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arXiv 2022年

作者： Liu, Yang Fan, Mingyuan Chen, Cen Liu, Ximeng Ma, Zhuo Wang, Li Ma, Jianfeng Xi'An China Shaanxi Key Laboratory of Network and System Security Xidian University Xi'An China College of Computer and Data Science Fuzhou University Fuzhou China School of Data Science and Engineering East China Normal University Shanghai China Ant Group Hangzhou China

Backdoor injection attack is an emerging threat to the security of neural networks, however, there still exist limited effective defense methods against the attack. In this paper, we propose BAERASER, a novel method that can erase the backdoor injected into the victim model through machine unlearning. Specifically, BAERASER mainly implements backdoor defense in two key steps. First, trigger pattern recovery is conducted to extract the trigger patterns infected by the victim model. Here, the trigger pattern recovery problem is equivalent to the one of extracting an unknown noise distribution from the victim model, which can be easily resolved by the entropy maximization based generative model. Subsequently, BAERASER leverages these recovered trigger patterns to reverse the backdoor injection procedure and induce the victim model to erase the polluted memories through a newly designed gradient ascent based machine unlearning method. Compared with the previous machine unlearning solutions, the proposed approach gets rid of the reliance on the full access to training data for retraining and shows higher effectiveness on backdoor erasing than existing fine-tuning or pruning methods. Moreover, experiments show that BAERASER can averagely lower the attack success rates of three kinds of state-of-the-art backdoor attacks by 99% on four benchmark datasets. © 2022, CC BY-NC-ND.

关键词： network security

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Fault Diagnosis for Rolling Bearing Based on RF-LSTM

Fault Diagnosis for Rolling Bearing Based on RF-LSTM

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Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), CAA Symposium on

作者： Qiuting Li Xiuqing Wang Yunpeng Yang Ruiyi Wang Feng Lv College of Computer and Cyber Security Hebei Normal University Shijiazhuang China Hebei Provincial Key Laboratory of Network & Information Security Shijiazhuang China Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics & Data Security Shijiazhuang China

ISBN: (纸本)9781665401166

In the era of big data, it is of great significance to study data-driven fault diagnostic methods for equipment in complex industrial environments. In this paper, a data-driven fault diagnostic method for rolling bearing based on RF-LSTM is proposed, which combines random forest (RF) algorithm with long and short term memory (LSTM) networks. RF is used to select sensor data’s features, which have great influence on rolling bearing fault diagnosis, and LSTM is used for prediction rolling bearing’s working states. The proposed method is used for fault diagnosis of the Case Western Reserve University (CWRU) bearing dataset and the experimental results validated the effectiveness of the proposed RF-LSTM model. Experimental results also show that the performance of the RFLSTM model is better than that of RF, LSTM, RF-RF, PCA-RF, PCA-LSTM models.

关键词： Fault diagnosis Radio frequency Fault detection Time series analysis Rolling bearings Forestry Predictive models

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Fault-Tolerant Differential Privacy Routing of Human-Cyber-Physical Fusion Systems for Large Language Models security

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IEEE Internet of Things Journal 2025年

作者： Lin, Limei Huang, Yanze Wang, Xiaoding Garg, Sahil Moussa, Sherif Alrashoud, Mubarak Fujian Normal University College of Computer and Cyber Security Fujian Provincial Key Laboratory of Network Security and Cryptology Fuzhou350117 China Fujian University of Technology School of Computer Science and Mathematics Fuzhou350118 China École de technologie supérieure Electrical Engineering Department MontréalQCH3C 1K3 Canada Canadian University School of Engineering Applied Science and Technology Dubai United Arab Emirates Chitkara University Institute of Engineering and Technology Chitkara University Centre for Research Impact and Outcome Punjab Rajpura140401 India Canadian University School of Engineering Applied Science and Technology Dubai United Arab Emirates Riyadh11543 Saudi Arabia

