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7th IEEE International Conference on Data Science in Cyberspace, DSC 2022

作者： Liu, Zhehui Wang, Zhongru Zhang, Yuntao Liu, Tong Fang, Binxing Pang, Zhengyuan Ministry of Education Key Laboratory of Trustworthy Distributed Computing and Service Beijing China Chinese Academy of Cyberspace Studies Beijing China North China University of Technology School of Information Science and Technology Beijing100144 China Beijing DigApis Technology Co. Ltd Beijing100871 China JiangSu DigApis Technology Co. Ltd Jiangsu Nantong226000 China

ISBN: (数字)9781665474801

ISBN: (纸本)9781665474801

Recently, more and more software vulnerabilities are disclosed and researchers tend to study on automatically discover and exploit the vulnerabilities. However, the main challenges of automated exploit generation are: 1) it is hard to analyze the program failure and extract useful information, 2) the scenario of the vulnerability too complex to successfully exploit. Therefore, This paper proposes a vulnerability exploit generation framework AEG-E. AEG-E can extract the control flow graph from the target program and employ the crash reproduce algorithm in symbolic execution to reduce the problem of path explosion. To adapt to complex vulnerability scenarios, we design the extendable and user-configurable exploit model to generate different exploit. Finally, we used the binaries from Robo Hacking Games and real world program to demonstrate the validity and efficiency of AEG-E. The experiment results shows that AEG-E is 2.913 times more efficient than previous exploit generation tool, REX. © 2022 IEEE.

关键词： Flow graphs

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Automated Vulnerability Discovery system Based on Hybrid Execution 7

Automated Vulnerability Discovery System Based on Hybrid Exe...

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7th IEEE International Conference on Data Science in Cyberspace, DSC 2022

作者： Liu, Tong Wang, Zhongru Zhang, Yuntao Liu, Zhehui Fang, Binxing Pang, Zhengyuan Ministry of Education Key Laboratory of Trustworthy Distributed Computing and Service Beijing China Chinese Academy of Cyberspace Studies Beijing China North China University of Technology School of Information Science and Technology Beijing100144 China Beijing DigApis Technology Co. Ltd Beijing100871 China JiangSu DigApis Technology Co. Ltd Jiangsu Nantong226000 China

ISBN: (数字)9781665474801

ISBN: (纸本)9781665474801

At present, program vulnerabilities occur frequently, which seriously threatens the security of cyberspace. Automated vulnerability discovery technology has attracted more and more attention because of its efficiency and universality. In the current field of vulnerability discovery, it is common to combine fuzzing with symbolic execution technology. Symbolic execution technology is often used to solve the input of complex paths and help fuzzing improve the program coverage, so as to detect vulnerabilities better. However, the symbolic execution has no goals in the process of hybrid execution exploration path, it is easy to cause the execution path to deviate from the expected target points. And generating the test case of the corresponding path requires frequently calling the solver for solution, while the current solver has low efficiency and poor solvability for nonlinear operation. In order to solve the above problems, we propose an automated vulnerability discovery system based on hybrid execution. In the system, we propose the symbolic execution guidance algorithm based on dynamic and static combination to guide the symbolic execution to solve the input reaching the target points, so as to avoid exploring in useless paths and avoid consuming a lot of time and computing resources. In addition we hook some nonlinear functions to optimize the nonlinear function constraint solving, so as to improve the hybrid execution efficiency. We have conducted extensive experiments on the RHG 2019 challenge dataset and the RHG 2021 challenge dataset. The experimental results show the effectiveness and scalability of the system. © 2022 IEEE.

关键词： Efficiency

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A Hybrid Routing Mutation Mechanism based on Mutation Cost and Resource Trustworthiness in Network Moving Target Defense 7

A Hybrid Routing Mutation Mechanism based on Mutation Cost a...

