Cited By
View all- Lei YNing DJie Y(2023)Multi-Sample Cooperative Training SDN Intrusion Detection System Based on Transformer2023 China Automation Congress (CAC)10.1109/CAC59555.2023.10450455(1342-1348)Online publication date: 17-Nov-2023
Feedback based Sampling for Intrusion Detection in Software Defined Network
Pages 95 - 99
Abstract
Cloud computing is being deployed more and more widely. However, the difficulty of monitoring the huge east-west traffic is a great security concern. In this paper, we proposed FBSample, a sampling method which employs the central control feature of SDN and feedback information of IDS. Evaluation results show FBSample can largely reduce the amount of packets to be transferred while maintaining a relatively high detection precision.
References
[1]
https://neuvector.com/blog/securing-east-west-traffic-in-container-based-data-center/
[2]
Chung C J, Khatkar P, Xing T, et al. NICE: network intrusion detection and countermeasure selection in virtual network systems{J}. IEEE transactions on dependable and secure computing, 2013, 10(4): 198--211.
[3]
Xing T, Xiong Z, Huang D, et al. SDNIPS: enabling soft-ware-defined networking based intrusion prevention system in clouds{C}//The 10th International Conference on Network and Service Management (CNSM) and Workshop. 2014: 308--311.
[4]
Yoon C, Park T, Lee S, et al. Enabling security functions with SDN: a feasibility study{J}. Computer Networks, 2015, 85: 19--35.
[5]
Jeong C, Ha T, Narantuya J, et al. Scalable network intrusion detection on virtual SDN environment{C}//2014 IEEE 3rd International Conference on Cloud Networking (CloudNet). 2014: 264--265.
[6]
Ha T, Yoon S, Risdianto A C, et al. Suspicious flow forwarding for multiple intrusion detection systems on soft-ware-defined networks{J}. IEEE Network, 2016, 30(6): 22--27.
[7]
Xing T, Huang D, Xu L, et al. Snortflow: a OpenFlow-based intrusion prevention system in cloud environment{C}//2013 Second GENI Research and Educational Experiment Workshop (GREE). 2013: 89--92.
[8]
Ha T, Kim S, An N, et al. Suspicious traffic sampling for intrusion detection in software-defined networks{J}. Computer Networks, 2016.
[9]
Cui JS, Guo C, Chen L, et al. Establishing process-level defense-in-depth framework for software defined networks{J}. Journal of Software, 2014, 25(10):2251--2265.
[10]
B. Rahbarinia, R. Perdisci, and A. Lanzi, "Peerrush: Mining for unwanted p2p traffic," Journal of Information Security and Applications, Vol. 19, 2014, pp. 194--208
[11]
Shiravi A, Shiravi H, Tavallaee M, et al. Toward developing a systematic approach to generate benchmark datasets for intrusion detection{J}. computers & security, 2012, 31(3): 357--374.
Index Terms
- Feedback based Sampling for Intrusion Detection in Software Defined Network
Recommendations
Malware Detection for Mobile Devices Using Software-Defined Networking
GREE '13: Proceedings of the 2013 Second GENI Research and Educational Experiment WorkshopThe rapid adoption of mobile devices comes with the growing prevalence of mobile malware. Mobile malware poses serious threats to personal information and creates challenges in securing network. Traditional network services provide connectivity but do ...
Suspicious traffic sampling for intrusion detection in software-defined networks
In order to defend a cloud computing system from security attackers, an intrusion detection system (IDS) is widely used to inspect suspicious traffic on the network. However, the processing capacity of an IDS is much smaller than the amount of traffic ...
Ransomware detection and mitigation using software-defined networking: The case of WannaCry
AbstractModern day ransomware families implement sophisticated encryption and propagation schemes, thus limiting chances to recover the data almost to zero. We investigate the use of software-defined networking (SDN) to detect and mitigate ...
Comments
Information & Contributors
Information
Published In
March 2018
187 pages
ISBN:9781450363617
DOI:10.1145/3199478
Copyright © 2018 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]
In-Cooperation
- Wuhan Univ.: Wuhan University, China
- University of Electronic Science and Technology of China: University of Electronic Science and Technology of China
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 16 March 2018
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Funding Sources
- Education Ministry - China Mobile Research Funding
Conference
ICCSP 2018
ICCSP 2018: 2018 the 2nd International Conference on Cryptography, Security and Privacy
March 16 - 19, 2018
Guiyang, China
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations1Total Citations
- 94Total Downloads
- Downloads (Last 12 months)3
- Downloads (Last 6 weeks)0
Reflects downloads up to 09 Nov 2024
Other Metrics
Citations
Cited By
View all- Lei YNing DJie Y(2023)Multi-Sample Cooperative Training SDN Intrusion Detection System Based on Transformer2023 China Automation Congress (CAC)10.1109/CAC59555.2023.10450455(1342-1348)Online publication date: 17-Nov-2023
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in