Results for “network”

Create, analyze, and visualize complex networks and graphs in Python with NetworkX, including graph algorithms, community detection, synthetic network generation, and multiple I/O formats.

Expert knowledge for Azure Networking development including troubleshooting, decision making, architecture & design patterns, and security. Use when designing hub-spoke VNets, Azure Firewall/NSG rules, App Gateway/Front Door WAF, DDoS, or fixing Microsoft.Network errors, and other Azure Networking related development tasks. Not for Azure Virtual Network (use azure-virtual-network), Azure Virtual Network Manager (use azure-virtual-network-manager), Azure Virtual WAN (use azure-virtual-wan), Azure Network Watcher (use azure-network-watcher).

Comprehensive toolkit for creating, analyzing, and visualizing complex networks and graphs in Python. Use when working with network/graph data structures, analyzing relationships between entities, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks, or visualizing network topologies. Applicable to social networks, biological networks, transportation systems, citation networks, and any domain involving pairwise relationships.

NetworkX is a Python package for creating, manipulating, and analyzing complex networks and graphs.

NetworkX is a Python package for creating, manipulating, and analyzing complex networks and graphs.

NetworkX is a Python package for creating, manipulating, and analyzing complex networks and graphs.

NetworkX is a Python package for creating, manipulating, and analyzing complex networks and graphs.

NetworkX is a Python package for creating, manipulating, and analyzing complex networks and graphs.

NetworkX is a Python package for creating, manipulating, and analyzing complex networks and graphs.

NetworkX is a Python package for creating, manipulating, and analyzing complex networks and graphs.

Create, analyze, and visualize complex networks and graphs in Python using NetworkX, including graph construction, algorithms, generators, I/O, and plotting.

NetworkX is a Python package for creating, manipulating, and analyzing complex networks and graphs.

NetworkX is a Python package for creating, manipulating, and analyzing complex networks and graphs.

Capture and analyze network traffic using Wireshark and tshark to reconstruct network events, extract artifacts, and identify malicious communications.

Produces NetworkX code with explicit graph kind, node identity, edge multiplicity, direction, attribute schema, weight semantics, and algorithm preconditions, including testing.

Internal network assessment. VLAN hopping, ARP spoofing detection, broadcast protocol abuse (LLMNR/NBT-NS/mDNS), network segmentation verification, SNMP enumeration, NFS exposure, router/switch audit, and internal service mapping. Assumes attacker has network access. Uses nmap, arp-scan, nbtscan, snmpwalk, onesixtyone, smbmap, nfs-common, masscan, hping3, and netexec.

Configures and troubleshoots network infrastructure: DNS records, reverse proxies, SSL certificates, firewalls, CDNs, and load balancers.

Build and maintain professional relationships through strategic outreach, events, and follow-up, with structured frameworks for deliberate practice.

Implements Kubernetes network policies for micro-segmentation, covering ingress/egress rules, Calico and Cilium, with a Python CLI for managing and validating policies.

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.

Create, manipulate, and analyze complex networks and graphs with the NetworkX Python package, covering graph construction, algorithms, generators, I/O, and visualization.

ENet reliable UDP skill for channels and packet fragmentation.

Investigates Google Cloud networking issues by analyzing logs, metrics, and diagnostics, including VPC Flow Logs, NAT, firewall, threat logs, latency, throughput, and Connectivity Tests.

Comprehensive toolkit for creating, analyzing, and visualizing complex networks and graphs in Python. Use when working with network/graph data structures, analyzing relationships between entities, computing graph algorithms (shortest paths, centrality, clustering), detecting...

Reads existing per-leader research files to map co-authorships, endorsements, and organizational ties across movement leaders, producing a cross-leader network graph with clusters, bridges, and isolates.

嵌入式网络调试工具，用于发现接口、抓包、分析 pcap/pcapng、做连通性测试、端口扫描和流量统计。 当用户提到 Wireshark、tshark、Npcap、抓包、网络联调、端口扫描、连通性排查、pcap 分析、 网络接口、ping 测试、traceroute、流量统计、Modbus TCP、EtherNet/IP 等网络协议调试时自动触发， 也兼容 /net 显式调用。即使用户只是说"抓个包看看"、"扫一下端口"、"网络通不通"或"分析一下这个 pcap"， 只要上下文中出现具体工具名（tshark、Wireshark、Npcap）、协议名（Modbus TCP、EtherNet/IP、ICMP 等）、 调试动作（抓包、端口扫描、连通性测试、ping、traceroute、流量统计、pcap 分析）或网络接口操作，就应触发此 skill。

Analyzes network traffic captures and flow data to identify adversary activity during security incidents, including command-and-control communications, lateral movement, data exfiltration, and exploitation attempts.

Creates, analyzes, and visualizes complex networks and graphs in Python with NetworkX. Use when working with network/graph data structures, analyzing relationships between entities, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks, or visualizing topologies — applicable to social, biological, transportation, citation, and any pairwise-relationship networks. This is classical graph analytics, not deep learning — for training graph neural networks (GCN/message passing, node/edge/graph classification on Cora-style data) use alterlab-torch-geometric instead. Part of the AlterLab Academic Skills suite.

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

Analyzes malware-generated network traffic from PCAP files to identify C2 protocols, data exfiltration, DNS tunneling, and beaconing patterns using Wireshark, Zeek, Suricata, and Python.