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7th IEEE International Conference on Data Science in Cyberspace, DSC 2022

作者： Guo, Kun Gao, Yibo Wang, Dongbin Zhi, Hui Zhang, Tao Lu, Yueming Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing China Beijing University of Posts and Telecommunications Cyberspace Security Research Center Peng Cheng Laboratory Beijing China Beijing University of Posts and Telecommunications National Engineering Laboratory for Mobile Network Beijing China TravelSky Technology Limited Beijing China China Academy of Launch Vehicle Technology Beijing China

ISBN: (数字)9781665474801

ISBN: (纸本)9781665474801

Network moving target defense technology can effectively defend against attacker monitoring of the service. The technology makes it more difficult for attackers to attack and can ensure secure communication for services. However, there may be multiple network attacks in the network. As a result, there may be security problems with the trustworthiness of the network resource mutation space required for network moving target defense technology. The focus of current researches is mostly on the mutation timing and cost optimization of the resource mutation space. It may lead to the possibility that the resources in the resource mutation space have been already under network attacks. In this paper, we propose a hybrid routing mutation mechanism based on mutation cost and resource trustworthiness in SDN networks. The mechanism is able to evaluate the routing mutation space selected by the routing-based network moving target defense technology. In terms of both mutation cost and resource trustworthiness, the optimal weighting ratio of the two factors will be to discovered. Simulation results show that the proposed mechanism can effectively enhance the ability of the routing mutation space to avoid attacked nodes and links and improve the transmission success rate of services. © 2022 IEEE.

关键词： Network routing

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STAD: Joint Spatial-Temporal Dimension and Channel Correlation for Time Series Anomaly Detection

STAD: Joint Spatial-Temporal Dimension and Channel Correlati...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Mingle Zhou Xingli Wang Delong Han Jin Wan Jiachen Li Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China SHANDONG SCICOM Information and Economy Research Institute Co. Ltd

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Accurately identifying real anomalies and pseudo-anomalies in complex multi-dimensional time series data has been a difficult problem in time series anomaly detection. To solve this problem, this paper proposes a new framework, STAD, that joint temporal and spatial dimensions. This framework guides the model to capture the correlation information between channels It also aims to learn the deep feature representation of sequences by mining potential information in the spatialtemporal dimension. It can effectively distinguish between true and false anomalies by comparing information from spatial and temporal dimensions. STAD identifies and integrates correlated channels by using a correlation aggregation mechanism to join multiple channels and detect *** addition, the KAN mixer designed in this paper can effectively extract features from different spatial locations in the spatial-temporal dimension. Through extensive experiments on several public datasets, STAD demonstrates its superiority in terms of accuracy and robustness.

关键词： Correlation Accuracy Time series analysis Signal processing Feature extraction Robustness Acoustics Speech processing Mixers Anomaly detection

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Symbiosis Rather Than Aggregation: Towards Generalized Federated Learning via Model Symbiosis

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IEEE Internet of Things Journal 2025年

作者： Liu, Yuange Liu, Yuru Zhang, Weishan Zheng, Chaoqun Jiang, Wei Luo, Daobin Qiao, Qiao Meng, Lingzhao Zhao, Hongwei Yang, Su College of Computer Science and Technology Qingdao266580 China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan250014 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Jinan250014 China Information Science Academy of China Electronics Technology Group Corporation Beijing100036 China Fudan University School of Computer Science Shanghai200433 China

Federated Learning (FL) faces significant challenges in scenarios with non-IID (non-independent and identically distributed) data distributions across participating clients. Traditional aggregation-based approaches often struggle with the inherent misalignment between local and global optimization objectives, leadingwhich leads to gradient divergence and suboptimal generalization performance. This paper proposes a novel FL framework that replaces conventional aggregation with a biologically inspired model symbiosis approach called FedSym, which employs a dual-level symbiotic mechanism. Ectosymbiosis performs coarse-grained hierarchical parameter recombinations through random layer-wise model combination, while endosymbiosis enables fine-grained intra-layer parameter fusion through weighted averaging, collectively steering model updates towards flatter loss landscapes. Our theoretical analysis demonstrates that FedSym's convergence rate is O(1T) under non-IID conditions, which matches the convergence properties of FedAvg. Extensive experimentsevaluations across multiple datasets and model architectures show that FedSym achieves substantial improvements over state-of-the-art FL methods, particularly in challenging scenarios with high data heterogeneity, and demonstrates robust performance across varying numbers of participating clients and federation scales. © 2014 IEEE.

