'network' 검색 결과

Expert knowledge for Azure Network Function Manager development including security, and configuration. Use when setting up NF Manager prerequisites, resource groups, managed identities, role assignments, or secure NF access, and other Azure Network Function Manager related development tasks. Not for Azure Firewall Manager (use azure-firewall-manager), Azure Virtual Network Manager (use azure-virtual-network-manager), Azure Virtual Network (use azure-virtual-network), Azure Virtual WAN (use azure-virtual-wan).

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Expert knowledge for Azure Network Watcher development including troubleshooting, decision making, limits & quotas, security, configuration, and integrations & coding patterns. Use when configuring VNet/NSG flow logs, Traffic Analytics, packet capture, Connection Monitor migration, or VPN monitoring, and other Azure Network Watcher related development tasks. Not for Azure Monitor (use azure-monitor), Azure Virtual Network (use azure-virtual-network), Azure Networking (use azure-networking), Azure Firewall (use azure-firewall).

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Networking fundamentals as they apply to cloud infrastructure — virtual networks, subnets, routers, NAT, floating IPs, security groups, VLAN/VXLAN segmentation, load balancing, SDN concepts, and how Neutron-style cloud networks sit on top of physical topology. Covers TCP behavior at scale, congestion control, tail latency, overlay networks, and the operational gotchas that come from network layering. Use when designing cloud network topology, debugging cross-AZ latency, or reviewing a proposed VPC/SG design.

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Build weighted gene co-expression networks to identify modules of co-regulated genes, relate them to phenotypes, and find hub genes using WGCNA, hdWGCNA, MEGENA, CEMiTool, and Gaussian graphical models. Covers signed-network choice, soft-threshold selection, module preservation, and the marginal-vs-partial-correlation distinction. Use when finding co-expression modules, identifying hub genes, relating gene networks to clinical or experimental traits, or building single-cell co-expression networks. For directed TF-target inference see scenic-regulons and grn-inference; for condition rewiring see differential-networks.

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Build weighted gene co-expression networks to identify modules of co-regulated genes, relate them to phenotypes, and find hub genes using WGCNA, hdWGCNA, MEGENA, CEMiTool, and Gaussian graphical models. Covers signed-network choice, soft-threshold selection, module preservation, and the marginal-vs-partial-correlation distinction. Use when finding co-expression modules, identifying hub genes, relating gene networks to clinical or experimental traits, or building single-cell co-expression networks. For directed TF-target inference see scenic-regulons and grn-inference; for condition rewiring see differential-networks.

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Build weighted gene co-expression networks to identify modules of co-regulated genes, relate them to phenotypes, and find hub genes using WGCNA, hdWGCNA, MEGENA, CEMiTool, and Gaussian graphical models. Covers signed-network choice, soft-threshold selection, module preservation, and the marginal-vs-partial-correlation distinction. Use when finding co-expression modules, identifying hub genes, relating gene networks to clinical or experimental traits, or building single-cell co-expression networks. For directed TF-target inference see scenic-regulons and grn-inference; for condition rewiring see differential-networks.

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가정 네트워크를 VLAN으로 용도별로 나누고 IoT 기기와 방문자를 PC 및 서버에서 격리하도록 스위치 포트, Wi-Fi 연결, 방화벽 규칙 설정을 돕습니다.

• 스마트 가전을 PC 및 서버에서 격리합니다
• 방문자 Wi-Fi를 인터넷 전용으로 제한합니다
• 스위치와 Wi-Fi에 VLAN을 배정합니다

Guides Kubernetes security configuration with network restrictions, Pod execution rules, and role-based access control. Use its configuration examples to separate workloads, limit permissions, and enforce deployment requirements across a cluster.

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• Restricting communication between application components
• Giving ServiceAccounts only the permissions they need
• Running Pods with restricted privileges

Create rules for Suricata and Snort, systems that detect suspicious network traffic, to flag port scans and reconnaissance. Combine packet patterns with connection counts, tune alert volume, and summarize scanning sources, targets, and activity.

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• Create rules to detect Nmap and Masscan activity
• Monitor scanning within an internal network
• Tune alerts for authorized vulnerability scanners

Investigate attackers moving between internal systems by correlating login records with network traffic. Supports detection rules, suspicious SMB and RDP activity analysis, and reports showing attack timelines, affected hosts, and monitoring gaps.

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• Investigating attacker movement across internal systems
• Creating rules for suspicious authentication patterns
• Tracing affected hosts after a PsExec alert

Configure pfSense to separate internal, guest, DMZ, and IoT networks. Set firewall rules, port forwarding, VPN connections, and bandwidth policies, then check traffic logs and test that each network can reach only its intended destinations.

