Chao Chen

Zhejiang Gongshang University

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Baoming Bai

Xidian University

Rui Yin

Hangzhou City University

Xiaohan Yu

University of Chinese Academy of Sciences

Chuanhuang Li

Zhejiang Gongshang University

Seung Jun Baek

Korea University

Xinmei Wang

Tianjin Central Hospital of Gynecology Obstetrics

Zesheng Chen

University of Cambridge

Zesheng Chen

Hangzhou Center for Disease Control and Prevention

Celimuge Wu

University of Electro-Communications

Qian Wang

Wuhan University

Cooperative Institutions

Chinese Academy of Sciences

Beijing University of Posts and Telecommunications

Zhejiang University

Xidian University

Zhejiang Gongshang University

Shanghai Jiao Tong University

University of Science and Technology of China

University of Chinese Academy of Sciences

National University of Defense Technology

Beihang University

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Anomaly-Based Insider Threat Detection Using Deep Autoencoders

2022 IEEE International Conference on Data Mining Workshops (ICDMW) (2018)

Liu Liu Olivier De Vel Chao Chen Jun Zhang Yang Xiang

In recent years, the malicious insider threat has become one of the most significant cyber security threats that an organisation can be subject to. Due to an insider's natural ability to evade deployed information security mechanisms such as firewalls and endpoint protections, the detection of an insider threat can be challenging. Moreover, compared to the volume of audit data that an organization collects for the purpose of intrusion/anomaly detection, the digital footprint left by a malicious insider's action can be minuscule. To detect insider threats from large and complex audit data, in this paper, we propose a detection system that implements anomaly detection using an ensemble of deep autoencoders. Each autoencoder in the ensemble is trained using a certain category of audit data, which represents a user's normal behaviour accurately. The reconstruction error obtained between the original and the decoded data is used to measure whether any behaviour is anomalous or not. After the data has been processed by the individually trained autoencoders and the respective reconstruction errors obtained, a joint decision-making mechanism is used to report a user's overall maliciousness score. Numerical experiments are conducted using a benchmark dataset for insider threat detection. Results indicate that the proposed detection system is able to detect all of the malicious insider actions with a reasonable false positive rate.

Insider threat

Benchmark (surveying)

10.1109/icdmw.2018.00014

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Citations (53)

A Novel Truth Discovery Approach for Health Recommendation Systems

IEEE Transactions on Consumer Electronics (2023)

Xiaohan Yu Zhongxin Shao Keming Dong Chao Chen

The truth discovery problem involves identifying the truth associated with objects from observations, which is crucial in health recommendation systems. While many methods effectively calculate the truth from multiple data sources, few address the challenges posed by dynamically generated data. In dynamic environments, data continually arrive and require processing in time due to the limited memory of health monitoring devices. In this paper, we propose a novel approach to discovering truths for health recommendation systems that uses time-series analysis methods to mine the evolutionary patterns of truths and account for potential similarities between objects. Our approach efficiently manages dynamic data without compromising estimation accuracy. We evaluate the performance of our proposed framework on two real-world datasets and a synthetic dataset, demonstrating its superiority over state-of-the-art methods in terms of estimation accuracy and efficiency. This approach has great potential to advance the development of more accurate and personalized health recommendation systems.

Ground truth

10.1109/tce.2023.3322869

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Fresh seafood delivery routing problem using an improved ant colony optimization

Annals of Operations Research (2017)

Baozhen Yao Chao Chen Xiaolin Song Xiaoli Yang

Vehicle Routing Problem

Benchmark (surveying)

Ant colony

10.1007/s10479-017-2531-2

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Citations (37)

Improved ADMM Penalized Decoder for Irregular Low-Density Parity-Check Codes

IEEE Communications Letters (2015)

Xiaopeng Jiao Haoyuan Wei Jianjun Mu Chao Chen

Based on linear programming (LP) decoding with alternating direction method of multipliers (ADMM), Liu et al. proposed an ADMM penalized decoder for low-density parity-check (LDPC) codes that can improve the error rate performance at low signal-to-noise ratios. In this letter, we propose a new ADMM penalized decoder to improve the error rate performance further for irregular LDPC codes. The proposed decoder modifies the penalty term of the objective function by assigning different penalty parameters for different variable node degrees. Simulations over three irregular LDPC codes show that the proposed decoder performs considerably better than the ADMM penalized decoder.

Soft-decision decoder

10.1109/lcomm.2015.2421445

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Citations (45)

Optimal Edge Nodes Deployment with Multi Association for Smart Health

IEEE Transactions on Molecular Biological and Multi-Scale Communications (2021)

Chao Chen Peng Li Xin Jian Langyun Wu Jun Yang

The further evolution and integration of internet of things (IoT) and artificial intelligence (AI) technologies would transform traditional health care processes into smart health care services, which leverages various wireless sensors, cameras, and controllers to collect multi-dimensional medical information and analyze them intelligently. In case of this, the massive amount of data generated by smart health devices need to be transferred, processed and stored quickly, which poses great challenges to traditional cloud-based communications with single association mechanism. To address this problem, this paper proposes an innovative multi-access edge computing based access network with multi-association mechanism, where a single smart device could associate and communicate with multiple edge nodes. The deployment of edge nodes for proposed access network is formulated as an extended knapsack problem, which considers not only signal to interference plus noise rate (SINR) requirement of devices, but also power constraint and load balance of edge nodes and it is solved by the simulated annealing (SA) algorithm. Numerical results via SA algorithm show that the proposed multi-association mechanism can improve the SINR of devices significantly, while achieving better load balancing among edge nodes. All these together provides good reference to build an efficient access network for future smart healthcare system.

