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International Conference on Computational Intelligence and Security

作者： Yang Chen XiaoYong Li Beijing Key Laboratory of Trustworthy Distributed Computing and Service (Beijing University of Posts and Telecommunications) Ministry of Education Beijing China

ISBN: (纸本)9781665404457;9781665447119

Domain Name System (DNS) is a key protocol and service used on the Internet. It is responsible for converting domain names into IP addresses. DNS tunnel is a method of encoding data of other programs or protocols in DNS query and response. Previous studies usually need to extract a large number of features manually and train the classifier of DNS tunnel detection by feature engineering. In this paper, a new framework for DNS tunnel detection is proposed, which can automatically extract features, including long short-term memory (LSTM) language model with attention mechanism and gated recurrent unit (GRU) language model with attention mechanism. Finally, a single-level classifier based on a character-level convolutional neural network (Char-CNN) is proposed. The results show that the LSTM and GRU language models based on attention mechanism and the algorithm of character-level convolution neural network achieve high accuracy and near-zero false positives.

关键词： Protocols Neural networks Training data Detectors Logic gates Feature extraction Classification algorithms

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Multi-level Directed Fuzzing for Detecting Use-after-Free Vulnerabilities

Multi-level Directed Fuzzing for Detecting Use-after-Free Vu...

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IEEE International Conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Yuntao Zhang Zhongru Wang Weiqiang Yu Binxing Fang Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing China Chinese Academy of Cyberspace Studies Beijing China Beijing DigApis Technology Co. Ltd Beijing China

ISBN: (纸本)9781665416597

Greybox fuzzing has been widely used in vulnerabilities detection. Most greybox fuzzing tools are coverage-based, which usually use basic block transition to gain code coverage and focus on improving it to trigger more bugs. However, only increasing code coverage is insufficient to find some heap-based vulnerabilities such as use-after-free (UAF) and double-free (DF). This is because, to trigger these vulnerabilities, one needs not only to cover more code, but also to execute special heap operations to satisfy a particular temporal constraint (i.e., allocating heap memory, free memory, and accessing the heap memory). In this paper, we propose an approach, namely MDFuzz, to detect heap-based vulnerabilities adopting multi-level directed greybox fuzzing. The key idea is identifying different targets to guide the fuzzing process to cover specific heap operations without wasting resources exploring unrelated program components. We first perform a static analysis to automatically recognize three critical targets related to heap operations and then calculate each basic block's distance to the targets. Moreover, we propose a probability-based multi-level seed queue and a novel seed selection strategy to augment the guidance of directed fuzzing. To evaluate MDFuzz, we have performed an evaluation on 7 real-world applications. The experimental results demonstrate that MDFuzz significantly outperforms the state-of-the-art fuzzers, including AFL, AFLFast and VUzzer, in terms of the time consumed to discover heap-based vulnerabilities. Moreover, MD-Fuzz found 4 previously unknown vulnerabilities in real-world programs.

关键词： Privacy Target recognition Conferences Computer bugs Static analysis Binary codes Fuzzing

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Scalable Blind Matching: An Efficient Ciphertext Matching Scheme for Content-Based Pub/Sub Cloud services

Scalable Blind Matching: An Efficient Ciphertext Matching Sc...

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IEEE International Conference on Big Data

作者： Qingling Feng Ruisheng Shi Qifeng Luo Lina Lan Jinqiao Shi Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9781728162515

ISBN: (纸本)9781728162522

Content-based publish/subscribe cloud services are prevailing recently. Confidentiality in publish/subscribe cloud services has become a major concern, especially for applications with sensitive data. Many methods have been proposed to achieve the confidentiality of events and subscriptions. Unfortunately, all these approaches suffer significant performance deterioration while matching on large-scale subscription set. In this paper, we propose an efficient ciphertext matching scheme called SBM. To the best of our knowledge, SBM is the first approach which can preserve confidentiality while at the same time provide scalable event matching. Furthermore, we have integrated SBM with an open source publish/subscribe middleware, PADRES and conducted extensive experiments to evaluate our scheme. The experimental results demonstrate that the matching speed of our solution is faster by two orders of magnitude than its counterparts.

关键词： Degradation Protocols Scalability Big Data Security Indexes Middleware

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An iterative sinogram metal artifact reducdion based on UNet

An iterative sinogram metal artifact reducdion based on UNet

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IEEE International Conference on Big Data

作者： Zichong An Xuemei Zhu Xiangling Fu Junqi Ma Chenyi Guo Zheng Zhang School of Computer Science(National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education Beijing China The University Hospital of Beijing University of Posts and Telecommunications Beijing China Yofo Medical Technology Co. Ltd Beijing China Department of Electronic Enginerring Tsinghua University Beijing China School of Modern Post(School of Automation) Beijing University of Posts and Telecommunications Beijing China

