skip to main content
10.1145/3542954.3542984acmotherconferencesArticle/Chapter ViewAbstractPublication PagesiccaConference Proceedingsconference-collections
research-article

Detecting Intrusion in Cloud using Snort: An Application towards Cyber-Security

Authors Info & Claims
Published:11 August 2022Publication History

ABSTRACT

Internet, various kinds of services are delivered using cloud computing. These resources include storing data, servers, databases records, networking systems, and software. Many people choose cloud computing for businesses because of it’s budget-friendly, excellent efficiency and performance features. But lately, the intrusion on cloud-based system raised a significant concern for choosing this platform. Due to the increasing rate of computer networks and their usage, the global IT infrastructure is prone to attacks. If the issue is left unattended, it can cause significant trouble for IT sector. Intrusion detection systems are signature-based, means it would look in a certain type of data packet, and it would watch all the traffic in a network which has visibility to and make a decision if it is good traffic or bad. It alerts analyst to look at the traffic that could be malicious. Snort is an open-source intrusion detection system that can monitor any traffic going in or out of the network and also monitor malicious behavior or any violation. To enable secure and trustworthy information transmission across diverse cloud companies in today’s networked business settings, a high level of security is required. Because cyber-attacks are only getting more complicated these days, it is critical that defense technology keeps up. After traditional security technologies fail, an intrusion detection system functions as an adaptive safeguard device for system security. In our research, we have shown by using this protocol traffic from source 0.0.0.0:68 to DST 255.255.255:67 (UDP) is being blocked on our WAN. It prevents the user from obtaining a DHCP address.