The rapid proliferation of Internet of Things (IoT) systems has introduced complex networks of interconnected devices, computational resources, and web-based communication infrastructure. Privacy protection in IoT data routing is critical to enabling secure deployment of large language models (LLMs) for processing distributed sensor data, user queries, and device-generated content. However, IoT environments inherently involve heterogeneous devices, dynamic network topologies, and resource-constrained nodes, complicating the design of privacy-preserving routing mechanisms that simultaneously ensure reliability across diverse communication layers. To address these challenges, we propose an innovative FtPR (Fault-tolerant Privacy Routing) model based on secure multiparty computing mechanism, which enables secure and efficient data fusion and transmission in IoT networks. FtPR establishes a novel connection between IoT device clusters and data center network architecture AQDNn routers, leveraging the hierarchical architecture of AQDNn to construct completely independent spanning trees (CIST). By exploiting the non-overlapping paths between nodes in distinct CISTs, FtPR achieves fault-tolerant routing while maintaining privacy guarantees. Building on this framework, we introduce a secure multiparty computing mechanism to perturb link weights in the AQDNn. This ensures that link weights across different CISTs adhere to constrained ranges, preventing adversarial inference of routing paths. Each node operates with localized knowledge of its connected link weights, eliminating the need for global network visibility. Consequently, even if malicious actors compromise one or multiple nodes, they cannot reconstruct end-to-end communication paths, thereby preserving route anonymity. Experimental results demonstrate that FtPR improves IoT network performance and security, reducing misclassification rates and marginal release score compared to state-of-the-art methods. © 2014 IEEE.

关键词： Anonymity

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High-Dimensional Quantum Secure Multiparty Summation with Random Decoy Particles

SSRN

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SSRN 2022年

作者： Song, Xiuli Yang, Shuai Deng, Hongyao Liao, Jinwei Wu, Tao School of Cyber Security and Information Law Chongqing University of Posts and Telecommunications Chongqing400065 China Henan Key Laboratory of Network Cryptography Technology Henan450001 China School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China College of Computer Engineering Yangtze Normal University Chongqing408100 China

Quantum secure multiparty summation (QSMS) calculates the sum of the privacy inputs of multiple participants without exposing these inputs. The current QSMS protocols mostly employ the fixed bases of decoy particles to enhance the security of information transmission. Unfortunately, this measure increases the risk of eavesdropper guessing the fixed bases. In this paper, a high-dimensional quantum secure multiparty summation with random decoy particles (HQSMS-RDP) is proposed to remedy the above-mentioned security deficiencies. In HQSMS-RDP, each participant performs the general Pauli operator on the received message particle to embed his own secret integer and random number into it, and then sends the transformed message particle and random decoy particles to the next participant. Moreover, each participant needs to neither prepare the decoy particles nor measure them during every transmission route. These decoy particles are chosen from a set of random single particles, so that an eavesdropper cannot deterministically guess the specific measurement bases for measuring them, resulting in the exposure probability of the eavesdropper eavesdropping on them approaching 1. The performance analysis shows that the resource consumption of HQSMS-RDP is lower than that of other similar QSMS protocols. The results simulated on the IBM cloud platform prove the correctness of HQSMS-RDP. © 2022, The Authors. All rights reserved.

关键词： security of data

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Shellshock Bash Vulnerability Modeling Analysis Based on Petri Net

Shellshock Bash Vulnerability Modeling Analysis Based on Pet...

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International Conference on networking and network Applications (NaNA)

作者： Liumei Zhang Xi Deng Yichuan Wang School of Computer Science Xi’an Shiyou University Xi’an China School of Computer Science and Engineering Xi’an University of Technology Xi’an China Shaanxi Key Laboratory for Network Computing and Security Technolog Xi’an China

The enhancement of Internet connectivity and the increase of information transmission speed yield the increasing frequency of network attacks. The Shellshock attackers often exploit bash vulnerability to read the content behind the function definition when importing environment variable functions. Then, malicious scripts can be executed in systems and servers, which compromises everything. Therefore, this paper proposes a formal modeling analysis method for the Shellshock Bash basis and automates the analysis of the patched position of the model. The relationship between the established model and the actual attack process is also discussed, which is a feasible reference for exploring unknown vulnerabilities and the location of corresponding patches.

