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A light-weight trust-based QoS routing algorithm for ad hoc networks
A Deep Learning Based Fast-Flux and CDN Domain Names Recognition Method
- Xunxun Chen, Gaochao Li, Yongzheng Zhang, Xiao Wu, Changbo Tian
- Computer ScienceProceedings of the 2nd International Conference…
- 16 March 2019
A method called Fast-flux and CDN Domains Recognizer (FCDR) to self-learning FF andCDN features and recognition them based on LSTM network so that the FCDR can classify and identify Fast Flux domain name, CDN domainName, other malicious domain names and common non-CDN domain name more accurately.
An Efficient Approach to Event Detection and Forecasting in Dynamic Multivariate Social Media Networks
- Minglai Shao, Jianxin Li, F. Chen, Hongyi Huang, Shuai Zhang, Xunxun Chen
- Computer ScienceWWW
- 3 April 2017
This paper generalizes traditional nonparametric statistics, and proposes a new class of scan statistic functions for measuring the joint significance of evolving subgraphs and subsets of attributes to indicate the ongoing or forthcoming event in dynamic multivariate networks.
MFRdnsI: A DNS Recursive Server Identification and Classification Method Based on Deep Learning
A recursive server identification method based on deep learning called MFRdnsI is proposed, which analysis the traffic direction characteristics, traffic statistics features and protocol field features in the DNS resolving records and uses multi-layer perceptron for automatic learning of multidimensional features.
Detecting Information Theft Based on Mobile Network Flows for Android Users
- Zhenyu Cheng, Xunxun Chen, Yongzheng Zhang, Shuhao Li, Yafei Sang
- Computer ScienceInternational Conference on Networking…
- 1 August 2017
A detection model based on the analysis of relationships between behavior patterns and network flows is proposed, which achieves its goal by using the Random Forest machine learning algorithm to classify the network flows into benign or malicious.
An Efficient Framework for Detecting Evolving Anomalous Subgraphs in Dynamic Networks
- Minglai Shao, Jianxin Li, F. Chen, Xunxun Chen
- Computer Science, MathematicsIEEE INFOCOM - IEEE Conference on Computer…
- 16 April 2018
This paper generalizes traditional nonparametric scan statistics, and proposes a large class of scan statistic functions for measuring the significance of evolving subgraphs in dynamic networks, and decomposes each scan statistic function as a sequence of subproblems with provable guarantees.
Content-level deduplication on mobile internet datasets
A new framework called SF-Dedup is proposed which is able to implement the deduplication process on a large set of Mobile Internet records, which can be smaller than 100B, or even smaller than 10B.
A Comprehensive Measurement Study of Domain-Squatting Abuse
- Yuwei Zeng, Tianning Zang, Yongzheng Zhang, Xunxun Chen, Yipeng Wang
- Computer ScienceICC - IEEE International Conference on…
- 1 May 2019
Although typo-squatting accounts for most of squatting domains, combo-Squatting are able to attract more traffic, and further case studies show that parking ads is still the most important way for attackers to make profits, the results clearly call for the need to better protect the intellectual property of domain names.
WM+: An Optimal Multi-pattern String Matching Algorithm Based on the WM Algorithm
The tuned WM algorithm (abbreviated as WM+) can reach higher performance by improving the shift table building algorithm and combining the AC algorithm with the original WM algorithm, and the scanning time of the WM+ algorithm in the worst case is predictable.
Structured Sparsity Model Based Trajectory Tracking Using Private Location Data Release
- Minglai Shao, Jianxin Li, Qiben Yan, Feng Chen, Hongyi Huang, Xunxun Chen
- Computer ScienceIEEE Transactions on Dependable and Secure…
- 7 February 2020
A novel location inference attack framework, iTracker, which simultaneously recovers multiple trajectories from differentially private trajectory data using the structured sparsity model is proposed, which is more effective in recovering multiple private trajectories simultaneously.