Why Cybersecurity Situation Awareness is Necessary?

The concept of situation awareness, originating in other fields and comprising perception, comprehension, and prediction, has evolved into "Cybersecurity Situation Awareness (CSA)" with the advancement of network technology. CSA focuses on collecting, understanding, and visualizing security factors that drive changes in network states within large-scale network environments, predicting emerging trends to support decision-making and response actions. This process involves five key steps: acquisition, comprehension, visualization, prediction, and response.

In cybersecurity, situation awareness products serve as visualized threat detection and analysis platforms. They can identify over 20 types of cloud-based security risks, including DDoS attacks, Web attacks, backdoor Trojans, vulnerability exploits, botnets, and abnormal behaviors. By leveraging big data analytics, these platforms categorize and aggregate attack events, threat alerts, and attack sources, providing users with a holistic view of security attack trends. The role of CSA is to help users detect and respond to security threats in real time, enhance network security defenses, and safeguard networks and data.

Advantages of CSA

Through the analysis and integration of security big data, CSA transforms "passive defense" into "active defense" by monitoring abnormal behaviors and potential threats in real time. It offers a comprehensive understanding of network security states, detects latent risks promptly, and implements countermeasures to prevent attacks. CSA also establishes a proactive security early-warning mechanism, enabling pre-emptive action against risks before incidents occur. This proactive approach enhances overall network security defenses by identifying, analyzing, and responding to threats in advance.

Technical Advantages of Cybersecurity Situation Awareness

1. Multi-Service Risk Perception

To meet multi-dimensional security detection needs, multi-service perception engines enhance capabilities for detecting, judging, identifying, predicting, and alerting risks across the entire network. These engines can be modularly combined based on business scenarios to collaboratively detect threats, identify anomalies, trace attacks, visualize trends, and coordinate responses. Machine learning is integrated to automate analysis and deep mining of massive security data.

2. Diverse Data Collection

To gather comprehensive intelligence, network, and endpoint information for risk analysis, CSA integrates external threat intelligence and employs active/passive techniques to collect heterogeneous logs from network devices, security appliances, vulnerability scanners, internet crawlers, hosts, and business applications. Logs are normalized and categorized to ensure rapid compatibility with systems from different vendors.

3. Intelligent Threat Analysis

Leveraging machine learning and expert systems, CSA analyzes large-scale sample data to establish baselines for user behavior, abnormal traffic, and threat attacks. Machine learning algorithms train models for detecting abnormal traffic, analyzing threat behaviors, and predicting traffic trends. Knowledge bases continuously optimize these models to identify threats and anticipate trends.

4. Full-Process Forensic Tracing

To reconstruct attack chains and facilitate evidence collection, CSA starts with threat events and uses security threat models to trace all attacker footprints. By integrating cloud-based real-time threat intelligence with local network/endpoint behavior and file data, CSA traces and visualizes complete attack chains, covering sources, methods, targets, and scope. It also rapidly identifies and characterizes unknown threats.

5. Cloud-Network-Endpoint Collaborative Defense

To achieve proactive defense against external threats, CSA builds a "cloud-network-endpoint" collaborative defense system. Using knowledge bases for policy management, it dynamically generates adaptive response strategies, pushes security policies to critical network devices, and leverages existing hardware gateways as enforcement units. This integrates cloud detection and perimeter defense for early warning, response, and handling of security incidents.

6. Automated Security Operations

To monitor asset performance and risks in real time, CSA supports automated deployment and dynamic management of microservices via cloud computing and container technology. It monitors key object statuses, analyzes risks for critical assets, and generates configuration policies and work orders for rapid response. Workflow management automates order triggering, distribution, tracking, reminders, and closure.

7. Multi-Dimensional Risk Visualization

To present risks from different stakeholder perspectives, CSA offers role-based visualizations for security operations. Using security big data and visualization techniques like 3D charts, radar plots, topology maps, and heatmaps, it provides tailored views for third-party supervisors, business operation specialists, information security specialists, and IT operation administrators, covering assets, traffic, business, and behavior dimensions.

Conclusion

Cybersecurity Situation Awareness (CSA) is a critical component of modern security strategies. By integrating real-time monitoring, intelligent analysis, and proactive response, CSA empowers organizations to stay ahead of evolving threats, enhance operational efficiency, and ensure the resilience of their digital infrastructure.