Results for “traffic-anomaly-detection”

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

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.

"Anomaly" covers three distinct problems, and a detector built for one is close to

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.

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

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 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.

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.

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.

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.

Detects lateral movement techniques including Pass-the-Hash, PsExec, WMI execution, RDP pivoting, and SMB-based spreading using SIEM correlation of Windows event logs, network flow data, and endpoint telemetry mapped to MITRE ATT&CK Lateral Movement (TA0008) techniques.

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

Extract violation details, fine amounts, vehicle information, and payment deadlines from traffic fine notices.

Detect domain fronting C2 traffic by analyzing SNI vs HTTP Host header mismatches in proxy logs and TLS certificate discrepancies using pyOpenSSL for certificate inspection.

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

Detect and prevent ARP spoofing attacks using ARPWatch, Dynamic ARP Inspection, Wireshark analysis, and custom Python monitoring scripts to protect against man-in-the-middle interception.

Detect C2 beaconing patterns in network traffic using frequency analysis, jitter detection, and domain reputation to identify compromised endpoints communicating with adversary infrastructure.

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.

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.

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

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

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.

Monitor dividend portfolios for risk triggers and route anomalies into a human review queue without auto-selling.

Use to scan support conversations for a harm nobody has categorised yet — a new failure mode, a scam pattern, or a product change hurting a group of customers before it shows in any metric. Trigger for "is anything new going wrong", "scan for emerging issues", "early warning from support", horizon scanning, a new scam pattern, or preparing for a question about what you might be missing.