In the practice of dentistry, oral dental CT images are frequently used to assist doctors in diagnosis. Filtered back projection (FBP) technique is widely employed in practice for the reconstruction of CT images obtained from X-ray calculations. However, when metal objects occur in a patient’s oral cavity, the CT images would show density discontinuities due to the metals’ “X-ray absorption coefficient is much larger than human tissues. When the FBP algorithm is applied to CT images with metals, severe metal artifacts would be obtained, which significantly reconstructed images. Therefore, metal artifact reduction (MAR) work is becoming an important problem in dentistry image processing. In this paper, we propose a novel iterative sinogram metal artifact reduction model (IS-MARM) to solve the problem. Inspired by the Diffusion model, we propose a new method to reduce metal artifacts and interpolate new data in sinogram of dentistry images iteratively. This approach reduces the difficulty of model learning and achieves good results. Secondly, we proposed a new simple method of iterative data generating to simulate real-world metals in CT sinogram images. Finally, we have demonstrated the effectiveness of our method through experiments on dental CT MAR work.

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An Efficient Trust Inference Algorithm with Local Weighted Centrality for Social Recommendation

An Efficient Trust Inference Algorithm with Local Weighted C...

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IEEE International Conference on Communications (ICC)

作者： Binsi Cai Xiaoyong Li Yali Gao Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications Beijing P.R.China

ISBN: (数字)9781728150895

ISBN: (纸本)9781728150901

The integration of trust system and recommendation system is a new hot spot in current research. Trust relationship has be exploited in social recommendation, which can effectively solve the problems of low recommendation quality, sparse data and cold start in the traditional recommendation system. Meanwhile, trust inference in social relations is necessary in completing trust information and expanding social recommendation knowledge base. In this work, we propose a new trust inference algorithm LWCTrust to improve the efficiency and accuracy of social recommendation. Firstly, we construct a local weighted centrality (LWC) metric based on the user's degree centrality and trust information, and propose a new adaptive breadth-first search algorithm. Then, based on the property of path decay, we compare two different trust decay strategies. In addition, considering inconsistencies and conflicts in trust opinion, we apply LWC metric to multi-path aggregation step and present a OWA dynamic aggregation strategy. A number of experiments are conducted on the real social network dataset Advogato, and the results validate the great performance of LWCTrust. Our work is the first to construct an efficient LWC metric using social graph trust information, and we explore the effect of attenuation functions on accuracy in path propagation.

关键词： Measurement Inference algorithms Reliability Heuristic algorithms Dictionaries Social network services Mathematical model

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Rigorous explanation of inference on probabilistic graphical models

arXiv

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arXiv 2020年

作者： Liu, Yifei Chen, Chao Zhang, Xi Xie, Sihong Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Bupt Beijing China Lehigh University BethlehemPA United States

Probabilistic graphical models, such as Markov random fields (MRF), exploit dependencies among random variables to model a rich family of joint probability distributions. Sophisticated infer-ence algorithms, such as belief propagation (BP), can effectively compute the marginal posteriors. Nonetheless, it is still difficult to interpret the infer-ence outcomes for important human decision mak-ing. There is no existing method to rigorously attribute the inference outcomes to the contributing factors of the graphical models. Shapley values provide an axiomatic framework, but naively computing or even approximating the values on general graphical models is challenging and less stud-ied. We propose GraphShapley to integrate the de-composability of Shapley values, the structure of MRFs, and the iterative nature of BP inference in a principled way for fast Shapley value compu-tation, that 1) systematically enumerates the im-portant contributions to the Shapley values of the explaining variables without duplicate;2) incre-mentally compute the contributions without start-ing from scratches. We theoretically character-ize GraphShapley regarding independence, equal contribution, and additivity. On nine graphs, we demonstrate that GraphShapley provides sensible and practical explanations. Copyright © 2020, The Authors. All rights reserved.

关键词： Graphic methods

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Base station network traffic prediction approach based on LMA-DeepAR

arXiv

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arXiv 2021年

作者： Zhang, Jiachen Zuo, Xingquan Xu, Mingying Han, Jing Zhang, Baisheng Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing Uinversity of Posts and Telecommunications Beijing China Key Laboratory of Intelligent Telecommunication Software and Multimedia Beijing Uinversity of Posts and Telecommunications Beijing China Network Interface Virtualization Shanghai System Design Deptment Zhongxing Telecommunication Equipment Corporation Shanghai China

Accurate network traffic prediction of base station cell is very vital for the expansion and reduction of wireless devices in base station cell. The burst and uncertainty of base station cell network traffic makes the network traffic nonlinear and non-stationary, which brings challenges to the long-term prediction of network traffic. In this paper, the traffic model LMA-DeepAR for base station network is established based on DeepAR. Acordding to the distribution characteristics of network traffic, this paper proposes an artificial feature sequence calculation method based on local moving average (LMA). The feature sequence is input into DeepAR as covariant, which makes the statistical characteristics of network traffic near a period of time in the past be considered when updating parameters, and the interference of non-stationary network traffic on model training will be reduced. Experimental results show that the proposed prediction approach (LMA-DeepAR) outperforms other methods in the overall long-term prediction performance and stability of multi cell network traffic. Copyright © 2021, The Authors. All rights reserved.

