Mitigating Browser-based DDoS Attacks using CORP

被引:2
作者
Agrawall, Akash [1 ]
Chaitanya, Krishna [2 ]
Agrawal, Arnav Kumar [3 ]
Choppella, Venkatesh [1 ]
机构
[1] IIIT Hyderabad, Hyderabad, India
[2] Microsoft India, Hyderabad, India
[3] Carnegie Mellon Univ, Pittsburgh, PA 15213 USA
来源
PROCEEDINGS OF THE 10TH INNOVATIONS IN SOFTWARE ENGINEERING CONFERENCE | 2017年
关键词
DDoS; Browser-based DDoS; Browser; !text type='Java']Java[!/text]script; Cross-origin requests; MITM (Man in the middle);
D O I
10.1145/3021460.3021477
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
On March 27, 2015, Github witnessed a massive DDoS attack, the largest in Github's history till date. In this incident, browsers and users were used as vectors to launch the attack. In this paper, we analyse such browser-based DDoS attacks and simulate them in a lab environment. Existing browser security policies like Same Origin Policy (SOP), Content Security Policy (CSP) do not mitigate these attacks by design. In this paper we observe that CORP (Cross Origin Request Policy), a browser security policy, can be used to mitigate these attacks. CORP enables a server to control cross-origin interactions initiated by a browser. The browser intercepts the cross-origin requests and blocks unwanted requests by the server. This takes the load off the server to mitigate the attack.
引用
收藏
页码:137 / 146
页数:10
相关论文
共 50 条
  • [31] Research on DDoS Attacks Detection Based on RDF-SVM
    Wang, Chenguang
    Zheng, Jing
    Li, Xiaoyong
    2017 10TH INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTATION TECHNOLOGY AND AUTOMATION (ICICTA 2017), 2017, : 161 - 165
  • [32] Defending SDN-based IoT Networks Against DDoS Attacks Using Markov Decision Process
    Zheng, Jianjun
    Namin, Akbar Siami
    2018 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), 2018, : 4589 - 4592
  • [33] DDoS Attacks Detection in IoV using ML-based Models with an Enhanced Feature Selection Technique
    Albishi, Ohoud Ali
    Abdullah, Monir
    INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2024, 15 (02) : 814 - 823
  • [34] Distinguishing DDoS Attacks from Flash Crowds Using Probability Metrics
    Li, Ke
    Zhou, Wanlei
    Li, Ping
    Hai, Jing
    Liu, Jianwen
    NSS: 2009 3RD INTERNATIONAL CONFERENCE ON NETWORK AND SYSTEM SECURITY, 2009, : 9 - 17
  • [35] DDoS Attacks Detection by Using Machine Learning Methods on Online Systems
    Baskaya, Dilek
    Samet, Refi
    2020 5TH INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND ENGINEERING (UBMK), 2020, : 52 - 57
  • [36] Distributed Intrusion Detection using Mobile Agents against DDoS Attacks
    Akyazi, Ugur
    Uyar, A. Sima Etaner
    23RD INTERNATIONAL SYMPOSIUM ON COMPUTER AND INFORMATION SCIENCES, 2008, : 346 - +
  • [37] Early Detection of Campus Network DDoS Attacks using Predictive Models
    Araki, Ryusei
    Hsu, Ying-Feng
    Matsuoka, Morito
    2022 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM 2022), 2022, : 3362 - 3367
  • [38] A Hybrid Defense Mechanism for DDoS attacks using Cluster Analysis in MANET
    Devi, P.
    Kannammal, A.
    PROCEEDINGS OF THE 2012 INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTING, COMMUNICATIONS AND INFORMATICS (ICACCI'12), 2012, : 287 - 291
  • [39] Detection of application layer DDoS attacks using big data technologies
    Singhal, Sunita
    Medeira, Paul Agostinho
    Singhal, Parth
    Khorajiya, Moin
    JOURNAL OF DISCRETE MATHEMATICAL SCIENCES & CRYPTOGRAPHY, 2020, 23 (02) : 563 - 571
  • [40] Efficient Classification of DDoS Attacks Using an Ensemble Feature Selection Algorithm
    Singh, Khundrakpam Johnson
    De, Tanmay
    JOURNAL OF INTELLIGENT SYSTEMS, 2020, 29 (01) : 71 - 83