关键词： Analytical models Firing Petri nets Information processing Tools Software Internet

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VAE-GANMDA: A microbe-drug association prediction model integrating variational autoencoders and generative adversarial networks

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Artificial Intelligence in Medicine 2025年 167卷

作者： Bo Wang Yang He Xiaoxin Du Lei Zhu Junqi Wang Tongxuan Wang School of Computer and Control Engineering Qiqihar University Qiqihar 161006 China Qiqihar University Heilongjiang Key Laboratory of Big Data Network Security Detection and Analysis Qiqihar 161006 China

Traditional biological experimental methods typically require weeks or even months of experimentation, and the cost of each experiment can reach hundreds or even thousands of dollars, which is quite expensive and time-consuming. To address this, a model called VAE-GANMDA, which integrates variational autoencoders (VAE) and generative adversarial networks (GAN) for predicting microbe-drug associations, has been proposed. Firstly, a heterogeneous network of microbes and drugs is established to enrich the association information. Secondly, by fusing VAE and GAN, the model learns the manifold distribution of data through association features, obtaining nonlinear manifold features. Furthermore, the VAE generation module is improved by integrating the Convolutional Block Attention Module (CBAM) and Gaussian kernel function, enhancing the smooth perception of manifold features, thus endowing VAE with stronger feature extraction capabilities. Then, singular value decomposition (SVD) technique is employed to extract linear features of the data. Finally, by combining linear and nonlinear features, the k-means++ algorithm is used to select balanced and high-quality negative samples for training the MLP classifier. Through performance evaluation, the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC) of VAE-GANMDA reach 0.9724 and 0.9635 respectively, outperforming classical machine learning methods and the majority of deep learning methods. Case studies demonstrate that VAE-GANMDA accurately predicts candidate drugs related to SARS-CoV-2 and candidate microbes related to ciprofloxacin.

关键词： Association prediction CBAM Generative adversarial network Microbes and drugs Variational autoencoder k-means++

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Semisupervised Encrypted Traffic Identification Based on Auxiliary Classification Generative Adversarial network

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computer Systems Science & engineering 2021年第12期39卷 373-390页

作者： Jiaming Mao Mingming Zhang Mu Chen Lu Chen Fei Xia Lei Fan ZiXuan Wang Wenbing Zhao State Grid Jiangsu Electric Power Co. Ltd.Information and Telecommunication BranchNanJing210003China Institute of Information and Communication Global Energy Interconnection Research InstituteState Grid Key Laboratory of Information and Network SecurityNanjing210003China Engineering Research Center of Post Big Data Technology and Application of Jiangsu Province Research and Development Center of Post Industry Technology of the State Posts Bureau(Internet of Things Technology)Broadband Wireless Communication Technology Engineering Research Center of the Ministry of EducationNanjing University of Posts and TelecommunicationsNanjing210003China Department of Electrical Engineering and Computer Science Cleveland State UniversityCleveland44115USA

The rapidly increasing popularity of mobile devices has changed the methods with which people access various network services and increased net-work traffic *** the past few decades,network traffic identification has been a research hotspot in the field of network management and security ***,as more network services use encryption technology,network traffic identification faces many *** classic machine learning methods can solve many problems that cannot be solved by port-and payload-based methods,manually extract features that are frequently updated is time-consuming and *** learning has good automatic feature learning capabilities and is an ideal method for network traffic identification,particularly encrypted traffic identification;Existing recognition methods based on deep learning primarily use supervised learning methods and rely on many labeled ***,in real scenarios,labeled samples are often difficult to *** paper adjusts the structure of the auxiliary classification generation adversarial network(ACGAN)so that it can use unlabeled samples for training,and use the wasserstein distance instead of the original cross entropy as the loss function to achieve semisupervised *** results show that the identification accuracy of ISCX and USTC data sets using the proposed method yields markedly better performance when the number of labeled samples is small compared to that of convolutional neural network(CNN)based classifier.

关键词： Encrypted traffic recognition deep learning generative adversarial network traffic classification semisupervised learning

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Cross-heterogeneous Domain Authentication Scheme Based on Blockchain

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Journal of Artificial Intelligence and Technology 2021年第2期1卷 92-100页

作者： Jing Liu Yixin Liu Yingxu Lai Rongchen Li Siyu Wu Sami Mian College of Computer Science Faculty of Information TechnologyBeijing University of TechnologyBeijing 100124China Shaanxi Key Laboratory of Network and System Security Xi’an 710071China Science and Technology on Information Assurance Laboratory Beijing 100072China Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of EducationBeijing 100124China Fan Gongxiu Honors College Beijing University of TechnologyBeijing 100124China University of Pittsburgh PittsburghPA 15260USA

