Section: Research Program

Intrusion Detection / Security Events Monitoring and Management

Today, we have not yet fully entered into a world of “security by design”. Security remains often a property that is considered a posteriori, when the system is deployed, which often results in applying patches when vulnerabilities are discovered (also called a “patch and pray” approach). Unfortunately, despite patching, the number of vulnerabilities remains high, as evidenced by the number of vulnerabilities published each year in the Common Vulnerabilities and Exposures (CVE) system. Thus, it is important to be able to early detect cyber-attacks, especially when they exploit vulnerabilities that are unknown. However, the efficiency of security events monitoring and management systems (including the IDS - Intrusion Detection Systems) is still an open issue today. Indeed, they are often unable to effectively deal with huge numbers of security events, and they usually produce too many false alarms yet missing some attacks. So one of the main research challenges in IT security remains the definition of efficient security events monitoring systems, i.e., that enable both to process a huge number of security events and to detect any attacks without flooding the security analysts with false alarms.

By exploiting vulnerabilities in operating systems, applications, or network services, an attacker can defeat preventive security mechanisms and violate the security policy of the whole system. The goal of an Intrusion Detection Systems (IDS) is to detect such violations by analyzing some security events generated on a monitored system. Ideally, the IDS should produce an alert for any violation (no false negative), and only for violations (no false positive).

To produce alerts, two detection techniques exist: the misuse based detection and the anomaly based detection. A misuse based detection is actually a signature based detection approach : it allows to detect only the attacks whose signature is available. From our point of view, while useful in practice, misuse detection is intrinsically limited. Indeed, it requires to update in real-time the database of signatures, similarly to what has to be done for antivirus tools. The CIDRE project-team follows the alternative approach, namely the anomaly approach, which consists in detecting a deviation from a referenced behavior. Our contributions on anomaly-based IDS follow three axis:

• Illegal Information Flow Detection: our goal is to detect information flows in the monitored system (either a node or a set of trusted nodes) that are allowed by the access control mechanism, but are illegal from the security policy point of view. This approach is particularly appealing to detect intrusions in a standalone node, such as a smartphone.

• Anomaly-Based Detection in Distributed Applications: our goal is to specify the normal behavior based on either a formal specification of the distributed application, or previous executions. This approach is particularly appealing to detect intrusions in industrial control systems since these systems exhibit well-defined behaviors at different levels: network level (network communication patterns, protocol specifications, etc.), control level (continue and discrete process control laws), or even the state of the local resources (memory or CPU).

• Online data analytics: our goal is to estimate on the fly different statistics or metrics on distributed input streams to detect abnormal behavior with respect to a well-defined criterion such as the distance between different streams, their correlation or their entropy.

Beside the anomaly-based IDS, we have also led research work on alert correlation and visualisation of security events. Indeed, in large systems, multiple (host and network) IDS and many sensors are deployed and they continuously and independently generate notifications (event's observations, warnings and alerts). To cope with this huge amount of collected data, we have studied two different approaches, each with specific goal:

• Alert Correlation System: the alerts of low level IDSes can be viewed as security events of a high level IDS whose goal is to correlate these alerts. An alert correlation system aims at exploiting the known relationships between some elements that appear in the flow of low level notifications to generate high semantic meta-alerts. The main goal is to reduce the number of alerts (and especially, false positive) returned to the security analysts and to allow a higher level analysis of the situation (situational awareness).

• Visualization Tools: a visualization tools aims at relying on the capacity of human beings to detect patterns and outliers in datasets when these datasets are properly visually represented. Human beings also know pieces of contextual information that are very difficult to formalize so as to make them usable by a computer. Visualization is therefore a very useful complementary tool to detect abnormal events in real time (monitoring), to search for malicious events in log files (data exploration and forensics) and to communicate results (reporting).