OpenArmor and OpenArmor Dashboards

Implementation

The implementation of the advanced logging and analysis system follows a phased approach. Phase 1 focuses on deploying eBPF logging, kernel space logging, and structured logging frameworks to capture comprehensive system events and activities. Phase 2 involves standardizing logs using OCSF, implementing AI log processing models for analysis, and developing threat detection rules. Phase 3 will mainly focus on the Log processing through the means of Artificial Intelligence(AI). In Phase 4, the obtained log data will be processed and analyzed for the automation of threat detection process. Phase 5 integrates the analysis results with monitoring systems for incident response, while Phase 6 emphasizes continuous improvement through feedback loops, model updates, and threat intelligence integration. Each phase employs cutting-edge techniques like machine learning, natural language processing, and automated correlation to ensure robust security posture and proactive threat detection.

Phase 1: Advanced Logging

eBPF Logging: Utilize eBPF (Extended Berkeley Packet Filter) to capture low-level system events and kernel activities with minimal overhead. eBPF allows you to safely run sandboxed programs in the kernel space, enabling efficient logging of system calls, network events, and other kernel-level activities.
Kernel Space Logging: Implement kernel modules or leverage existing kernel instrumentation frameworks (e.g., ftrace, perf, or LTTng) to capture kernel-level events directly from the operating system kernel. This enables logging of events such as process creation, file access, network traffic, and system resource utilization.
Structured Logging: Adopt structured logging formats like JSON or Protocol Buffers to ensure consistent and machine-readable log data across applications and services. Structured logging allows for easier parsing, indexing, and analysis of log data.

Phase 2: Log Standardization and Analysis

OCSF Standardization: Implement the Open Cybersecurity Schema Framework (OCSF) or similar industry standards to normalize log data from various sources into a consistent format. This standardization process typically involves parsing, enriching, and mapping log data to a common schema, enabling seamless integration and analysis across different systems.
AI Log Processing: Train and deploy machine learning models, such as natural language processing (NLP) models or deep learning architectures, to analyze log data and extract valuable insights. These models can be used for tasks like log classification, anomaly detection, and root cause analysis.
Threat Detection Rules: Develop and maintain a library of threat detection rules based on known attack patterns, security vulnerabilities, and industry best practices. These rules can be based on signatures, heuristics, or machine learning models, and can be applied to the standardized log data to identify potential security threats or policy violations.
Automated Correlation: Implement automated log correlation techniques to connect and analyze related events across different log sources. This can help identify complex attack patterns, track user activities, and provide a comprehensive view of security incidents.

Phase 3: AI Log Processing

Employ natural language processing (NLP) techniques to extract relevant information from unstructured log data, such as error messages, stack traces, and free-text descriptions.
Use unsupervised machine learning algorithms like clustering and anomaly detection to identify unusual patterns or deviations from normal behavior in log data.
Implement sequence learning models, such as recurrent neural networks (RNNs) or transformer architectures, to analyze log sequences and detect anomalies based on temporal patterns.
Develop supervised machine learning models, like decision trees or neural networks, to classify log events based on labeled training data from known incidents or threats.

Phase 4: Automated Threat Detection

Implement rule-based threat detection engines that leverage predefined signatures or patterns to identify known threats, such as malware infections, unauthorized access attempts, or policy violations.
Develop statistical anomaly detection models that can identify deviations from expected behavior or baseline patterns, indicating potential threats or compromised systems.
Utilize graph-based analysis techniques to identify suspicious relationships or interactions between entities (e.g., users, processes, network connections) in log data.
Implement user and entity behavior analytics (UEBA) models that can detect insider threats, compromised accounts, or policy violations by analyzing user activities and behavioral patterns.
Integrate threat intelligence feeds and indicators of compromise (IoCs) from external sources to enhance threat detection capabilities and identify emerging threats.
Continuously retrain and update machine learning models with new log data and labeled incidents to improve accuracy and adapt to evolving threats.

Phase 5: Monitoring and Response

Set up a centralized logging and analysis platform to collect, store, and process logs from multiple sources in real-time.
Integrate the analysis results with security information and event management (SIEM) systems for comprehensive monitoring and incident response.
Define incident response procedures and workflows to investigate and mitigate detected threats based on the automated threat detection alerts.

Phase 6: Continuous Improvement

Establish a feedback loop to continuously refine and improve the logging, analysis, and threat detection processes based on real-world incidents and evolving security threats.
Regularly update and enhance the AI log processing models, threat detection rules, and logging mechanisms to adapt to new attack vectors and techniques.
Leverage machine learning and data analytics to identify emerging patterns and trends in log data, enabling proactive security measures and threat intelligence gathering.

Conclusion Methodology