References

  1. Omar Achbarou, My Ahmed El Kiram, Outmane Bourkoukou, and Salim Elbouanani. 2018. A new distributed intrusion detection system based on multi-agent system for cloud environment. International Journal of Communication Networks and Information Security 10, 3(2018), 526.Google ScholarGoogle Scholar
  2. Celyn Birkinshaw, Elpida Rouka, and Vassilios G Vassilakis. 2019. Implementing an intrusion detection and prevention system using software-defined networking: Defending against port-scanning and denial-of-service attacks. Journal of Network and Computer Applications 136 (2019), 71–85.Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Vittorio Cozzolino, Nikolai Schwellnus, Jörg Ott, and Aaron Yi Ding. 2020. UIDS: Unikernel-based Intrusion Detection System for the Internet of Things. In DISS 2020-Workshop on Decentralized IoT Systems and Security.Google ScholarGoogle Scholar
  4. Sudhir N Dhage and BB Meshram. 2012. Intrusion detection system in cloud computing environment. International Journal of Cloud Computing 1, 2-3 (2012), 261–282.Google ScholarGoogle ScholarCross RefCross Ref
  5. Mondher Essid, Farah Jemili, and Ouajdi Korbaa. 2019. Distributed Architecture of Snort IDS in Cloud Environment. In International Conference on Intelligent Systems Design and Applications. Springer, 100–111.Google ScholarGoogle Scholar
  6. Akash Garg and Prachi Maheshwari. 2016. Performance analysis of snort-based intrusion detection system. In 2016 3rd International Conference on Advanced Computing and Communication Systems (ICACCS), Vol. 1. IEEE, 1–5.Google ScholarGoogle ScholarCross RefCross Ref
  7. Zohaib Hassan, Roman Odarchenko, Sergiy Gnatyuk, Abnash Zaman, Masroor Shah, 2018. Detection of distributed denial of service attacks using snort rules in cloud computing & remote control systems. In 2018 IEEE 5th International Conference on Methods and Systems of Navigation and Motion Control (MSNMC). IEEE, 283–288.Google ScholarGoogle ScholarCross RefCross Ref
  8. Mohamed Idhammad, Karim Afdel, and Mustapha Belouch. 2018. Distributed intrusion detection system for cloud environments based on data mining techniques. Procedia Computer Science 127 (2018), 35–41.Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Dhanamma Jagli, Seema Purohit, and N Subash Chandra. 2017. SaaS CloudQual: a quality model for evaluating software as a service on the cloud computing environment. In Innovations in computer science and engineering. Springer, 73–80.Google ScholarGoogle Scholar
  10. R Kalaiprasath, R Elankavi, and R Udayakumar. 2017. Cloud security and compliance-a semantic approach in end to end security. International Journal on Smart Sensing and Intelligent Systems 10, 5(2017).Google ScholarGoogle ScholarCross RefCross Ref
  11. Nattawat Khamphakdee, Nunnapus Benjamas, and Saiyan Saiyod. 2014. Improving intrusion detection system based on snort rules for network probe attack detection. In 2014 2nd International Conference on Information and Communication Technology (ICoICT). IEEE, 69–74.Google ScholarGoogle ScholarCross RefCross Ref
  12. George Loukas, Tuan Vuong, Ryan Heartfield, Georgia Sakellari, Yongpil Yoon, and Diane Gan. 2017. Cloud-based cyber-physical intrusion detection for vehicles using deep learning. Ieee Access 6(2017), 3491–3508.Google ScholarGoogle ScholarCross RefCross Ref
  13. Xiaoyu Ma, Xiao Fu, Bin Luo, Xiaojiang Du, and Mohsen Guizani. 2019. A design of firewall based on feedback of intrusion detection system in cloud environment. In 2019 IEEE Global Communications Conference (GLOBECOM). IEEE, 1–6.Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Amar Meryem and Bouabid EL Ouahidi. 2020. Hybrid intrusion detection system using machine learning. Network Security 2020, 5 (2020), 8–19.Google ScholarGoogle ScholarCross RefCross Ref
  15. Chirag Modi, Dhiren Patel, Bhavesh Borisaniya, Hiren Patel, Avi Patel, and Muttukrishnan Rajarajan. 2013. A survey of intrusion detection techniques in cloud. Journal of network and computer applications 36, 1(2013), 42–57.Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Chnar Mustafa Mohammed, Subhi RM Zebaree, 2021. Sufficient comparison among cloud computing services: IaaS, PaaS, and SaaS: A review. International Journal of Science and Business 5, 2 (2021), 17–30.Google ScholarGoogle Scholar
  17. Iqra Sattar, Muhammad Shahid, and Younis Abbas. 2015. A review of techniques to detect and prevent distributed denial of service (DDoS) attack in cloud computing environment. International Journal of Computer Applications 115, 8(2015).Google ScholarGoogle ScholarCross RefCross Ref
  18. Ashish Singh and Kakali Chatterjee. 2017. Cloud security issues and challenges: A survey. Journal of Network and Computer Applications 79 (2017), 88–115.Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Nalini Subramanian and Andrews Jeyaraj. 2018. Recent security challenges in cloud computing. Computers & Electrical Engineering 71 (2018), 28–42.Google ScholarGoogle ScholarCross RefCross Ref
  20. Hamed Tabrizchi and Marjan Kuchaki Rafsanjani. 2020. A survey on security challenges in cloud computing: issues, threats, and solutions. The journal of supercomputing 76, 12 (2020), 9493–9532.Google ScholarGoogle Scholar
  21. I Sumaiya Thaseen and Ch Aswani Kumar. 2014. Intrusion detection model using fusion of PCA and optimized SVM. In 2014 International conference on contemporary computing and informatics (IC3I). IEEE, 879–884.Google ScholarGoogle ScholarCross RefCross Ref
  22. Luis M Vaquero, Luis Rodero-Merino, and Daniel Morán. 2011. Locking the sky: a survey on IaaS cloud security. Computing 91, 1 (2011), 93–118.Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Wenjuan Wang, Xuehui Du, Dibin Shan, Ruoxi Qin, and Na Wang. 2020. Cloud intrusion detection method based on stacked contractive auto-encoder and support vector machine. IEEE Transactions on Cloud Computing(2020).Google ScholarGoogle ScholarCross RefCross Ref
  24. Qingqing Zhang, Hongbian Yang, Kai Li, and Qian Zhang. 2010. Research on the intrusion detection technology with hybrid model. In 2010 The 2nd Conference on Environmental Science and Information Application Technology, Vol. 2. IEEE, 646–649.Google ScholarGoogle Scholar

Index Terms

  1. Detecting Intrusion in Cloud using Snort: An Application towards Cyber-Security

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Other conferences
      ICCA '22: Proceedings of the 2nd International Conference on Computing Advancements
      March 2022
      543 pages
      ISBN:9781450397346
      DOI:10.1145/3542954

      Copyright © 2022 ACM

      Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 11 August 2022

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article
      • Research
      • Refereed limited
    • Article Metrics

      • Downloads (Last 12 months)90
      • Downloads (Last 6 weeks)7

      Other Metrics

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    HTML Format

    View this article in HTML Format .

    View HTML Format