Network forensics and log analysis remain time-intensive tasks in cybersecurity operations. Traditional analysis workflows require security analysts to manually parse packet captures and log files using command-line tools like tshark, grep, and awk. This process demands specialized knowledge of query syntax, protocol structures, and data formats.

Atherius addresses this challenge by combining large language models with automated security analysis tools. The system accepts natural language queries and translates them into executable analysis workflows, eliminating the need for manual command construction.

The platform handles three primary data types: network packet captures (PCAP files), system logs, and security event data. Users can upload these files and query them conversationally, receiving structured analysis results within seconds.

The system architecture consists of three main components: a Next.js web interface, a Python-based analysis engine, and a LangChain-powered orchestration layer. The web interface handles file uploads and presents analysis results through an interactive chat interface.

The Python analysis engine implements 30+ specialized functions for security data processing. These functions wrap existing tools (tshark, pandas, re) and provide standardized outputs that the language model can interpret. Each function serves a specific purpose: extracting DNS queries, identifying TCP conversations, parsing log timestamps, or computing traffic statistics.

LangChain orchestrates the interaction between the language model (Gemini 1.5 Flash) and the analysis functions. When a user submits a query, the system uses ReAct (Reasoning and Acting) prompting to break down the question, select appropriate tools, execute analysis, and synthesize results.

All file processing happens in ephemeral storage. Uploaded PCAP and log files are deleted immediately after analysis completes, ensuring sensitive network data never persists on disk.

The analysis workflow follows a multi-step process. First, the system parses the natural language query to identify the analysis objective (e.g., "find DNS traffic to suspicious domains"). Second, it selects relevant analysis functions from the available toolkit.

Function selection uses the language model's understanding of security operations. For DNS-related queries, it might invoke extract_dns_queries, get_tcp_conversations, and analyze_traffic_patterns in sequence. Each function returns structured data that feeds into subsequent analysis steps.

The system validates function outputs before presenting results. If a tshark command returns empty data or malformed output, the orchestration layer attempts alternative extraction methods. This validation reduces false positives and improves result reliability.

Results are formatted as markdown documents with embedded tables and code blocks. The language model annotates findings with context about what the data indicates and suggests follow-up investigation steps.

Network analysis capabilities focus on packet-level inspection and protocol analysis. The system can extract protocol distributions, identify largest data transfers, map source-destination relationships, and detect beaconing patterns.

Protocol analysis breaks down traffic by application layer protocols (HTTP, DNS, TLS) and transport protocols (TCP, UDP). This helps identify unusual protocol usage or data exfiltration attempts disguised as legitimate traffic.

The conversation tracking feature groups related packets into sessions. For TCP traffic, this means identifying complete three-way handshakes, data transfer volumes, and connection teardowns. For UDP, it groups packets by source-destination pairs and time windows.

Temporal analysis examines packet timing to detect anomalies. Regular intervals between packets might indicate automated beaconing. Sudden traffic spikes could signal data exfiltration or network scanning.

The anomaly detection module identifies traffic patterns that deviate from expected baselines. Rather than using predefined signatures, it applies statistical methods to flag outliers in packet size, timing, protocol usage, and connection behavior.

Statistical analysis calculates distributions for key metrics: packet inter-arrival times, payload sizes, and connection durations. Traffic that falls outside normal ranges (e.g., 3 standard deviations from mean) gets flagged for investigation.

Behavioral analysis looks at host communication patterns. A single host contacting hundreds of internal IPs in short succession indicates potential reconnaissance. Repeated connections to the same external IP with fixed intervals suggest command and control activity.

The system presents anomalies with supporting evidence: time series graphs showing traffic spikes, tables ranking hosts by anomaly score, and packet samples demonstrating unusual behavior. Analysts can then investigate flagged activity using standard forensic procedures.

DNS analysis extracts query-response pairs from packet captures and correlates them with subsequent network activity. This helps trace how domain resolutions lead to data transfers or command execution.

The system identifies DNS queries with successful responses (rcode=0), failed resolutions, and queries to non-existent domains. Each resolved domain is linked to its IP addresses and the hosts that initiated queries.

