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	1. GPALzz: A state-leading fuzzing framework via basic network communication interface
	HAO Wen-Peng,GUO Yan-Hui
	Computer Science and Technology 25 March 2024
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	Abstract:Today, the rapid adoption of IoT devices has brought security risks while facilitating people's lives. Latest research focuses on information acquisition and state establishment, however exists the defects of relys on vendors and lacks universality; focuses on communication formats rather than device information; difficult to accurately guide fuzzing into specific states then in a stable state. To address the above issues, we propose an interactive leading fuzzing scheme, named GPALzz. It uses active automata learning to break free from vendor dependence, establishes device service state guidance fuzzy testing, and utilies interaction capabilities to establish an equivalent automaton that describes device service state information. Furthermore, we selected 9 devices to verify our scheme and discovered 24 crashes in 7 devices.
	TO cite this article:HAO Wen-Peng,GUO Yan-Hui. GPALzz: A state-leading fuzzing framework via basic network communication interface[OL].[25 March 2024] http://en.paper.edu.cn/en_releasepaper/content/4762868


	2. DTAME: A Unified Approach for ABAC Policy Mining and Efficient Evaluation Using Decision Trees
	LAN Ze-Jun, GUAN Jian-Feng
	Computer Science and Technology 28 February 2024
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	Abstract:\justifying Attribute-Based Access Control (ABAC) has been chosen to replace the traditional access control model due to its dynamics, flexibility and scalability recently. However, during the migration and deployment process of ABAC policies, the key issue is how to mine an accurate and concise access control policy collection and quickly evaluate the policies when an access request arrives. Previous studies have typically taken the problems of policy mining and policy evaluation separately. Policy mining primarily focuses on the compactness of the policy itself, while policy evaluation concentrates on assessing the performance of policy matching. The lack of coordination between policy mining and policy evaluation results in that the concise strategy obtained through policy mining cannot maximize the performance of policy evaluation. To trick this issue, this paper proposed a decision tree based ABAC policy mining and policy evaluation (DTAME) scheme that addresses both issues concurrently by introducing an ABAC policy mining and evaluation method based on the decision tree algorithm. On the other hand, some hotspot policy rules are frequently accessed in some scenarios. Therefore, to maximize evaluation performance, this paper also optimizes the algorithm based on access control logs. Experimental results show that the DTAME can enhance the performance of policy evaluation while ensuring that the mined policies remain compact and effective.
	TO cite this article:LAN Ze-Jun, GUAN Jian-Feng. DTAME: A Unified Approach for ABAC Policy Mining and Efficient Evaluation Using Decision Trees[OL].[28 February 2024] http://en.paper.edu.cn/en_releasepaper/content/4762310

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	3. User Authentication from Smartwatch Photoplethysmography sensor
	TAN ZhiHao,HUANG Qinlong,YANG Yixian
	Computer Science and Technology 22 March 2021
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	Abstract:With the rapid proliferation of smartwatch, a secure and convenient smartwatch-based user authentication scheme are desired. As the widely deployed bioelectrical signal sensor in smartwatch, Photoplethysmography (PPG) sensors have shown potentials for authentication. Existing authentication solutions usually have some limitations. They require the user to provide an amount of registration data from user to reflect the profile of user, which may impact the experience of user. In this paper, we propose a PPG-based smartwatch authentication scheme. We leverage the Siamese Network to extract the feature of user from the PPG signal affected by the finger-level gesture for authentication. We conduct some experiments to evaluate the performance of the scheme. The experiment results show that our model has an average accuracy rate of 92.43\%. In addition, the authentication model can achieve high authentication accuracy with a small amount of user registration data.
	TO cite this article:TAN ZhiHao,HUANG Qinlong,YANG Yixian. User Authentication from Smartwatch Photoplethysmography sensor[OL].[22 March 2021] http://en.paper.edu.cn/en_releasepaper/content/4754180


