Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data.

Ducson Nguyen

dblp:167/0198 · DBLP profile ↗
← Back
1ranked-venue papers
0as first author
0since 2021 · last 2015
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Software engineering, systems software and programming languages · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Network and information security
1 paper
Systems and software security · 100%
Software engineering, system software, and programming languages
1 paper
Program analysis · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Systems and software security › memory safety › memory error detection
buffer overflow detection
0.212015
Data-Delineation in Software Binaries and its Application to Buffer-Overrun Discovery · ICSE (1) 2015
Systems and software security
memory safety
0.212015
Data-Delineation in Software Binaries and its Application to Buffer-Overrun Discovery · ICSE (1) 2015
Program analysis
static analysis
0.212015
Data-Delineation in Software Binaries and its Application to Buffer-Overrun Discovery · ICSE (1) 2015

Methods — techniques the papers use, named apart from their topics

heuristic static analysis · 0.4DWARF debugging information · 0.4
YearPublicationVenuePosition
2015 Data-Delineation in Software Binaries and its Application to Buffer-Overrun Discovery
abstract
Detecting memory-safety violations in binaries is complicated by the lack of knowledge of the intended data layout, i.e., the locations and sizes of objects. We present lightweight, static, heuristic analyses for recovering the intended layout of data in a stripped binary. Comparison against DWARF debugging information shows high precision and recall rates for inferring source-level object boundaries. On a collection of benchmarks, our analysis eliminates a third to a half of incorrect object boundaries identified by an IDA Pro-inspired heuristic, while retaining nearly all valid object boundaries. In addition to measuring their accuracy directly, we evaluate the effect of using the recovered data for improving the precision of static buffer-overrun detection in the defect-detection tool CodeSonar/x86. We demonstrate that CodeSonar's false-positive rate drops by about 80% across our internal evaluation suite for the tool, while our approximation of CodeSonar's recall only degrades about 25%.
Denis Gopan, Evan Driscoll, Ducson Nguyen, Dimitri Naydich, Alexey Loginov, David Melski
ICSE (1)3