Specialized Cyber Defense & Operational Agents
Problem Statement
During an active Layer-7 DDoS attack or web application vulnerability exploitation campaign, manual creation of Web Application Firewall (WAF) blocking rules takes too long, allowing malicious traffic to overwhelm backend web servers.
Goal
Build an AI agent that analyzes real-time HTTP web request telemetry, detects malicious pattern clusters (e.g., specific User-Agents, URIs, or parameter payloads), and auto-deploys custom WAF blocking rules.
Outcome Measurement
- Mitigation Velocity: Dynamic WAF rule generated and deployed in under 10 seconds.
- Uptime Assurance: Preserved 99.99% application availability during simulated DDoS attacks.
How the Code was Built
Identifies request anomalies and constructs Cloudflare / AWS WAF custom JSON blocking rules.
Executable Code
from pydantic import BaseModel, Fieldfrom langchain_openai import ChatOpenAIclass WAFRuleDeployment(BaseModel): rule_name: str target_metric: str = Field(description="HEADER, URI, IP, or USER_AGENT") match_pattern: str action: str = "BLOCK" aws_waf_json: strdef generate_dynamic_waf_rule(attack_log_sample: str) -> WAFRuleDeployment: llm = ChatOpenAI(model="gpt-4o", temperature=0) structured_llm = llm.with_structured_output(WAFRuleDeployment) prompt = f"Analyze DDoS / L7 attack traffic sample and generate immediate AWS WAF blocking rule payload:\n{attack_log_sample}" return structured_llm.invoke(prompt)if __name__ == "__main__": sample_traffic = "1000 requests/sec with User-Agent: 'Python-urllib/3.10-DDoS-Bot' to URI: '/login?attack=true'" res = generate_dynamic_waf_rule(sample_traffic) print(f"WAF Rule: {res.rule_name}") print(f"Pattern: {res.match_pattern}") print("AWS WAF Rule JSON:\n", res.aws_waf_json)
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