Collaborative anomaly-based detection of large-scale internet attacks

被引:14
|
作者
Gamer, Thomas [1 ]
机构
[1] KIT, Inst Telemat, D-76131 Karlsruhe, Germany
关键词
Attack detection; Collaboration; Large-scale attacks; INTRUSION;
D O I
10.1016/j.comnet.2011.08.015
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
The Internet infrastructure and Internet-based business today still suffer from various attacks like Distributed Denial-of-Service (DDoS) attacks or worm propagations. A necessary first step in order to cope with such large-scale attacks is to provide an Internet-wide detection of such ongoing attacks, i.e., a detection that is not limited to single detection systems only. Therefore, collaborative detection systems were developed in the past. They, however, often rely on close trust relationships, which only rarely are available in the Internet. This means that the scope of detection is limited to only a small part of the Internet, mostly to a single administrative domain. This paper, therefore, introduces our newly developed collaborative attack detection that facilitates collaboration beyond domain boundaries without requiring close trust relationships. In-network detection systems are explicitly considered, too. Such systems are located on routers in the core of the Internet and are characterized by limited resources available for detection. Finally, a detailed simulative levaluation of our proposed solution is presented. (C) 2011 Elsevier B.V. All rights reserved.
引用
收藏
页码:169 / 185
页数:17
相关论文
共 50 条
  • [31] Anomaly-based protection approach against wireless network attacks
    Fayssal, Samer
    Hariri, Salim
    2007 IEEE INTERNATIONAL CONFERENCE ON PERVASIVE SERVICES, 2007, : 193 - 195
  • [32] An Adaptive Deep-Ensemble Anomaly-Based Intrusion Detection System for the Internet of Things
    Albulayhi, Khalid
    Sheldon, Frederick T.
    2021 IEEE WORLD AI IOT CONGRESS (AIIOT), 2021, : 187 - 196
  • [33] Anomaly-Based Intrusion Detection by Machine Learning: A Case Study on Probing Attacks to an Institutional Network
    Tufan, Emrah
    Tezcan, Cihangir
    Acarturk, Cengiz
    IEEE ACCESS, 2021, 9 : 50078 - 50092
  • [34] Collaborative Anomaly Detection for Internet of Things based on Federated Learning
    Kim, Seongwoo
    Cai, He
    Hua, Cunqing
    Gu, Pengwenlong
    Xu, Wenchao
    Park, Jeonghyeok
    2020 IEEE/CIC INTERNATIONAL CONFERENCE ON COMMUNICATIONS IN CHINA (ICCC), 2020, : 623 - 628
  • [35] Anomaly-Based Method for Detecting Multiple Classes of Network Attacks
    Gurina, Anastasia
    Eliseev, Vladimir
    INFORMATION, 2019, 10 (03)
  • [36] Collaborative Fault Detection for Large-Scale Photovoltaic Systems
    Zhao, Yingying
    Li, Dongsheng
    Lu, Tun
    Lv, Qin
    Gu, Ning
    Shang, Li
    IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, 2020, 11 (04) : 2745 - 2754
  • [37] Anomaly-based network intrusion detection using denoising autoencoder and Wasserstein GAN synthetic attacks
    Arafah, Mohammad
    Phillips, Iain
    Adnane, Asma
    Hadi, Wael
    Alauthman, Mohammad
    Al-Banna, Abedal-Kareem
    APPLIED SOFT COMPUTING, 2025, 168
  • [38] On the symbiosis of specification-based and anomaly-based detection
    Stakhanova, Natalia
    Basu, Samik
    Wong, Johnny
    COMPUTERS & SECURITY, 2010, 29 (02) : 253 - 268
  • [39] ANOMALY-BASED NETWORK INTRUSION DETECTION METHODS
    Nevlud, Pavel
    Bures, Miroslav
    Kapicak, Lukas
    Zdralek, Jaroslav
    ADVANCES IN ELECTRICAL AND ELECTRONIC ENGINEERING, 2013, 11 (06) : 468 - 474
  • [40] Anomaly-Based Network Intrusion Detection System
    Villalba, L. J. G.
    Orozco, A. L. S.
    Vidal, J. M.
    IEEE LATIN AMERICA TRANSACTIONS, 2015, 13 (03) : 850 - 855