关键词： Federated learning

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Emotion Recognition Empowered Human-Computer Interaction with Domain Adaptation Network

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IEEE Transactions on Consumer Electronics 2024年

作者： Xu, Xu Fu, Chong Chen, Junxin Northeastern University School of Computer Science and Engineering Shenyang110004 China Dalian University of Technology China The School of Software Dalian116621 China Northeastern University School of Computer Science and Engineering Shenyang110004 China Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110819 China Ministry of Education Engineering Research Center of Security Technology of Complex Network System Shenyang110004 China Dalian University of Technology School of Software Dalian116621 China

Multi-modal emotion recognition plays a vital role in the human-computer interaction (HCI) for consumer electronics. Nowadays, many studies have developed multi-modal fusion algorithms for this ***, two challenging issues remain unsolved, i.e., inefficient multi-modal feature fusion and unclear distance in feature *** this end, we develop a novel framework, namely LAFDA-Net, for cross-subject emotion recognition using EEG and eye movement *** is based on low-rank fusion and domain adaptation *** specifically, the multi-modal signals are input into the feature extraction branch in parallel to generate ***, these features are fused by the low-rank fusion branch, reducing complexity and avoiding ***, the fused features are flattened and sent to the classification branch to determine the emotion *** training, these features are input into the domain adaptive branch to bridge the gap between the source domain and target *** benchmark datasets, i.e., SEED, SEED-IV, and SEED-V, are employed for performance *** results demonstrate that the proposed LAFDA-Net is robust, effective, and has advantages over peer methods. © 1975-2011 IEEE.

关键词： Emotion Recognition

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A Time-Efficient Distributed Constant Conditional Functional Dependency Discovery Algorithm for Data Consistency

A Time-Efficient Distributed Constant Conditional Functional...

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IEEE Annual International Computer Software and Applications Conference (COMPSAC)

作者： Junchao Chen Jian-tao Zhou Xinyu Hao Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Engineering Laboratory for Big Data Analysis Technology College of Computer Science Inner Mongolia University Engineering Research Center of Ecological Big Data Ministry of Education National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot China

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

The accuracy of data analysis depends on data quality, and addressing data consistency issues is a key challenge to improve it. Constant Conditional Functional Dependency (CCFD) is an effective approach that ensures data consistency by enforcing bindings of semantically related values, thus providing quality assurance for data analysis and decision-making processes. However, with the growth of data scale, especially the increasing number of data tuples and attributes, existing single-machine CCFD discovery algorithms face issues of low computational efficiency and lengthy computation time. This paper proposes a time-efficient distributed CCFD discovery algorithm (DCCFD). Through the optimization of data preprocessing and index mapping, the data organization structure is enhanced, laying the foundation for the discovery of CCFDs under distributed conditions. The Spark parallel computing framework is used to partition the dataset, which accelerates the parallel loading and processing of data. Additionally, this algorithm ensures accuracy and processing speed when discovering dependencies by efficiently generating frequent itemsets and verifying CCFDs in parallel. Experiments on multiple real datasets show that, especially with the complex Airline dataset, the DCCFD algorithm not only accurately discovers CCFDs, but also reduces the average running time by 75.64% compared with the preCFDMiner algorithm.

关键词： Accuracy Data preprocessing Software algorithms Distributed databases Parallel processing Software Partitioning algorithms

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Workflow Scheduling Method in Multi-Cluster Environments Using Tasks' Spatiotemporal Attributes

Workflow Scheduling Method in Multi-Cluster Environments Usi...

