Smart home devices are vulnerable to passive inference attacks based on network traffic, even in the presence of encryption. In this paper, we present Ping-Pong, a tool that can automatically extract packetlevel signatures (i.e., simple sequences of packet lengths and directions) from the network traffic of smart home devices, and use those signatures to detect occurrences of specific device events (e.g., light bulb turning ON/OFF). We evaluated PingPong on popular smart home devices ranging from smart plugs to thermostats and home security systems. We have successfully: (1) extracted packet-level signatures from 18 devices (11 of which are the most popular smart home devices on Amazon) from 15 popular vendors, (2) used those signatures to detect occurrences of specific device events with an average recall of more than 97%, and (3) shown that the signatures are unique among tens of millions of packets of real world network traffic.
Corrupt data structures are an important cause of unacceptable program execution. Data structure repair (which eliminates inconsistencies by updating corrupt data structures to conform to consistency constraints) promises to enable many programs to continue to execute acceptably in the face of otherwise fatal data structure corruption errors. A key issue is obtaining an accurate and comprehensive data structure consistency specification.We present a new technique for obtaining data structure consistency specifications for data structure repair. Instead of requiring the developer to manually generate such specifications, our approach automatically generates candidate data structure consistency properties using the Daikon invariant detection tool. The developer then reviews these properties, potentially rejecting or generalizing overly specific properties to obtain a specification suitable for automatic enforcement via data structure repair.We have implemented this approach and applied it to three sizable benchmark programs: CTAS (an air-traffic control system), BIND (a widely-used Internet name server) and Freeciv (an interactive game). Our results indicate that (1) automatic constraint generation produces constraints that enable programs to execute successfully through data structure consistency errors, (2) compared to manual specification, automatic generation can produce more comprehensive sets of constraints that cover a larger range of data structure consistency properties, and (3) reviewing the properties is relatively straightforward and requires substantially less programmer effort than manual generation, primarily because it reduces the need to examine the program text to understand its operation and extract the relevant consistency constraints. Moreover, when evaluated by a hostile third party "Red Team" contracted to evaluate the effectiveness of the technique, our data structure inference and enforcement tools successfully prevented several otherwise fatal attacks.
We present a system that accepts a specification of key data structure constraints, then dynamically detects and repairs violations of these constraints. Our experience using our system indicates that the specifications are relatively easy to develop once one understands the data structures. Furthermore, for our set of benchmark applications, our system can effectively repair errors to deliver consistent data structures that allow the program to continue to operate successfully within its designed operating envelope.
We present a system that accepts a specification of key data structure constraints, then dynamically detects and repairs violations of these constraints. Our experience using our system indicates that the specifications are relatively easy to develop once one understands the data structures. Furthermore, for our set of benchmark applications, our system can effectively repair errors to deliver consistent data structures that allow the program to continue to operate successfully within its designed operating envelope.
Writing low-level concurrent software has traditionally required intimate knowledge of the entire toolchain and often has involved coding in assembly. New language standards have extended C and C++ with support for low-level atomic operations and a weak memory model, enabling developers to write portable and efficient multithreaded code. In this article, we present CDSC hecker , a tool for exhaustively exploring the behaviors of concurrent code under the C/C++ memory model. We have used CDSC hecker to exhaustively unit test concurrent data structure implementations and have discovered errors in a published implementation of a work-stealing queue and a single producer, single consumer queue.
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