前显示原文说明。

Deploys and configures Suricata IDS/IPS with Emerging Threats rulesets, EVE JSON logging, and custom rules for real-time network traffic inspection, threat detection, and integration with SIEM platforms for centralized security monitoring.

中文简介正在准备中。当前显示原文说明。

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.

中文简介正在准备中。当前显示原文说明。

Detects prompt injection attacks targeting LLM-based applications using a multi-layered defense combining regex pattern matching for known attack signatures, heuristic scoring for structural anomalies, and transformer-based classification with DeBERTa models. The detector analyzes user inputs before they reach the LLM, flagging direct injections (system prompt overrides, role-play escapes, instruction hijacking) and indirect injections (encoded payloads, multi-language obfuscation, delimiter-based escapes). Based on the OWASP LLM Top 10 (LLM01:2025 Prompt Injection) and Simon Willison's prompt injection taxonomy. Activates for requests involving prompt injection detection, LLM input sanitization, AI security scanning, or prompt attack classification.

中文简介正在准备中。当前显示原文说明。

Detects anomalous authentication patterns using UEBA analytics, statistical baselines, and machine learning models to identify impossible travel, credential stuffing, brute force, password spraying, and compromised account behaviors across authentication logs. Activates for requests involving authentication anomaly detection, login behavior analysis, UEBA implementation, or suspicious sign-in investigation.

中文简介正在准备中。当前显示原文说明。

Detect unauthorized modifications to running containers by monitoring for binary execution drift, file system changes, and configuration deviations from the original container image.

中文简介正在准备中。当前显示原文说明。

Correlates security events in IBM QRadar SIEM using AQL (Ariel Query Language), custom rules, building blocks, and offense management to detect multi-stage attacks across network, endpoint, and application log sources. Use when SOC analysts need to investigate QRadar offenses, build correlation rules, or tune detection logic for reducing false positives.

中文简介正在准备中。当前显示原文说明。

Detect and investigate Azure service principal abuse including privilege escalation, credential compromise, admin consent bypass, and unauthorized enumeration in Microsoft Entra ID environments.

中文简介正在准备中。当前显示原文说明。

Performs memory forensics analysis using Volatility 3 to extract evidence of malware execution, process injection, network connections, and credential theft from RAM dumps captured during incident response. Covers memory acquisition, process analysis, DLL inspection, and malware detection. Activates for requests involving memory forensics, RAM analysis, Volatility framework, memory dump investigation, volatile evidence analysis, or live memory acquisition.

中文简介正在准备中。当前显示原文说明。

Builds a structured SOC incident response playbook for ransomware attacks covering detection, containment, eradication, and recovery phases with specific SIEM queries, isolation procedures, and decision trees. Use when SOC teams need formalized response procedures for ransomware incidents aligned to NIST SP 800-61 and MITRE ATT&CK ransomware techniques.

中文简介正在准备中。当前显示原文说明。

Simulates man-in-the-middle attacks using Ettercap, mitmproxy, and Bettercap in authorized environments to intercept, analyze, and modify network traffic for testing encryption enforcement, certificate validation, and detection capabilities.

中文简介正在准备中。当前显示原文说明。

Systematically collects, categorizes, and distributes indicators of compromise (IOCs) during and after security incidents to enable detection, blocking, and threat intelligence sharing. Covers network, host, email, and behavioral indicators using STIX/TAXII formats and threat intelligence platforms. Activates for requests involving IOC collection, indicator extraction, threat indicator sharing, compromise indicators, STIX export, or IOC enrichment.

中文简介正在准备中。当前显示原文说明。

Builds automated threat intelligence feed integration pipelines connecting STIX/TAXII feeds, open-source threat intel, and commercial TI platforms into SIEM and security tools for real-time IOC matching and alerting. Use when SOC teams need to operationalize threat intelligence by automating feed ingestion, normalization, scoring, and distribution to detection systems.

中文简介正在准备中。当前显示原文说明。