Domain aggregation groups queries by target domain, revealing which external infrastructure receives the most requests. Multiple domains resolving to the same IP range often indicates shared hosting infrastructure or coordinated campaigns.

For each domain, the system can retrieve full traffic details: query timestamps, requesting hosts, resolved IPs, and protocols used for subsequent connections. This makes it straightforward to trace an infection from initial DNS lookup through data exfiltration.

Log analysis handles structured and unstructured log formats from various sources: system logs (syslog), application logs, and security tool outputs. The parsing engine recognizes common timestamp formats, severity levels, and message structures.

Cross-log correlation matches events across different sources based on timestamps and contextual information. An authentication failure in auth.log can be correlated with network connections in firewall logs and process execution in system logs.

Time-window queries filter events within specified intervals. Users can ask for "authentication events followed by privilege escalation within 5 minutes," and the system constructs appropriate temporal filters.

Pattern matching identifies unusual sequences in log data: service accounts authenticating from multiple IPs, access to sensitive files outside business hours, or processes spawning in unexpected hierarchies. These behavioral indicators often reveal intrusions that signature-based tools miss.

Timeline reconstruction builds chronological narratives of incident progression. The system pulls relevant entries from all log sources and presents them in temporal order with context about related events.

The backend uses FastAPI for REST endpoints and WebSocket connections for real-time result streaming. File uploads are handled through multipart form data with size limits of 100MB for PCAP files and 50MB for logs.

Analysis functions execute as subprocess calls with timeout protection. If a tshark command doesn't complete within 30 seconds, the system kills the process and returns a timeout error. This prevents resource exhaustion from malformed inputs.

The language model interface uses streaming responses to provide incremental results. As each analysis function completes, its output is streamed to the frontend. Users see progress in real time rather than waiting for complete analysis.

Error handling covers common failure modes: invalid PCAP format, empty log files, malformed tshark filters, and command execution errors. Each error type returns specific feedback about what went wrong and how to fix it.

Query response times vary based on analysis complexity and data volume. Simple queries (e.g., "show protocol distribution") typically complete in 5-10 seconds for PCAP files under 10MB. Complex queries requiring multiple tool invocations may take 15-30 seconds.

The system processes large PCAP files through sampling when appropriate. For traffic overviews, analyzing every 10th packet provides sufficient statistical accuracy while reducing processing time by 90%.

Memory usage scales with file size. A 50MB PCAP file requires approximately 200MB RAM during processing. The system enforces memory limits to prevent exhaustion on shared hosting environments.

Concurrent queries are supported through async execution. Multiple users can submit analysis requests simultaneously without blocking. Each request gets its own execution context and file storage.

Security operations teams use Atherius for incident triage. When an alert fires, analysts upload relevant PCAP and log files, then query for specific indicators: connections to known malicious IPs, unusual data transfers, or authentication anomalies.

Network troubleshooting benefits from conversational packet analysis. Engineers can ask "why is this host retransmitting packets" or "what DNS servers are being queried" without remembering exact tshark filter syntax.

Training scenarios use the platform to teach junior analysts forensic techniques. Rather than starting with command-line tools, trainees can focus on security concepts while the system handles technical implementation.

Compliance reporting leverages the automatic documentation. Every analysis session produces an audit trail showing which data was examined, what queries were run, and what conclusions were reached. This satisfies many regulatory requirements for incident documentation.

Testing with real-world incident data shows significant time reduction compared to manual analysis. Tasks that previously required 45-60 minutes of tshark commands now complete in 8-12 minutes through natural language queries.

Accuracy evaluation compared Atherius outputs against expert manual analysis on 50 packet captures. The system identified 94% of the same security-relevant findings, with false positives under 5%.

User studies with SOC analysts indicate reduced cognitive load. Analysts reported spending less time on tool mechanics and more time on security interpretation. This shift allows junior analysts to perform analyses previously requiring senior expertise.

The audit trail generation has proven valuable for compliance documentation. Organizations using the platform report 60% reduction in time spent documenting incident investigations for regulatory submissions.