With the rising popularity of the Internet and the development of big data technology,an increasing number of organizations are opting to cooperate across domains to maximize their *** organizations use public key infrastructure to ensure security in accessing their data and ***,with the continuous development of identity-based encryption(IBE)technology,small-and medium-sized enterprises are increasingly using IBE to deploy internal authentication *** solve the problems that arise when crossing heterogeneous authentication domains and to guarantee the security of the certification process,we propose using blockchain technology to establish a reliable cross-domain authentication *** the distributed and tamper-resistant characteristics of the blockchain,we design a cross-domain authentication model based on blockchain to guarantee the security of the heterogeneous authentication process and present a cross-domain authentication protocol based on *** model does not change the internal trust structure of each authentication domain and is highly ***,on the premise of ensuring security,the process of verifying the signature of the root certificate in the traditional cross-domain authentication protocol is improved to verify the hash value of the root certificate,thereby improving the authentication *** developed prototype exhibits generality and simplicity compared to previous methods.

关键词： public key infrastructure identity-based encryption cross-domain authentication blockchain

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SS-GNN: A Simple-Structured Graph Neural network for Affinity Prediction

arXiv

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arXiv 2022年

作者： Zhang, Shuke Jin, Yanzhao Liu, Tianmeng Wang, Qi Zhang, Zhaohui Zhao, Shuliang Shan, Bo Software College Hebei Normal University Shijiazhuang050024 China Shijiazhuang Xianyu Digital Biotechnology Co. Ltd Shijiazhuang050024 China College of Computer and Cyber Security Hebei Normal University Shijiazhuang050024 China Hebei Provincial Key Laboratory of Network and Information Security Shijiazhuang050024 China Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics & Data Security Shijiazhuang050024 China

Efficient and effective drug-target binding affinity (DTBA) prediction is a challenging task due to the limited computational resources in practical applications and is a crucial basis for drug screening. Inspired by the good representation ability of graph neural networks (GNNs), we propose a simple-structured GNN model named SS-GNN to accurately predict DTBA. By constructing a single undirected graph based on a distance threshold to represent protein-ligand interactions, the scale of the graph data is greatly reduced. Moreover, ignoring covalent bonds in the protein further reduces the computational cost of the model. The GNN-MLP module takes the latent feature extraction of atoms and edges in the graph as two mutually independent processes. We also develop an edge-based atom-pair feature aggregation method to represent complex interactions and a graph pooling-based method to predict the binding affinity of the complex. We achieve state-of-the-art prediction performance using a simple model (with only 0.6M parameters) without introducing complicated geometric feature descriptions. SS-GNN achieves Pearson's Rp=0.853 on the PDBbind v2016 core set, outperforming state-of-the-art GNN-based methods by 5.2%. Moreover, the simplified model structure and concise data processing procedure improve the prediction efficiency of the model. For a typical protein-ligand complex, affinity prediction takes only 0.2 ms. All codes are freely accessible at https://***/xianyuco/SS-GNN. © 2022, CC BY-NC-SA.

关键词： Forecasting

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Accurate and Fast Detection of DDoS Attacks in High-Speed network with Asymmetric Routing

Accurate and Fast Detection of DDoS Attacks in High-Speed Ne...

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2021 IEEE Global Communications Conference (GLOBECOM)

作者： Hua Wu Tingzheng Chen Ziling Shao Guang Cheng Xiaoyan Hu School of Cyber Science and Engineering Southeast University Nanjing China Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University Nanjing China Purple Mountain Laboratories for Network and Communication Security Nanjing China

ISBN: (纸本)9781728181059

The existing DDoS attack detection methods based on a single monitoring point only consider symmetric routing scenarios, which may not be practical. Such schemes will produce high false positives when facing the asymmetric routing scenarios. Besides, few of them are applicable in high-speed networks. The paper designs a DDoS detection scheme customized for high-speed networks and takes asymmetric routing scenarios into account. Systematic sampling is applied to high-speed incoming traffic, and a proposed Double Composite Structure Sketch (DCSS) is utilized for fast recording and extraction of features based on the characteristics of DDoS attacks in both symmetric and asymmetric routing scenarios. Then classifiers are trained for online DDoS detection. Our experimental results using the public dataset show that in a 10Gbps network with asymmetric routing, our approach can accurately detect UDP Flood and SYN Flood attacks within 20 seconds when the sampling rate is set to 1/2048.

关键词： Systematics High-speed networks Conferences Routing Denial-of-service attack Feature extraction Global communication

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