关键词： Base stations

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Deep reinforcement learning based dynamic optimization of bus timetable

arXiv

引用

arXiv 2021年

作者： Ai, Guanqun Zuo, Xingquan Chen, Gang Wu, Binglin School of Computer Science Beijing University of Posts and Telecommunications Beijing100876 China Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing100876 China School of Engineering and Computer Science Victoria University of Wellington Wellington6140 New Zealand

Bus timetable optimization is a key issue to reduce operational cost of bus companies and improve the service quality. Existing methods use exact or heuristic algorithms to optimize the timetable in an offline manner. In practice, the passenger flow may change significantly over time. Timetables determined in offline cannot adjust the departure interval to satisfy the changed passenger flow. Aiming at improving the online performance of bus timetable, we propose a Deep Reinforcement Learning based bus Timetable dynamic Optimization method (DRL-TO). In this method, the timetable optimization is considered as a sequential decision problem. A Deep Q-Network (DQN) is employed as the decision model to determine whether to dispatch a bus service during each minute of the service period. Therefore, the departure intervals of bus services are determined in real time in accordance with passenger demand. We identify several new and useful state features for the DQN, including the load factor, carrying capacity utilization rate, and the number of stranding passengers. Taking into account both the interests of the bus company and passengers, a reward function is designed, which includes the indicators of full load rate, empty load rate, passengers’ waiting time, and the number of stranding passengers. Building on an existing method for calculating the carrying capacity, we develop a new technique to enhance the matching degree at each bus station. Experiments demonstrate that compared with the timetable generated by the state-of-the-art bus timetable optimization approach based on a memetic algorithm (BTOA-MA), Genetic Algorithm (GA) and the manual method, DRL-TO can dynamically determine the departure intervals based on the real-time passenger flow, saving 8% of vehicles and reducing 17% of passengers’ waiting time on average. Copyright © 2021, The Authors. All rights reserved.

关键词： Buses

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Reinforcement Learning Based Antenna Selection in User-Centric Massive MIMO 91

Reinforcement Learning Based Antenna Selection in User-Centr...

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91st IEEE Vehicular Technology Conference, VTC Spring 2020

作者： Chai, Xinxin Gao, Hui Sun, Ji Su, Xin Lv, Tiejun Zeng, Jie Beijing University of Posts and Telecommunications Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing100876 China Institute of Telecommunications Ministry of Industry and Information Technology Beijing100804 China Tsinghua University Beijing National Research Center for Information Science and Technology Beijing100091 China

ISBN: (纸本)9781728152073

In this paper, we consider a user-centric massive multiple-input multiple-output (UC-MMIMO) system, wherein the optimal antenna selection (AS) is very complicated, because of the huge number of deployed antennas. Traditional AS algorithms rely heavily on full and perfect channel state information (CSI). Thus, we propose a novel AS algorithm to achieve low-complexity and less CSI reliance for UC-MMIMO. The proposed AS algorithm consists of the selection stage and the adjustment stage. In the selection stage, antennas are selected by a reinforcement learning (RL) based algorithm in which input data are the locations of users. In the adjustment stage, an adjustment mechanism is designed to further improve the performance. Numerical results show that our algorithm achieves better performance with lower complexity compared with related traditional algorithms. © 2020 IEEE.

关键词： MIMO systems

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Dynamic Multichannel Access for 5G and Beyond with Fast Time-Varying Channel

Dynamic Multichannel Access for 5G and Beyond with Fast Time...

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IEEE International Conference on Communications (ICC)

作者： Shaoyang Wang Tiejun Lv Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9781728150895

ISBN: (纸本)9781728150901

In 5G and beyond wireless communication systems, providing satisfactory service to users in high-mobility scenarios becomes very essential. Unstable service supply caused by frequent handover under high-mobility compromises the service experience of users. Moreover, the demanding requirements for processing delays and non-immediate information processing errors are particularly prominent. This paper proposes a novel learning-based approach to solving the dynamic multichannel access (DMCA) problem under fast time-varying channel arising from high-mobility. Specifically, we first propose the subjective experience-based quality of service, and formulate the corresponding optimization problem based on the designed access criterion. Invoking our proposed prediction-based deep deterministic policy gradient algorithm and incremental learning-based online channel prediction model, a novel DMCA scheme, which combines the recurrent neural network with deep reinforcement learning, is proposed. Corroborated by experiments built in real channel data, the performance of proposed learning-based DMCA scheme approaches that derived from the exhaustive search method when making a decision at each time-slot, and is superior to the exhaustive search method when making a decision for every few time-slots. Furthermore, our scheme significantly reduces processing delays and effectively alleviates errors caused by the non-immediacy of information acquisition and processing.

关键词： Delays Quality of service Time-varying channels 5G mobile communication Predictive models Wireless communication Optimization

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