Towards Translating Graph Transformation Approaches by Model Transformations

Recently, many researchers are working on semantics preserving model transformation. In the field of graph transformation one can think of translating graph grammars written in one approach to a behaviourally equivalent graph grammar in another approach. In this paper we translate graph grammars developed with the GROOVE tool to AGG graph grammars by first investigating the set of core graph transformation concepts supported by both tools. Then, we define what it means for two graph grammars to be behaviourally equivalent, and for the regarded approaches we actually show how to handle different definitions of both - application conditions and graph structures. The translation itself is explained by means of intuitive examples

The Design & Implementation of an Abstract Semantic Graph for Statement-Level Dynamic Analysis of C++ Applications

In this thesis, we describe our system, Hylian, for statement-level analysis, both static and dynamic, of a C++ application. We begin by extending the GNU gcc parser to generate parse trees in XML format for each of the compilation units in a C++ application. We then provide verification that the generated parse trees are structurally equivalent to the code in the original C++ application. We use the generated parse trees, together with an augmented version of the gcc test suite, to recover a grammar for the C++ dialect that we parse. We use the recovered grammar to generate a schema for further verification of the parse trees and evaluate the coverage provided by our C++ test suite. We then extend the parse tree, for each compilation unit, with semantic information to form an abstract semantic graph, ASG, and then link the ASGs for all of the compilation units into a unified ASG for the entire application under study. In addition, to relieve the cognitive burden of information that may inundate a developer, we describe our development of extensions to Hylian to build abbreviated abstract semantic graphs, which incorporate information about user code, but not about compiler provided library code. Finally, we describe the various approaches that we adopted to provide assurance for the developer that the ASGs that Hylian builds, correctly represent the program under study

Linking analysis and transformation tools with source-based mappings

This paper discusses an approach to linking separate analysis and transformation tools, such that analysis results can be used to guide transformations. Our approach consists of two phases. First, the analysis tool maps its results to relevant locations in the source code. Second, a mapping in the reverse direction is performed: the analysis results expressed as source positions and data are mapped to the abstractions used in the transformation tool. We discuss a prototype implementation of this approach in detail, and present the results of a number of case studies

Physical-Fingerprinting of Electronic Control Unit (ECU) Based on Machine Learning Algorithm for In-Vehicle Network Communication Protocol “CAN-BUS”

The Controller Area Network (CAN) bus serves as a legacy protocol for in-vehicle data communication. Simplicity, robustness, and suitability for real-time systems are the salient features of the CAN bus protocol. However, it lacks the basic security features such as massage authentication, which makes it vulnerable to the spoofing attacks. In a CAN network, linking CAN packet to the sender node is a challenging task. This paper aims to address this issue by developing a framework to link each CAN packet to its source. Physical signal attributes of the received packet consisting of channel and node (or device) which contains specific unique artifacts are considered to achieve this goal. Material and design imperfections in the physical channel and digital device, which are the main contributing factors behind the device-channel specific unique artifacts, are leveraged to link the received electrical signal to the transmitter. Generally, the inimitable patterns of signals from each ECUs exist over the course of time that can manifest the stability of the proposed method. Uniqueness of the channel-device specific attributes are also investigated for time-and frequency-domain. Feature vector is made up of both time and frequency domain physical attributes and then employed to train a neural network-based classifier. Performance of the proposed fingerprinting method is evaluated by using a dataset collected from 16 different channels and four identical ECUs transmitting same message. Experimental results indicate that the proposed method achieves correct detection rates of 95.2% and 98.3% for channel and ECU classification, respectively.Master of Science in EngineeringComputer Engineering, College of Engineering and Computer ScienceUniversity of Michigan-Dearbornhttps://deepblue.lib.umich.edu/bitstream/2027.42/140731/1/Thesis manuscript_v3.pdfDescription of Thesis manuscript_v3.pdf : Thesi