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Electronic Information Engineering and Computer Communication (EIECC), International Conference on

作者： Yudong Geng Jing Chen Honghua Yan Qingshui Hao Aimin Zhang Tianxiang Zhang Mingchao Ji Tonglin Fu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Shandong Greenthink Electric Power Technology Co. Ltd Jinan China

ISBN: (数字)9798331534622

ISBN: (纸本)9798331534639

Nowadays, computing resources are widely distributed and diverse, and the demand for efficient execution of various workflow tasks on wide-area resources has been raised. multi-objection scheduling method based on space-time attribute of tasks. A scheduling scheme is generated through the three stages of workflow scheduling queue generation, resource selection combined with task spatiotemporal attributes and task and resource matching, and the selected resources of workflow are run according to this scheduling scheme. This method constructs a scheduling problem model to minimize workflow completion time, cost and resource matching degree, and finds the optimal scheduling scheme to improve task execution efficiency. In the process of the experiment, the workflow is actually run in the multi-cluster environment, and the experiment proves that it has a good effect in reducing the completion time and cost of workflow.

关键词： Sequential analysis Costs Processor scheduling Filtering Optimal scheduling Scheduling Spatiotemporal phenomena

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Bag2image: A Multi-Instance Network Traffic Representation for Network Security Event Prediction

SSRN

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SSRN 2024年

作者： Zhang, Jiachen Han, Daoqi Lv, Zhaoxuan Liu, Yang Lu, Yueming Duan, Junke Zhang, Xinyu School of Cyberspace Security Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education China School of Automation Beijing Institute of Technology Beijing China Beijing Institute of Astronautical Systems Engineering Beijing China

In practical scenarios, security events triggered by abnormal network traffic often result from the collective behavior of multiple data streams, embodying group security events with collective characteristics. Existing research methods, focusing on individual data streams, lack a macroscopic analysis and struggle with challenges of analyzing massive, imbalanced data sets. To address these challenges, this paper adopts a multi-instance learning approach, mapping multiple data streams into a bag with a coarse-grained approach, where each bag corresponds to a security event label and each data stream represents an instance. We propose a multi-instance network traffic conversion method, Bag2Image, which transforms temporal multi-instance network traffic data into image representations, preserving the spatio-temporal characteristics of instances within the bag through image channels and pixels. This strategy allows the network security event prediction task to be approached as an image classification problem, leveraging advanced image classification techniques for prediction. Our cross-experiments with six advanced multi-instance learning (MIL) algorithms and six different classification models demonstrate the superior performance of our method on both the UNSW-NB15 dataset and a private dataset. Specifically, our method achieved the highest F1 scores of 77.9% and 74.4% on these datasets, respectively, representing improvements of 4.1% and 13.5% over the second-best MIL algorithm. The recall rates also saw increases of 4.1% and 13.2%, respectively. © 2024, The Authors. All rights reserved.

关键词： Network security

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EventTrojan: Manipulating Non-Intrusive Speech Quality Assessment via Imperceptible Events

EventTrojan: Manipulating Non-Intrusive Speech Quality Asses...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Ying Ren Kailai Shen Zhe Ye Diqun Yan Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China Juphoon System Software Co. Ltd Ningbo China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology (Shandong Academy of Sciences) China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Non-Intrusive speech quality assessment (NISQA) has gained significant attention for predicting speech’s mean opinion score (MOS) without requiring the reference speech. Researchers have gradually started to apply NISQA to various practical scenarios. However, little attention has been paid to the security of NISQA models. Backdoor attacks represent the most serious threat to deep neural networks (DNNs) due to the fact that backdoors possess a very high attack success rate once embedded. However, existing backdoor attacks assume that the attacker actively feeds samples containing triggers into the model during the inference phase. This is not adapted to the specific scenario of NISQA. And current backdoor attacks on regression tasks lack an objective metric to measure the attack performance. To address these issues, we propose a novel backdoor triggering approach (EventTrojan) that utilizes an event during the usage of the NISQA model as a trigger. Moreover, we innovatively provide an objective metric for backdoor attacks on regression tasks. Extensive experiments on four benchmark datasets demonstrate the effectiveness of the EventTrojan attack. Besides, it also has good resistance to several defense methods.

关键词： Measurement Resistance Adaptation models Multimedia systems Current measurement Artificial neural networks Benchmark testing

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