English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

A software vulnerability could be exploited without any visible symptoms.When no source code is available, although such silent program executions couldcause very serious damage, the general problem of analyzing silent yet harmfulexecutions is still an open problem. In this work, we propose a graph neuralnetwork (GNN) assisted data flow analysis method for spotting silent bufferoverflows in execution traces. The new method combines a novel graph structure(denoted DFG+) beyond data-flow graphs, a tool to extract {\tt DFG+} fromexecution traces, and a modified Relational Graph Convolutional Network as theGNN model to be trained. The evaluation results show that a well-trained modelcan be used to analyze vulnerabilities in execution traces (ofpreviously-unseen programs) without support of any source code. Our modelachieves 94.39\% accuracy on the test data and successfully locates 29 out of30 real-world silent buffer overflow vulnerabilities. Leveraging deep learning,the proposed method is, to our best knowledge, the first general-purposeanalysis method for silent buffer overflows. It is also the first method tospot silent buffer overflows in global variables, stack variables, or heapvariables without crossing the boundary of allocated chunks.

Spotting Silent Buffer Overflows in Execution Trace through Graph Neural Network Assisted Data Flow Analysis