BAMBOO: A Multi-instance Multi-label Approach Towards VDI User Logon Behavior Modeling
Authors: Wenping Fan, Yao Zhang, Qichen Hao, Xinya Wu, Min-Ling Zhang
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental studies on real VDI customers data clearly validate the effectiveness of the proposed MIML-based approach against state-of-the-art VDI user logon behavior modeling techniques. |
| Researcher Affiliation | Collaboration | 1VMware Information Technology (China) Ltd. 2School of Computer Science and Engineering, Southeast University, Nanjing 210096, China 3Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, China |
| Pseudocode | Yes | Algorithm 1: The pseudo-code of BAMBOO |
| Open Source Code | No | The paper does not provide any specific links or explicit statements about the availability of the source code for the methodology described. |
| Open Datasets | No | The paper states, 'The experiments are conducted on five real VDI customers data sets where the logon behavior granularity interval is set to 30 minutes.' It does not provide any concrete access information (link, DOI, repository, or formal citation) for a publicly available or open dataset. |
| Dataset Splits | No | The paper mentions 'Given the validation set V' within the algorithm description, but it does not provide specific details on the dataset split for training, validation, and testing (e.g., percentages, sample counts, or explicit instructions for creating these splits). |
| Hardware Specification | No | The paper mentions 'We use the AWS EC2 instance t3.medium ($0.06/hour) as the virtual machine of VDI desktops to calculate the infrastructure cost.' This refers to the environment being modeled (VDI desktops), not the specific hardware used to conduct the research experiments (e.g., CPU/GPU models for training or inference). |
| Software Dependencies | No | The paper mentions 'GBDT ([Friedman, 2001; 2002]) is used as instance-level learner' but does not specify any software libraries or dependencies with their version numbers. |
| Experiment Setup | Yes | For BAMBOO, the algorithm parameters are set as following. The feature extraction sequence length is the same as SOUP. The supporting user group size n is set to 2. The boosting rounds R is set to 30. GBDT is used as instance-level learner. The candidate selection rate ζ is set to 0.5. The error-rate balance parameter ν is set to 0.8. |