Efficient Context-Sensitive Intrusion Detection

Model-based intrusion detection compares a process’s execution against a program model to detect intrusion attempts. Models constructed from static program analysis have historically traded precision for efficiency. We address this problem with our Dyck model, the first efficient statically-constructed context-sensitive model. This model specifies both the correct sequences of system calls that a program can generate and the stack changes occurring at function call sites. Experiments demonstrate that the Dyck model is an order of magnitude more precise than a context-insensitive finite state machine model. With null call squelching, a dynamic technique to bound cost, the Dyck model operates in time similar to the contextinsensitive model. We also present two static analysis techniques designed to counter mimicry and evasion attacks. Our branch analysis identifies between 32% and 64% of our test programs’ system call sites as affecting control flow via their return values. Interprocedural argument capture of general values recovers 32% to 69% more arguments than previously reported techniques.