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  • A Survey of Machine Learning Techniques Applied to Software Defined Networking (SDN): Research Issues and Challenges

    • Authors: Xie, J.;  Advisor: -;  Participants: Yu, F. R.; Huang, T.; Xie, R.; Liu, J.; Wang, C.; Liu, Y. (2019)

  • In recent years, with the rapid development of current Internet and mobile communication technologies, the infrastructure, devices and resources in networking systems are becoming more complex and heterogeneous. In order to efficiently organize, manage, maintain and optimize networking systems, more intelligence needs to be deployed. However, due to the inherently distributed feature of traditional networks, machine learning techniques are hard to be applied and deployed to control and operate networks. Software defined networking (SDN) brings us new chances to provide intelligence inside the networks. The capabilities of SDN (e.g., logically centralized control, global view of the network, software-based traffic analysis, and dynamic updating of forwarding rules) make it easier to ...
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  • A Combination Method for Android Malware Detection Based on Control Flow Graphs and Machine Learning Algorithms

    • Authors: Ma, Z.;  Advisor: -;  Participants: Ge, H.; Liu, Y.; Zhao, M.; Ma, J. (2019)

  • Android malware severely threaten system and user security in terms of privilege escalation, remote control, tariff theft, and privacy leakage. Therefore, it is of great importance and necessity to detect Android malware. In this paper, we present a combination method for Android malware detection based on the machine learning algorithm. First, we construct the control ow graph of the application to obtain API information. Based on the API information, we innovatively construct Boolean, frequency, and time-series data sets. Based on these three data sets, three detection models for Android malware detection regarding API calls, API frequency, and API sequence aspects are constructed. Ultimately, an ensemble model is constructed for conformity. We tested and compared the accuracy an...
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  • A Nonlinear Regression Application via Machine Learning Techniques for Geomagnetic Data Reconstruction Processing

    • Authors: Liu, H.;  Advisor: -;  Participants: Liu, Z.; Liu, S.; Liu, Y.; Bin, J.; Shi, F.; Dong, H. (2019)

  • The integrity of geomagnetic data is a critical factor in understanding the evolutionary process of Earth’s magnetic field, as it provides useful information for near-surface exploration, unexploded explosive ordnance detection, and so on. Aimed to reconstruct undersampled geomagnetic data, this paper presents a geomagnetic data reconstruction approach based on machine learning techniques. The traditional linear interpolation approaches are prone to time inefficiency and high labor cost, while the proposed approach has a significant improvement. In this paper, three classic machine learning models, support vector machine, random forests, and gradient boosting were built. Besides, a deep learning algorithm, recurrent neural network, was explored to further improve the training perform...
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  • Power Allocation Schemes Based on Machine Learning for Distributed Antenna Systems

    • Authors: Liu, Y.;  Advisor: -;  Participants: He, C.; Li, X.; Zhang, C.; Tian, C. (2019)

  • In this paper, we investigate the great potential of the combination of machine learning technology and wireless communications. Currently, many researchers have proposed various optimization algorithms on resource allocation for distributed antenna systems (DASs). However, the existing methods are mostly hard to implement because of their high computational complexity. In this paper, a new system model for machine learning is considered for the scenario of DAS, which is more practical with its low computational complexity. We utilize the k-nearest neighbor (k-NN) algorithm based on the database of a traditional sub-gradient iterative method to get a power allocation scheme for DAS. The simulation results show that our k-NN algorithm can also obtain the power distribution scheme whi...
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