Edge device

10.1109/tmbmc.2021.3118951

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Citations (2)

Local Queueing-Based Data-Driven Task Scheduling for Multicore Systems

Meng Li Chao Chen Guchuan Zhu Yvon Savaria

Nowadays, multicore systems are widely used in high performance computing. Many algorithms have been proposed to enhance the system performance by load balancing or concurrent scheduling to reduce the execution time of applications. However, task scheduling on multicore systems is still an open issue, which needs to be analyzed to fully utilize the processing capacity and achieve low processing latencies. In order to tackle the inefficient utilization of CPU cores, a queueing-based data-driven task scheduling scheme, which focuses on local parallel computing, is introduced in this paper. In this scheduling scheme, multi-queue management is proposed for dynamic task scheduling to target a full utilization of local CPU cores when input tasks can keep them fully used. Furthermore, the preemption technique is applied to guarantee that high priority tasks will not be blocked by low priority tasks. Our solution can be combined with other algorithms taking into account earliest finish time or critical path to generate better results. Thus CPU core utilization can be improved while minimizing the makespan of high priority DAGs. Finally, simulations are carried out to verify the proposed task scheduling scheme. The reported results confirm its viability and efficiency.

Multi-core processor

Preemption

Fixed-priority pre-emptive scheduling

Critical path method

10.1109/mwscas.2018.8623930

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Frequent Items Mining Algorithm Over High Speed Network Flows Based on Double Hash Method

International Journal of Future Generation Communication and Networking (2016)

Lei Bai Chao Chen

In the high-speed backbone network, with the increasing speed of network link, the number of network flows increase rapidly.Meanwhile, with restrictions on hardware computing and storage resources, so, how to identify and measure large flows timely and accurately in massive data become a hot issue in high speed network flow measurement area.In this paper, we propose a new algorithm based on double hash algorithm to realize large flow frequent items identification, according to the defect of MF algorithm which produces false positive easily and frequent updates to bring the huge pressure to the system.The complexity and false positive rate of the algorithm was analyzed.The effect of large flow frequent items statistical accuracy and discard rate for parameter configuration was analyzed through simulation.The theoretical analysis and the simulation result indicate that compare to MF algorithm, our algorithm can identify large flow frequent items more accurately, and satisfies the need of actual measurement.

10.14257/ijfgcn.2016.9.5.08

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A Fast Decoding Algorithm for Generalized Reed-Solomon Codes and Alternant Codes

arXiv (Cornell University) (2025)

Nianqi Tang Yunghsiang S. Han D. Y. Pei Chao Chen

In this paper, it is shown that the syndromes of generalized Reed-Solomon (GRS) codes and alternant codes can be characterized in terms of inverse fast Fourier transform, regardless of code definitions. Then a fast decoding algorithm is proposed, which has a computational complexity of $O(n\log(n-k) + (n-k)\log^2(n-k))$ for all $(n,k)$ GRS codes and $(n,k)$ alternant codes. Particularly, this provides a new decoding method for Goppa codes, which is an important subclass of alternant codes. When decoding the binary Goppa code with length $8192$ and correction capability $128$, the new algorithm is nearly 10 times faster than traditional methods. The decoding algorithm is suitable for the McEliece cryptosystem, which is a candidate for post-quantum cryptography techniques.

Reed–Solomon error correction

Berlekamp–Welch algorithm

BCJR algorithm

10.48550/arxiv.2502.02356

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Design of Quasi-Cyclic Cycle LDPC Codes over GF(q)

IEICE Transactions on Communications (2012)

HU Shu-kai Chao Chen Rong Sun Xinmei Wang

Quasi-cyclic (QC) low-density parity-check (LDPC) codes have several appealing properties regarding decoding, storage requirements and encoding aspects. In this paper, we focus on the QC LDPC codes over GF(q) whose parity-check matrices have fixed column weight j=2. By investigating two subgraphs in the Tanner graphs of the corresponding base matrices, we derive two upper bounds on the minimum Hamming distance for this class of codes. In addition, a method is proposed to construct QC LDPC codes over GF(q), which have good Hamming distance distributions. Simulations show that our designed codes have good performance.

Hamming distance

Forward error correction

Hamming weight

Hamming graph

Raptor code

Tornado code

10.1587/transcom.e95.b.983

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Electro-Photonic NoC Designs for Kilocore Systems

ACM Journal on Emerging Technologies in Computing Systems (2016)

José Luis Abellán Chao Chen Ajay Joshi

The increasing core count in manycore systems requires a corresponding large Network-on-chip (NoC) bandwidth to support the overlying applications. However, it is not possible to provide this large bandwidth in an energy-efficient manner using electrical link technology. To overcome this issue, photonic link technology has been proposed as a replacement. This work explores the limits and opportunities for using photonic links to design the NoC architecture for a future Kilocore system. Three different NoC designs are explored: ElecNoC, an electrical concentrated two-dimensional- (2D) mesh NoC; HybNoC, an electrical concentrated 2D mesh with a photonic multi-crossbar NoC; and PhotoNoC, a photonic multi-bus NoC. We consider both private and shared cache architectures and, to leverage the large bandwidth density of photonic links, we investigate the use of prefetching and aggressive non-blocking caches. Our analysis using contemporary Big Data workloads shows that the non-blocking caches with a shared LLC can best leverage the large bandwidth of the photonic links in the Kilocore system. Moreover, compared to ElecNoC-based and HybNoC-based Kilocore systems, a PhotoNoC-based Kilocore system achieves up to 2.5× and 1.5× better performance, respectively, and can support up to 2.1× and 1.1× higher bandwidth, respectively, while dissipating comparable power in the overall system.

Leverage (statistics)

10.1145/2967614

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Citations (12)