Detecting Semantic Bugs in Autopilot Software by Classifying Anomalous Variables

Detecting and classifying anomalous behavior in spatiotemporal network data

We investigate different models for detecting and classifying important geopolitical events in high-frequency spatiotemporal network data. Building on previous empirical work on the network response to real-world events, our goal is to develop a generative model that can identify the time, location, and nature of different emergency and non-emergency events. As a testbed for these models, we us...

Classifying Bugs with Interpolants

We present an approach to the classification of error messages in the context of static checking in the style of ESC/Java. The idea is to compute a semantics-based signature for each error message and then group together error messages with the same signature. The approach aims at exploiting modern verification techniques based on, e.g., Craig interpolation in order to generate small but signif...

Detecting Malicious Software by Monitoring Anomalous Windows Registry Accesses

We present a host-based intrusion detection system for Microsoft Windows. The core of the system is an algorithm that detects attacks on a host machine by looking for anomalous accesses to the Windows Registry. The key idea is to first train a model of normal registry behavior for a host and to use this model to detect abnormal registry accesses at run-time. The system trains a normal model usi...

Detecting and Mitigating Concurrency Bugs

Variability bugs in system software

One of the key aspects of software product line engineering (SPLE) is the handling of variation points that can be combined to form specific products. Variability management is the discipline responsible for the specification, combination and use of variation points. Many researches have contributed to this area by providing formal methods to analyze variability by means of reasoning operations...