2 research outputs found
Context2Name: A Deep Learning-Based Approach to Infer Natural Variable Names from Usage Contexts
Most of the JavaScript code deployed in the wild has been minified, a process
in which identifier names are replaced with short, arbitrary and meaningless
names. Minified code occupies less space, but also makes the code extremely
difficult to manually inspect and understand. This paper presents Context2Name,
a deep learningbased technique that partially reverses the effect of
minification by predicting natural identifier names for minified names. The
core idea is to predict from the usage context of a variable a name that
captures the meaning of the variable. The approach combines a lightweight,
token-based static analysis with an auto-encoder neural network that summarizes
usage contexts and a recurrent neural network that predict natural names for a
given usage context. We evaluate Context2Name with a large corpus of real-world
JavaScript code and show that it successfully predicts 47.5% of all minified
identifiers while taking only 2.9 milliseconds on average to predict a name. A
comparison with the state-of-the-art tools JSNice and JSNaughty shows that our
approach performs comparably in terms of accuracy while improving in terms of
efficiency. Moreover, Context2Name complements the state-of-the-art by
predicting 5.3% additional identifiers that are missed by both existing tools
Evaluation Methodologies in Software Protection Research
Man-at-the-end (MATE) attackers have full control over the system on which
the attacked software runs, and try to break the confidentiality or integrity
of assets embedded in the software. Both companies and malware authors want to
prevent such attacks. This has driven an arms race between attackers and
defenders, resulting in a plethora of different protection and analysis
methods. However, it remains difficult to measure the strength of protections
because MATE attackers can reach their goals in many different ways and a
universally accepted evaluation methodology does not exist. This survey
systematically reviews the evaluation methodologies of papers on obfuscation, a
major class of protections against MATE attacks. For 572 papers, we collected
113 aspects of their evaluation methodologies, ranging from sample set types
and sizes, over sample treatment, to performed measurements. We provide
detailed insights into how the academic state of the art evaluates both the
protections and analyses thereon. In summary, there is a clear need for better
evaluation methodologies. We identify nine challenges for software protection
evaluations, which represent threats to the validity, reproducibility, and
interpretation of research results in the context of MATE attacks