	4. A Elimination Mechanism of Glue Variables for Solving SAT Problems in Linguistics
	ZHANG Ziwei,ZHANG Yang
	Computer Science and Technology 04 February 2021
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	Abstract:We propose GVE(Glue Variables Elimination), an algorithm that organically combines neural networks with a deterministic solver to solve SAT(Boolean satisfiability problem) in the filed of linguistics. It gives full play to their respective advantages by following steps: (a) finding the glue variables of the problem; (b) determining their values; (c) simplifying the original formula; (d) using a deterministic solver to solve the simplified problem. We use SATCOMP 2003-2019 benchmarks as the test data sets, and compare our model with the SAT solver CADICAL that has performed well in SATCOMP 2019 as well as the neural network models proposed in recent years. GVE model shows good performance. As the complexity of the problem increases, the solution time is much better than the deterministic solver, while at the same time more accurate than other neural network models.
	TO cite this article:ZHANG Ziwei,ZHANG Yang. A Elimination Mechanism of Glue Variables for Solving SAT Problems in Linguistics[OL].[ 4 February 2021] http://en.paper.edu.cn/en_releasepaper/content/4753646


	5. PPMT: Privacy-Preserving Genomic Data Sharing with Personalized Medicine Testing in Cloud Computing
	YUE Wei,HUANG Qinlong,YANG Yixian
	Computer Science and Technology 09 January 2020
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	Abstract:With the rapid development of bioinformatics and the availability of genetic sequencing technologies, genomic data ushers in a new era of precision medicine. Cloud computing, features as low cost, rich storage and rapid processing can precisely respond to the challenges brought by the emergence of massive genomic data. Considering the security of cloud platform and the privacy of genomic data, we firstly introduce PPMT which utilizes key-policy attribute-based encryption (KP-ABE) to realize genomic data access control with abundant attributes, and employs KP-ABE with equality test to achieve personalized medicine test by matching digitized single nucleotide polymorphisms (SNPs) directly on the users' ciphertext without encrypting multiple times. We conduct extensive experiments with the dataset ``1000 Genomes", and the results show that PPMT can greatly reduce the computation and communication overhead compared with existing schemes and are practical enough test authorization requirements.
	TO cite this article:YUE Wei,HUANG Qinlong,YANG Yixian. PPMT: Privacy-Preserving Genomic Data Sharing with Personalized Medicine Testing in Cloud Computing[OL].[ 9 January 2020] http://en.paper.edu.cn/en_releasepaper/content/4750385


	6. Secure Task Allocation Based on Anonymous Identity in Mobile Crowdsourcing
	HE Yue,HUANG Qinlong,HUANG Qinlong,NIU Xin-Xin,NIU Xin-Xin
	Computer Science and Technology 02 January 2020
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	Abstract:Mobile crowdsourcing emerges as a new paradigm, which assembles a group of workers with mobile devices to cooperatively achieve arduous tasks delegated by requester. However, due to varieties of attacks and untrustworthy mobile crowdsourcing server, mobile crowdsourcing inevitably raises secure concerns, in which confidentiality of task and identity privacy of worker will be exposed. Consequently, in this paper, a secure task allocation based on anonymous identity scheme in mobile crowdsourcing is proposed to resist against these threats. It leverages identity-based broadcast proxy re-encryption to distribute task from requester to a crowd of workers without the leakage of task data. In addition, pseudoidentity technique is utilized to protect identity of worker when it submits its identity to apply for task. The extensive analysis illustrates that this scheme can preserve confidentiality of task data and identity privacy of worker and withstand various attacks. Experiments have been conducted to validate the performance of this scheme.
	TO cite this article:HE Yue,HUANG Qinlong,HUANG Qinlong, et al. Secure Task Allocation Based on Anonymous Identity in Mobile Crowdsourcing[OL].[ 2 January 2020] http://en.paper.edu.cn/en_releasepaper/content/4750396


	7. Optimizing data drilling with the user interaction weighted minimum description length principle
	Tao Fang,Shen Qiwei,Li Wei,Xu Tong
	Computer Science and Technology 13 December 2018
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	Abstract:Interaction exploration is an effective way to obtain potential information from the treemap that visualized large-scale data. Data exploration techniques that has been proposed (such as translation and scaling on a two-dimensional plane) provide limited context information only or provide excessive distortion. Query was proposed to get interested view, but it need clear destination. The method that get a detailed view according to scores evaluated by user interaction can\'t get a suitable initial view. To this, we propose an interactive exploration method that performs unbalanced weighting according to the user's focus and uses the minimum description length principle (MDL) for node aggregation. This method can freely expand the focus area in a good degree of aggregation, which can provide the necessary data information and reduce the visual confusion caused by the excessive data set. This enables efficient data mining in large-scale datasets. We further verify the availability of method by experiment. In addition, the interaction mode in this paper has no conflict with the existing interaction modes (such as zooming, rotating, etc.), and the better results can be achieved by selectively combining according to different scenarios.
	TO cite this article:Tao Fang,Shen Qiwei,Li Wei, et al. Optimizing data drilling with the user interaction weighted minimum description length principle[OL].[13 December 2018] http://en.paper.edu.cn/en_releasepaper/content/4746669


