Botnet Detection via Command-and-Control Access Patterns

The patent transforms the detection approach from signature-based (static) to behavior-based (dynamic) by monitoring command-line parameters and access patterns. Instead of detecting malware based on known binary signatures, the system detects botnet activity by analyzing changes in system behavior parameters such as command-line arguments, file access patterns, and network connection behaviors, enabling detection of both known and new malware variants.

The patent replaces the mechanical malware scanning system with a machine learning-based behavioral analysis system. The supervised learning model substitutes traditional signature-matching mechanisms with a system that learns and detects anomalous behavioral patterns, enabling detection of obfuscated and new malware that lack known signatures.

The patent introduces an intermediary layer of behavioral analysis between the bot and CnC server. Instead of directly detecting encoded communications, the system monitors the intermediary behaviors - command-line executions, file accesses, and network connection patterns - that precede and accompany CnC communications. This intermediary behavioral signature detection provides more reliable identification than direct message analysis.

The patent converts the harmful obfuscation techniques used by botnets into detectable patterns. The encoded messages, hidden server locations, and indirect communication methods that were designed to evade detection actually create distinctive behavioral signatures in command-line usage and access patterns. By analyzing these transformed indicators, the system turns the attackers' evasion techniques into detection opportunities.

The patent implements feedback through supervised learning where labeled training data provides ground truth for model improvement. The system uses feedback from known botnet behaviors to refine detection thresholds and patterns, learning from past mistakes to reduce false positives. This feedback mechanism allows the system to distinguish between legitimate anomalies and actual botnet activity, improving reliability while maintaining adaptability.

The patent performs preliminary classification of anomalies through supervised learning before triggering alerts. Instead of immediately alerting on every detected anomaly, the system first evaluates anomalies against learned patterns from training data, pre-filtering out benign variations. This preliminary action reduces false positives by distinguishing between normal variations and genuine botnet indicators before alert generation.

The patent creates a universal detection framework that functions across diverse cloud environments and virtual machine configurations. The behavioral analysis approach and supervised learning model are architecture-agnostic, working effectively whether monitoring physical servers, virtual machines, or containerized environments. This universality enables the same system to scale across heterogeneous cloud infrastructures without requiring environment-specific implementations.

The patent uses copying by deploying identical detection agents and models across multiple virtual machines and cloud instances. Rather than requiring customized detection systems for each environment, the same behavioral monitoring agent and supervised learning model are replicated across the infrastructure, enabling scalable deployment that maintains consistent detection capabilities across thousands of virtual machines.