Analysis of the Impact of the Slow HTTP DOS and DDOS Attacks on the Cloud Environment

被引:0
|
作者
Yevsieieva, Oksana [1 ]
Helalat, Seyed Milad [1 ]
机构
[1] Kharkiv Natl Univ Radioelect, Dept Infocommun Engn, Kharkov, Ukraine
关键词
Cloud Security; Denial of service; Slow HTTP attack; virtualization;
D O I
暂无
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Denial of service (DOS) attack is an attack which the attacker or attackers make an effort to make a service or resources out of access. If attackers want to intensify the power of the attack or stay undetected they might use Distributed DOS (DDOS) methods. Application layer DOS (ADOS) attacks are exploiting a flaw in a software, therefore they need fewer resources on the attacker side, hard to detect and also stronger hardware on the victim side doesn't guarantee the attack failed. Slow HTTP attacks are ADOS attack in which they take advantage of the architecture of HTTP protocol connection's mechanism and target the vulnerable web servers. There has been much research about ways to prevent the Slow HTTP DOS attacks, but none of these studies have considered the impact of such DOS and DDOS attack on the virtual environment which is the core of cloud computing. One of the main question that this paper is trying to solve is, what impacts Slow HTTP DOS and DDOS can cause on a virtual machine (VM) also on the neighbor VM. In this paper, we demonstrate and analyze the direct and indirect impact of the slow HTTP DOS and DDOS on the virtual environment, we will launch the Slowloris DOS and DDOS on VMs and analyze the direct impact of the attack on the target VM also the indirect impact of the attack on the neighbor VM.
引用
收藏
页码:519 / 523
页数:5
相关论文
共 50 条
  • [21] Detection Techniques for DDoS Attacks in Cloud Environment: Review Paper
    Alanazi, Sultan T.
    Anbar, Mohammed
    Karuppayah, Shankar
    Al-Ani, Ahmed K.
    Sanjalawe, Yousef K.
    INTELLIGENT AND INTERACTIVE COMPUTING, 2019, 67 : 337 - 354
  • [22] Machine Learning Methods for DDoS Attacks Detection in the Cloud Environment
    Ouhssini, Mohamed
    Afdel, Karim
    ADVANCED INTELLIGENT SYSTEMS FOR SUSTAINABLE DEVELOPMENT (AI2SD'2020), VOL 2, 2022, 1418 : 401 - 413
  • [23] Analysis and Detection of DDoS Attacks on Cloud Computing Environment using Machine Learning Techniques
    Wani, Abdul Raoof
    Rana, Q. P.
    Saxena, U.
    Pandey, Nitin
    PROCEEDINGS 2019 AMITY INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE (AICAI), 2019, : 870 - 875
  • [24] DDoS Attacks Analysis in Bigdata (Hadoop) Environment
    Ahmad, Shakeel
    Yasin, Amanullah
    Shafi, Qaisar
    PROCEEDINGS OF 2018 15TH INTERNATIONAL BHURBAN CONFERENCE ON APPLIED SCIENCES AND TECHNOLOGY (IBCAST), 2018, : 495 - 501
  • [25] Detection of DoS and DDoS Attacks in NGMN Using Frequency Domain Analysis
    Hashim, Fazirulhisyam
    Kibria, M. Rubaiyat
    Jamalipour, Abbas
    2008 14TH ASIA-PACIFIC CONFERENCE ON COMMUNICATIONS, (APCC), VOLS 1 AND 2, 2008, : 547 - 551
  • [26] Solutions for DDoS Attacks on Cloud
    Bhardwaj, Akashdeep
    Subrahmanyam, G. V. B.
    Avasthi, Vinay
    Sastry, Hanumat G.
    2016 6th International Conference - Cloud System and Big Data Engineering (Confluence), 2016, : 163 - 167
  • [27] Extenuate DDoS Attacks in Cloud
    Kiranmai, B.
    Damodaram, A.
    PROCEEDINGS OF THE 2016 2ND INTERNATIONAL CONFERENCE ON APPLIED AND THEORETICAL COMPUTING AND COMMUNICATION TECHNOLOGY (ICATCCT), 2016, : 235 - 238
  • [28] Detection of HTTP DDoS Attacks Using NFStream and TensorFlow
    Chovanec, Martin
    Hasin, Martin
    Havrilla, Martin
    Chovancova, Eva
    APPLIED SCIENCES-BASEL, 2023, 13 (11):
  • [29] HTTP/2 Tsunami: Investigating HTTP/2 Proxy Amplification DDoS Attacks
    Beckett, David
    Sezer, Sakir
    2017 SEVENTH INTERNATIONAL CONFERENCE ON EMERGING SECURITY TECHNOLOGIES (EST), 2017, : 127 - 132
  • [30] An AI Based IDS Framework For Detecting DDoS Attacks In Cloud Environment
    Varma, S. Asha
    Reddy, K. Ganesh
    INFORMATION SECURITY JOURNAL, 2023, 33 (06): : 613 - 625