	8. Learning to Query: Deep Learning Based Method for Improving Join Query in Hive
	Lixiang Huang,Tong Xu,WeiLi,Lei Zhang
	Computer Science and Technology 12 December 2018
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	Abstract:Improving join query is crucial for efficiency of processing queries over big data. Hive is a batch-oriented big data management engine that is well suited for data analysis and mining. In this paper, we introduced an innovative method to improve join query in Hive, from the perspective of users. In this solution, users only need to configure the entity attributes, filtering rules and join rules between the entities in the visual operation interface, and our intelligent model can decide the optimal query plan and execute it. We proposed a graph-based SQL generation model to generate Hive-based query plans for complex join query, and use deep learning technique LSTM for query cost prediction so as to pick out the optimal query plan. Moreover, through a comprehensive set of experiments, we demonstrate that our proposed method performs well and significantly improves join query in Hive.
	TO cite this article:Lixiang Huang,Tong Xu,WeiLi, et al. Learning to Query: Deep Learning Based Method for Improving Join Query in Hive[OL].[12 December 2018] http://en.paper.edu.cn/en_releasepaper/content/4746691


	9. Large-scale global optimization using cooperative coevolution with self-adaptive differential grouping
	FANG Wei,FANG Wei,MIN Ruigao,ZHOU Jianhong,ZHOU Jianhong,ZHOU Jianhong
	Computer Science and Technology 05 December 2018
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	Abstract:Cooperative co-evolution (CC) is a popular evolutionary computation approach that can divide a large problem into a set of smaller sub-problems and solve them independently. CC has been an important divide-and-conquer algorithm for large-scale global optimization (LSGO) problems. Identification of variable interactions is the main challenge in CC to decompose the LSGO problems. Differential Grouping (DG) is a competitive variable grouping algorithm that can address the non-separable components of a continuous problem. As an improved version of DG, Global Differential Grouping (GDG) addresses the drawbacks of DG which are variables interactions missing during grouping and grouping performance sensitive to the threshold. In this paper, a Self-adaptive Differential Grouping (SDG) based on GDG is proposed in order to further improve the grouping accuracy on the CEC'2010 LSGO benchmark suite. The threshold for grouping in SDG can adjust adaptively along with the magnitude of different functions and is determined by only two points which is a randomly sampled point and its corresponding opposite point in the decision space. A self-adaptive pyramid allocation (SPA) strategy that can allocate different computational resource to subcomponents is also studied in this paper. The proposed algorithm, where SDG and SPA working with the optimizer $SaNSDE$ (CCSPA-SDG), is used to optimize the CEC'2010 LSGO benchmark suite. Experimental results show that SDG achieved ideal decomposition of the variables for all the CEC'2010 LSGO benchmark functions. The optimization performance of CCSPA-SDG also outperforms the state-of-the-art results.
	TO cite this article:FANG Wei,FANG Wei,MIN Ruigao, et al. Large-scale global optimization using cooperative coevolution with self-adaptive differential grouping[OL].[ 5 December 2018] http://en.paper.edu.cn/en_releasepaper/content/4746608


	10. Approximate CTL model checking
	ZHU Weijun
	Computer Science and Technology 21 June 2018
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	Abstract:The state explosion problem restrict the further applications of Computational Tree LogicCTL model checking. To this end, this study tries to seek an acceptable approximate solution for CTL model checking by introducing the Machine Learning (ML) technique, and a method for predicting results of CTL model checking via the Boosted Tree (BT) algorithm is proposed in this paper. First, for a number of Kripke structures and CTL formulas, a data set A containing model checking results is obtained, using the existing CTL model checking algorithm. Second, the CTL model checking problem can be induced to a binary classification problem of machine learning. As a result, an approximate CTL model checking technique occurs. The experiments show that the new method has the average accuracy of 96%, and its average efficiency is 210 times higher than that of the representative model checking method, if the length of each of CTL formulas equals to 500.These results indicate that the new method can quickly and accurately determine results of CTL model checking for a given Kripke structure and a given long CTL formula since the new method avoid the famous state explosion problem.
	TO cite this article:ZHU Weijun. Approximate CTL model checking[OL].[21 June 2018] http://en.paper.edu.cn/en_releasepaper/content/4745478

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