Blogs (1) >>
ICSE 2019
Sat 25 - Fri 31 May 2019 Montreal, QC, Canada
Wed 29 May 2019 15:10 - 15:20 at Duluth - Security 2 Chair(s): Arie van Deursen

Code flaws or vulnerabilities are prevalent in software systems and can potentially cause a variety of problems including deadlock, hacking, information loss and system failure. A variety of approaches have been developed to try and detect the most likely locations of such code vulnerabilities in large code bases. Most of them rely on manually designing code features (e.g. complexity metrics or frequencies of code tokens) that represent the characteristics of the potentially problematic code to locate. However, all suffer from challenges in sufficiently capturing both semantic and syntactic representation of source code, an important capability for building accurate prediction models. In this paper, we describe a new approach, built upon the powerful deep learning Long Short Term Memory model, to automatically learn both semantic and syntactic features of code. Our evaluation on 18 Android applications and the Firefox application demonstrates that the prediction power obtained from our learned features is better than what is achieved by state of the art vulnerability prediction models, for both within-project prediction and cross-project prediction.

Wed 29 May
Times are displayed in time zone: Eastern Time (US & Canada) change

14:00 - 15:30: Security 2Papers / Demonstrations / Technical Track / Journal-First Papers / New Ideas and Emerging Results at Duluth
Chair(s): Arie van DeursenDelft University of Technology
14:00 - 14:20
The Seven Sins: Security Smells in Infrastructure as Code ScriptsArtifacts AvailableACM SIGSOFT Distinguished Paper AwardTechnical TrackIndustry Program
Technical Track
Akond RahmanNorth Carolina State University, Chris ParninNCSU, Laurie WilliamsNorth Carolina State University
14:20 - 14:40
DifFuzz: Differential Fuzzing for Side-Channel AnalysisArtifacts AvailableArtifacts Evaluated ReusableTechnical Track
Technical Track
Shirin NilizadehUniversity of Texas at Arlington, Yannic NollerHumboldt-Universität zu Berlin, Corina S. PasareanuCarnegie Mellon University Silicon Valley, NASA Ames Research Center
14:40 - 14:50
Detecting Suspicious Package UpdatesIndustry ProgramNIER
New Ideas and Emerging Results
Kalil GarrettGeorgia State University, Gabriel FerreiraCarnegie Mellon University, Limin JiaCarnegie Mellon University, Joshua SunshineCarnegie Mellon University, Christian KaestnerCarnegie Mellon University
14:50 - 15:10
EASYFLOW: Keep Ethereum Away From OverflowDemos
Jianbo GaoPeking University, Han LiuTsinghua University, Chao Liu, Qingshan LiPeking University, Zhi GuanPeking University, Zhong Chen
Pre-print Media Attached
15:10 - 15:20
Automatic feature learning for predicting vulnerable software componentsIndustry ProgramJournal-First
Journal-First Papers
Hoa Khanh DamUniversity of Wollongong, Truyen Tran, Trang PhamDeakin University, Shien Wee NgUniversity of Wollongong, John GrundyMonash University, Aditya Ghose
Link to publication DOI Pre-print
15:20 - 15:30
Discussion Period