Results for “network-anomaly-detection”

Detect anomalies in DNP3 protocol communications used in SCADA systems by monitoring for unauthorized control commands, firmware update attempts, protocol violations, and deviations from baseline traffic patterns using deep packet inspection and machine learning approaches.

Deploys anomaly detection for industrial control environments using machine learning models trained on OT network baselines, physics-based process models, and behavioral analysis of industrial protocol communications.

Detects anomalous authentication patterns using UEBA analytics, statistical baselines, and machine learning to identify impossible travel, credential stuffing, brute force, password spraying, and compromised account behaviors across authentication logs.

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.

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.

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

Parse NetFlow v9 and IPFIX records to detect volumetric anomalies, port scanning, data exfiltration, and C2 beaconing patterns using the Python netflow library.

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.

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.

Detects anomalies in Modbus/TCP and Modbus RTU communications in industrial control systems using Zeek, Suricata, and custom Python analysis.

Detect suspicious Windows service installations (MITRE ATT&CK T1543.003) by parsing System event logs for Event ID 7045, analyzing service binary paths, and identifying indicators of persistence mechanisms.

Analyze Zeek network logs to detect lateral movement techniques including SMB admin share access, DCE/RPC remote service creation, NTLM account spray, Kerberos anomalies, and large internal data transfers.

Detect anomalous user activities including impossible travel, unusual access patterns, privilege abuse, and insider threats using SIEM-based behavioral baselines and statistical analysis.

Detects unusual options activity and classifies smart money signals to help follow institutional positioning, including volume/OI ratio spikes, block trades, sweep orders, and net premium flow.

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

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.

Detect insider threats by modeling normal user and entity behavior with Elasticsearch, computing anomaly scores, and correlating low-confidence indicators into high-confidence alerts.

Detects command-and-control (C2) communications tunneled through DNS protocol, including DNS tunneling tools, domain generation algorithms, and encoded payload delivery via TXT/CNAME records.

Detect DNS tunneling and data exfiltration by analyzing Zeek dns.log for high-entropy subdomain queries, excessive query volume, long query lengths, and unusual DNS record types indicating covert channel communication.

Analyzes Zeek conn.log connection intervals using statistical methods to detect C2 beaconing patterns, flagging periodic connections with low jitter.