A Study on Advanced Persistent Threats

被引:0
|
作者
Chen, Ping [1 ]
Desmet, Lieven [1 ]
Huygens, Christophe [1 ]
机构
[1] Katholieke Univ Leuven, iMinds DistriNet, B-3001 Leuven, Belgium
关键词
advanced threat; APT; sophisticated attacks; cyber security;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
A recent class of threats, known as Advanced Persistent Threats (APTs), has drawn increasing attention from researchers, primarily from the industrial security sector. APTs are cyber attacks executed by sophisticated and well-resourced adversaries targeting specific information in high-profile companies and governments, usually in a long term campaign involving different steps. To a significant extent, the academic community has neglected the specificity of these threats and as such an objective approach to the APT issue is lacking. In this paper, we present the results of a comprehensive study on APT, characterizing its distinguishing characteristics and attack model, and analyzing techniques commonly seen in APT attacks. We also enumerate some non-conventional countermeasures that can help to mitigate APTs, hereby highlighting the directions for future research.
引用
收藏
页码:63 / 72
页数:10
相关论文
共 50 条
  • [31] An adaptive defense mechanism to prevent advanced persistent threats
    Xie, Yi-xi
    Ji, Li-xin
    Li, Ling-shu
    Guo, Zehua
    Baker, Thar
    CONNECTION SCIENCE, 2021, 33 (02) : 359 - 379
  • [32] Beyond Blacklisting: Cyberdefense in the Era of Advanced Persistent Threats
    Beuhring, Aaron
    Salous, Kyle
    IEEE SECURITY & PRIVACY, 2014, 12 (05) : 90 - 93
  • [33] APTHunter: Detecting Advanced Persistent Threats in Early Stages
    Mahmoud, Moustafa
    Mannan, Mohammad
    Youssef, Amr
    DIGITAL THREATS: RESEARCH AND PRACTICE, 2023, 4 (01):
  • [34] The Influences of Feature Sets on the Detection of Advanced Persistent Threats
    Hofer-Schmitz, Katharina
    Kleb, Ulrike
    Stojanovic, Branka
    ELECTRONICS, 2021, 10 (06) : 1 - 22
  • [35] A Study of Classifying Advanced Persistent Threats With Multi-Layered Deep Learning Approaches
    Hu, Yen-Hung
    Hsieh, Chung-Chu
    19TH IEEE INTERNATIONAL SYMPOSIUM ON PARALLEL AND DISTRIBUTED PROCESSING WITH APPLICATIONS (ISPA/BDCLOUD/SOCIALCOM/SUSTAINCOM 2021), 2021, : 1645 - 1650
  • [36] Prospect Theoretic Study of Honeypot Defense Against Advanced Persistent Threats in Power Grid
    Tian, Wen
    Ji, Xiaopeng
    Liu, Weiwei
    Liu, Guangjie
    Zhai, Jiangtao
    Dai, Yuewei
    Huang, Shuhua
    IEEE ACCESS, 2020, 8 (08): : 64075 - 64085
  • [37] Surviving advanced persistent threats in a distributed environment - Architecture and analysis
    Mehresh, Ruchika
    Upadhyaya, Shambhu
    INFORMATION SYSTEMS FRONTIERS, 2015, 17 (05) : 987 - 995
  • [38] Learning Games for Defending Advanced Persistent Threats in Cyber Systems
    Zhu, Tianqing
    Ye, Dayong
    Cheng, Zishuo
    Zhou, Wanlei
    Yu, Philip S.
    IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2023, 53 (04): : 2410 - 2422
  • [39] A Context-Based Detection Framework for Advanced Persistent Threats
    Giura, Paul
    Wang, Wei
    2012 ASE INTERNATIONAL CONFERENCE ON CYBER SECURITY (CYBERSECURITY), 2012, : 69 - 74
  • [40] Dealing with Advanced Persistent Threats in Smart Grid ICT Networks
    Skopik, Florian
    Friedberg, Ivo
    Fiedler, Roman
    2014 IEEE PES INNOVATIVE SMART GRID TECHNOLOGIES CONFERENCE (ISGT), 2014,