Guidelines for Detection Engineering in Dynamic Systems

Guidelines for Detection Engineering in Dynamic Systems

Hello, detection engineering does not have to be overcomplicated. This article will help you and your team build detection engineering considerations.

Find the tools to establish these values and create a table-top exercise game for your team here.

Understand the System

1. System Understanding: Begin by gaining a deep understanding of the dynamic system you’re responsible for detecting anomalies in. This involves learning how the system operates, the significance of control actions, the impact of system noise, and the expected behavior under normal conditions.

2. Define Normal Behavior: Define what constitutes “normal” behavior for your system. Understand how the system’s state should evolve over time in standard operating conditions and identify associated costs for different states and control actions.

3. Identify Anomalies: Focus on identifying potential anomalies or deviations from normal behavior. These anomalies may result from unexpected changes in control actions, external disturbances, or system failures. Utilize techniques such as statistical analysis, machine learning, or domain-specific knowledge to spot these anomalies.

4. Data Collection: Collect data from the dynamic system. This may involve acquiring real-time or historical data on the system’s states, control actions, and relevant sensor measurements.

5. Feature Engineering: Identify the relevant features and data points that can be used for anomaly detection. Key features may include the system state, control actions, and available sensor data.

6. Algorithm Selection: Choose appropriate detection algorithms or techniques that align with your data and the nature of anomalies you’re aiming to detect. Options include statistical process control, time series analysis, or machine learning-based anomaly detection.

7. Model Training: If machine learning techniques are employed, training models on historical data may be necessary to understand the system’s normal behavior. This often involves using labeled data where anomalies are known.

8. Thresholds and Rules: Establish detection thresholds or rules based on your knowledge of the system’s behavior. These thresholds should enable you to distinguish normal behavior from anomalies effectively.

9. Testing and Validation: Evaluate your detection mechanisms using real or simulated data to ensure their effectiveness in identifying anomalies while minimizing false alarms.

10. Monitoring and Alerting: Implement continuous monitoring of the system and establish alerting mechanisms to notify relevant personnel when anomalies are detected. Fine-tune alerting thresholds to minimize false positives.

11. Response and Remediation: Develop a well-defined plan for responding to detected anomalies. Depending on the nature of the anomalies, responses may include automated actions, manual intervention, or further investigation.

12. Feedback Loop: Maintain a continuous feedback loop to update and refine your detection mechanisms based on insights gained from monitoring and lessons learned from past incidents.

Leveraging Data Science in Detection Engineering

Define Sets and Relationships

• define_sets(): Define sets for services, weaknesses, knowledge blocks, and MTD (Moving Target Defense) techniques. Initialize these sets with predefined elements.

• define_relationships(): Define relationships between services and common weaknesses, weaknesses and knowledge blocks, and knowledge blocks and MTD techniques. Create sets of tuples to represent these relationships.

Attacker Strategies

• define_attacker_strategies(): Define a dictionary of attacker strategies, each associated with a description. Provide a list of possible attacker strategies.

Defender Strategies

• define_defender_strategies(): Define a dictionary of defender strategies, each associated with a description. Provide a list of possible defender strategies.

Equilibrium Concepts

• define_equilibrium_concepts(): Define a dictionary of equilibrium concepts, each associated with a description. Provide a list of possible equilibrium concepts.

User Interaction Loop

• user_interaction_loop(): Implement the main user interaction loop, continuously prompting the user to explore different categories (Services, Weaknesses, Knowledge Blocks, MTD Techniques, Attacker Strategies, Defender Strategies, Equilibrium Concepts, or Quit).

• print_data_for_category(): This function takes a category (e.g., “S,” “W,” “AS”) as input and prints the corresponding data (sets, strategies, or concepts).

Main Function

• main(): This is the entry point of the program. It calls functions to define sets, relationships, and strategies. It then enters the user interaction loop.