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181 results on '"Liu, Yuan'an"'

2. Game-based Pricing and Task Offloading in Mobile Edge Computing Enabled Edge-Cloud Systems

Author

Su, Yi, Fan, Wenhao, Liu, Yuan'an, and Wu, Fan

Subjects
Computer Science - Computer Science and Game Theory
Abstract

As a momentous enabling of the Internet of things (IoT), mobile edge computing (MEC) provides IoT mobile devices (MD) with powerful external computing and storage resources. However, a mechanism addressing distributed task offloading and price competition for the open exchange marketplace has not been established properly, which has become a huge obstacle to MEC's application in the IoT market. In this paper, we formulate a distributed mechanism to analyze the interaction between OSPs and IoT MDs in the MEC enabled edge-cloud system by appling multi-leader multi-follower two-tier Stackelberg game theory. We first prove the existence of the Stackelberg equilibrium, and then we propose two distributed algorithms, namely iterative proximal offloading algorithm (IPOA) and iterative Stackelberg game pricing algorithm (ISPA). The IPOA solves the follower non-cooperative game among IoT MDs and ISPA uses backward induction to deal with the price competition among OSPs. Experimental results show that IPOA can markedly reduce the disutility of IoT MDs compared with other traditional task offloading schemes and the price of anarchy is always less than 150\%. Besides, results also demonstrate that ISPA is reliable in boosting the revenue of OSPs., Comment: 12 pages, 9 figures

Published
2021

3. FamDroid: Learning-Based Android Malware Family Classification Using Static Analysis

Author

fan, Wenhao, Zhao, Liang, Wang, Jiayang, Chen, Ye, Wu, Fan, and Liu, Yuan'an

Subjects
Computer Science - Cryptography and Security
Abstract

Android is currently the most extensively used smartphone platform in the world. Due to its popularity and open source nature, Android malware has been rapidly growing in recent years, and bringing great risks to users' privacy. The malware applications in a malware family may have common features and similar behaviors, which are beneficial for malware detection and inspection. Thus, classifying Android malware into their corresponding families is an important task in malware analysis. At present, the main problem of existing research works on Android malware family classification lies in that the extracted features are inadequate to represent the common behavior characteristics of the malware in malicious families, and leveraging a single classifier or a static ensemble classifier is restricted to further improve the accuracy of classification. In this paper, we propose FamDroid, a learning-based Android malware family classification scheme using static analysis technology. In FamDroid, the explicit features including permissions, hardware components, app components, intent filters are extracted from the apk files of a malware application. Besides, a hidden feature generated from the extracted APIs is used to represents the API call relationship in the application. Then, we design an adaptive weighted ensemble classifier, which considers the adaptability of the sample to each base classifier, to carry out accurate malware family classification. We conducted experiments on the Drebin dataset which contains 5560 Android malicious applications. The superiority of FamDroid is demonstrated through comparing it with 5 traditional machine learning models and 4 state-of-the-art reference schemes. FamDroid can correctly classify 98.92% of malware samples into their families and achieve 99.12% F1-Score., Comment: This manuscript has been submitted to Neurocomputing and is currently under its consideration for publication

Published
2021

32. A Proxy Based Information Integration System for Distributed Wireless Sensor Networks

Author

Li, Li, Liu, Yuan’an, Tang, Bihua, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, and Luo, Yuhua, editor

Published
2007
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41. Efficient PAPR reduction algorithm in OFDM based on nonlinear piecewise companding.

Author

XING Zhitong, LI Yun, PENG Deyi, ZHANG Bensi, LIU Kaiming, and LIU Yuan'an

Abstract

Focusing on the high peak-to-average power ratio (PAPR) problem in orthogonal frequency division multiplexing (OFDM) systems, a generalized hybrid of rayleigh and sine distribution based nonlinear companding algorithm for PAPR reduction in OFDM systems was provided. For the proposed algorithm, signal samples with small amplitudes remain unchanged. For the signal samples with large amplitudes, their probability density function were changed from rayleigh distribution to sine-based distribution. The proposed algorithm can effectively reduce the PAPR, and at the same time, maintain the bit error rate performance and power spectral density performance. Simulation results indicate that with the same PAPR performance, compared with referred companding schemes, the proposed algorithm has lower bit error rate and out of band radiation. Keywords: OFDM, PAPR, companding, signal distortion [ABSTRACT FROM AUTHOR]

Published
2022
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