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abstract

PLAS 2018 - ACM SIGSAC Workshop on Programming Languages and Analysis for Security

Published: 15 October 2018 Publication History

Abstract

The 13th ACM SIGSAC Workshop on Programming Languages and Analysis for Security (PLAS 2018) is co-located with the 25th ACM Conference on Computer and Communications Security (ACM CCS 2018). Over its now more than ten-year history, PLAS has provided a unique forum for researchers and practitioners to exchange ideas about programming language and program analysis techniques with the goal of improving the security of software systems. PLAS aims to provide a forum for exploring and evaluating ideas on using programming language and program analysis techniques to improve the security of software systems. Strongly encouraged are proposals of new, speculative ideas, evaluations of new or known techniques in practical settings, and discussions of emerging threats and important problems.

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  • (2023)A Review of Deep Learning-Based Binary Code Similarity AnalysisElectronics10.3390/electronics1222467112:22(4671)Online publication date: 16-Nov-2023

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  1. PLAS 2018 - ACM SIGSAC Workshop on Programming Languages and Analysis for Security

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    cover image ACM Conferences
    CCS '18: Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security
    October 2018
    2359 pages
    ISBN:9781450356930
    DOI:10.1145/3243734
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 15 October 2018

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    Author Tags

    1. programming languages
    2. security

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    • Abstract

    Funding Sources

    • Microsoft Research

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    CCS '18
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    Acceptance Rates

    CCS '18 Paper Acceptance Rate 134 of 809 submissions, 17%;
    Overall Acceptance Rate 1,261 of 6,999 submissions, 18%

    Upcoming Conference

    CCS '25

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    Cited By

    View all
    • (2023)A Review of Deep Learning-Based Binary Code Similarity AnalysisElectronics10.3390/electronics1222467112:22(4671)Online publication date: 16-Nov-2023

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