How to keep text private? A systematic review of deep learning methods for privacy-preserving natural language processing
Deep learning (DL) models for natural language processing (NLP) tasks often handle
private data, demanding protection against breaches and disclosures. Data protection laws …
private data, demanding protection against breaches and disclosures. Data protection laws …
Dos and don'ts of machine learning in computer security
With the growing processing power of computing systems and the increasing availability of
massive datasets, machine learning algorithms have led to major breakthroughs in many …
massive datasets, machine learning algorithms have led to major breakthroughs in many …
A survey on machine learning techniques for source code analysis
The advancements in machine learning techniques have encouraged researchers to apply
these techniques to a myriad of software engineering tasks that use source code analysis …
these techniques to a myriad of software engineering tasks that use source code analysis …
AUToSen: Deep-Learning-Based Implicit Continuous Authentication Using Smartphone Sensors
Smartphones have become crucial for our daily life activities and are increasingly loaded
with our personal information to perform several sensitive tasks, including, mobile banking …
with our personal information to perform several sensitive tasks, including, mobile banking …
Analyzing and detecting emerging Internet of Things malware: A graph-based approach
The steady growth in the number of deployed Internet of Things (IoT) devices has been
paralleled with an equal growth in the number of malicious software (malware) targeting …
paralleled with an equal growth in the number of malicious software (malware) targeting …
Authorship Attribution Methods, Challenges, and Future Research Directions: A Comprehensive Survey
Over the past few decades, researchers have put their effort and paid significant attention to
the authorship attribution field, as it plays an important role in software forensics analysis …
the authorship attribution field, as it plays an important role in software forensics analysis …
Misleading authorship attribution of source code using adversarial learning
In this paper, we present a novel attack against authorship attribution of source code. We
exploit that recent attribution methods rest on machine learning and thus can be deceived by …
exploit that recent attribution methods rest on machine learning and thus can be deceived by …
[PDF][PDF] Unifying the perspectives of nlp and software engineering: A survey on language models for code
In this work we systematically review the recent advancements in code processing with
language models, covering 50+ models, 30+ evaluation tasks, 170+ datasets, and 700 …
language models, covering 50+ models, 30+ evaluation tasks, 170+ datasets, and 700 …
Assessing the generalizability of code2vec token embeddings
Many Natural Language Processing (NLP) tasks, such as sentiment analysis or syntactic
parsing, have benefited from the development of word embedding models. In particular …
parsing, have benefited from the development of word embedding models. In particular …
" Get in Researchers; We're Measuring Reproducibility": A Reproducibility Study of Machine Learning Papers in Tier 1 Security Conferences
Reproducibility is crucial to the advancement of science; it strengthens confidence in
seemingly contradictory results and expands the boundaries of known discoveries …
seemingly contradictory results and expands the boundaries of known discoveries …