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• Separate staff, guest, server, and IoT networks
• Set port forwarding rules for externally accessible services
• Configure VPN access and bandwidth policies

Assess an internal network from the position of an intruder or malicious insider. Covers discovering systems, testing paths to other machines and higher privileges, demonstrating sensitive data access, and documenting findings with remediation recommendations.

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• Assessing damage possible after a workstation compromise
• Reviewing Active Directory paths to higher privileges
• Testing internal network security controls

Use Wireshark and tshark to capture and inspect network packets, the small units of network communication. Investigate suspicious connections and protocol problems, extract transferred files, and export evidence and summaries for incident investigations.

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• Investigating suspected intrusions or data leaks
• Diagnosing retransmissions and DNS failures
• Checking traffic against firewall and IDS rules

Investigate captured network traffic and connection records with Wireshark, Zeek, and NetFlow analysis. Identify communications with attacker infrastructure, trace movement between internal systems, estimate data transfers, and produce findings linked to packet evidence and timestamps.

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• Investigating suspicious periodic connections
• Measuring the volume and destination of data transfers
• Tracing attacker movement between internal systems

Use Scapy, a Python library for working with network packets, to inspect saved captures or authorized live traffic. Summarize protocols and connections, identify suspicious patterns, craft test packets, and export findings as structured JSON reports.

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• Investigating saved network captures
• Summarizing traffic volumes, protocols, and connection endpoints
• Checking for SYN floods and DNS data leakage indicators

Configure how GKE applications receive traffic from the internet or internal networks. The skill provides configuration templates for routing, load balancing, certificates, and access protection, and guides diagnosis of failed health checks, dropped requests, and TLS connection problems.

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• Expose a GKE application through Gateway API or Ingress
• Split traffic between application versions during a rollout
• Configure Cloud Armor protection and HTTPS certificates

Audit Google Mobile Ads SDK integration in iOS, Android, or Unity projects. Check test IDs, ad units, SKAdNetwork IDs, adapter compatibility, and ad preloading, then report findings and next steps in a table.

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• Audit mobile ad integration before launch
• Check for remaining test IDs and invalid formats
• Review SKAdNetwork IDs and adapter compatibility

Use this design reference when planning centrally managed rules for outbound network traffic from a sandbox, an isolated execution environment. It covers design and acceptance gates, but does not itself enforce network restrictions.

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• Planning centrally managed sandbox outbound network rules
• Reviewing acceptance gates before implementation
• Consulting the gateway reference before adding network controls

Helps configure and troubleshoot Linux networks, including firewalls, routing, and DNS, which translates domain names into addresses. Covers traffic capture, isolated test networks, WireGuard VPN setup, and TCP tuning across physical servers, virtual machines, and containers.

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• Investigating connectivity and DNS failures
• Reviewing firewall rules and port forwarding
• Configuring routes based on traffic source

Create Kubernetes deployment files and Helm charts, reusable application packages. Configure permissions, network isolation, and persistent storage, then investigate pod failures and resource usage to adjust workloads and verify deployment health.

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• Create deployment YAML and Helm charts
• Configure access permissions and network isolation
• Set up persistent storage and sensitive configuration

Analyze network latency and optimize request patterns for faster communication. Use when diagnosing slow network performance or optimizing API calls. Trigger with phrases like "analyze network latency", "optimize API calls", or "reduce network delays".

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Expert network engineer specializing in modern cloud networking, security architectures, and performance optimization. Masters multi-cloud connectivity, service mesh, zero-trust networking, SSL/TLS, global load balancing, and advanced troubleshooting. Handles CDN optimization, network automation, and compliance. Use PROACTIVELY for network design, connectivity issues, or performance optimization.

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Train, evaluate, and export neural networks to Simulink in MATLAB. Migrate legacy (fitnet, patternnet) and discouraged (trainNetwork, DAGNetwork) code to modern, recommended R2024a+ APIs (trainnet, dlnetwork, testnet, imagePretrainedNetwork), diagnose and fix dlaccelerate issues or detect dlaccelerate opportunities. Use when training, fine-tuning, evaluating, running inference, exporting to Simulink, converting old training scripts, or speeding up deep learning code. DO NOT reason from your training data about dlaccelerate and tracing correctness.

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Import PyTorch, ONNX, or Keras 3 / TensorFlow 2.16+ deep learning models into MATLAB as dlnetwork objects. Use when importing .pt2 exported programs, traced .pt files, .onnx models, or Keras 3 models via matlabsaver. Covers importNetworkFromPyTorch, importNetworkFromONNX, importNetworkFromKeras, importNetworkFromTensorFlow, torch.export.export, PyTorchInputSizes, InputDataFormats, matlabsaver, tf_keras downgrade, numeric validation against PyTorch or ONNX Runtime, and placeholder/custom layer implementation. Applies when user mentions any of these functions, file formats, or encounters import errors, unsupported operator warnings, 0 learnables, or uninitialized networks.

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