Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
10.1145/3548606.3563683acmconferencesArticle/Chapter ViewAbstractPublication PagesccsConference Proceedingsconference-collections
abstract

AISec '22: 15th ACM Workshop on Artificial Intelligence and Security

Published: 07 November 2022 Publication History
  • Get Citation Alerts
  • Abstract

    Recent years have seen a dramatic increase in applications of Artificial Intelligence (AI), Machine Learning (ML), and data mining to security and privacy problems. The analytic tools and intelligent behavior provided by these techniques make AI and ML increasingly important for autonomous real-time analysis and decision making in domains with a wealth of data or that require quick reactions to constantly changing situations. The use of learning methods in security-sensitive domains, in which adversaries may attempt to mislead or evade intelligent machines, creates new frontiers for security research. The recent widespread adoption of "deep learning" techniques, whose security properties are difficult to reason about directly, has only added to the importance of this research. In addition, data mining and machine learning techniques create a wealth of privacy issues, due to the abundance and accessibility of data. The AISec workshop provides a venue for presenting and discussing new developments in the intersection of security and privacy with AI and machine learning.

    Index Terms

    1. AISec '22: 15th ACM Workshop on Artificial Intelligence and Security

          Recommendations

          Comments

          Information & Contributors

          Information

          Published In

          cover image ACM Conferences
          CCS '22: Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security
          November 2022
          3598 pages
          ISBN:9781450394505
          DOI:10.1145/3548606
          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.

          Sponsors

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          Published: 07 November 2022

          Check for updates

          Author Tags

          1. adversarial machine learning
          2. artificial intelligence
          3. privacy
          4. security

          Qualifiers

          • Abstract

          Conference

          CCS '22
          Sponsor:

          Acceptance Rates

          Overall Acceptance Rate 1,261 of 6,999 submissions, 18%

          Upcoming Conference

          CCS '24
          ACM SIGSAC Conference on Computer and Communications Security
          October 14 - 18, 2024
          Salt Lake City , UT , USA

          Contributors

          Other Metrics

          Bibliometrics & Citations

          Bibliometrics

          Article Metrics

          • 0
            Total Citations
          • 155
            Total Downloads
          • Downloads (Last 12 months)39
          • Downloads (Last 6 weeks)1
          Reflects downloads up to

          Other Metrics

          Citations

          View Options

          Get Access

          Login options

          View options

          PDF

          View or Download as a PDF file.

          PDF

          eReader

          View online with eReader.

          eReader

          Media

          Figures

          Other

          Tables

          Share

          Share

          Share this Publication link

          Share on social media