Results for “network-scanning”

Craft, send, sniff, and dissect network packets using Scapy for protocol analysis, network reconnaissance, and traffic anomaly detection in authorized security testing.

Analyze network packet captures (PCAP/PCAPNG) using Wireshark, tshark, tcpdump, and Python to reconstruct communications, extract files, and identify malicious traffic.

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.

Captures and analyzes network packet data using Wireshark and tshark to identify malicious traffic patterns, diagnose protocol issues, extract artifacts, and support incident response investigations on authorized network segments.

Automates packet capture analysis using tshark and pyshark to extract protocol statistics, detect suspicious flows, identify IOCs, and analyze DNS anomalies from PCAP files.

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.

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.

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

Analyze Zeek conn.log and NetFlow data to detect ransomware network indicators including C2 beaconing, TOR exit node connections, data exfiltration, and suspicious DNS patterns.

Identify command-and-control beaconing patterns in network traffic by applying statistical frequency analysis, jitter calculation, and coefficient of variation scoring to detect periodic callbacks from compromised endpoints.

Create, manipulate, and analyze complex networks and graphs using the NetworkX Python package.

Create, manipulate, and analyze complex networks and graphs using the NetworkX Python package, including algorithms, generators, I/O, and visualization.

Build network traffic baselines from NetFlow/IPFIX data using Python pandas for statistical analysis, z-score anomaly detection, and hourly/daily traffic pattern profiling.

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.

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

Provides a standardized interface for ingesting raw data across domains such as genomics, network analysis, document review, and spatial mapping, converting it into semantic vectors for agent use.