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Over-sampling algorithm for imbalanced data classification

Journal of Systems Engineering and Electronics 2019年第6期30卷 1182-1191页

作者： XU Xiaolong CHEN Wen SUN Yanfei Jiangsu Key Laboratory of Big Data Security&Intelligent Processing Nanjing University of Posts and TelecommunicationsNanjing 210023China Institute of Big Data Research at Yancheng Nanjing University of Posts and TelecommunicationsYancheng 224000China Office of Scientific R&D Nanjing University of Posts and TelecommunicationsNanjing 210023China

For imbalanced datasets, the focus of classification is to identify samples of the minority class. The performance of current data mining algorithms is not good enough for processing imbalanced datasets. The synthetic minority over-sampling technique(SMOTE) is specifically designed for learning from imbalanced datasets, generating synthetic minority class examples by interpolating between minority class examples nearby. However, the SMOTE encounters the overgeneralization problem. The densitybased spatial clustering of applications with noise(DBSCAN) is not rigorous when dealing with the samples near the *** optimize the DBSCAN algorithm for this problem to make clustering more reasonable. This paper integrates the optimized DBSCAN and SMOTE, and proposes a density-based synthetic minority over-sampling technique(DSMOTE). First, the optimized DBSCAN is used to divide the samples of the minority class into three groups, including core samples, borderline samples and noise samples, and then the noise samples of minority class is removed to synthesize more effective samples. In order to make full use of the information of core samples and borderline samples,different strategies are used to over-sample core samples and borderline samples. Experiments show that DSMOTE can achieve better results compared with SMOTE and Borderline-SMOTE in terms of precision, recall and F-value.

关键词： imbalanced data density-based spatial clustering of applications with noise(DBSCAN) synthetic minority over sampling technique(SMOTE) over-sampling.

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Sharing of Encrypted Lock keys in the Blockchain-Based Renting House System from Time- and Identity-Based Proxy Reencryption

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China Communications 2022年第5期19卷 164-177页

作者： Zhiwei Wang Liping Qian Danwei Chen Guozi sun School of Computer Nanjing University of Posts and TelecommunicationsNanjing 210023China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing 210023China Guangxi Key Laboratory of Cryptography and Information Security Department of Computer SciencesGuilin University of Electronic TechnologyGuilin 541004China

To design an efficient protocol for sharing the encrypted lock keys in the renting house system,we introduce a new notion called time-and identitybased proxy reencryption(TIPRE)and the blockchain *** CPA secure TIPRE scheme is constructed from Green et al.’s identity-based proxy reencryption scheme by adding the time *** every time period,a time stamp authority generates a public key embedded with the current time stamp for each *** our protocol for the renting house system,the TIPRE scheme is the primary building block,and the blockchain platform serves instead of a trusted third party,such as a real estate agency between landlords and *** TIPRE scheme allows the landlord to change the lock key at each time period for *** blockchain platform allows the landlords and tenants to directly interact,and all of the interactions are recorded in the blockchain database to provide the desired security requirements,such as nonrepudiation and ***,we provide the secure analysis of our protocol and test its performance by implementing it in the MacBook Pro and the Intel Edison development platforms.

关键词： identity-based proxy reencryption time stamp renting houses system blockchain

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Raft Consensus Algorithm Based on Credit Model in Consortium Blockchain

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Wuhan University Journal of Natural Sciences 2020年第2期25卷 146-154页

作者： CHEN Yunfang LIU Ping ZHANG Wei School of Computer Science Nanjing University of Posts and TelecommunicationsNanjing 210023JiangsuChina Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and TelecommunicationsNanjing 210023JiangsuChina

As one of the underlying technologies of the blockchain,the consensus algorithm plays a vital role in ensuring security and *** a consensus algorithm for the private blockchain,Raft has better performance than the rest of the consensus algorithms,and it does not cause problems such as the concentrated hashing power,resource waste and ***,Raft can only be used in a non-byzantine environment with a small network *** order to enable Raft to be used in a large-scale network with a certain number of byzantine nodes,this paper combines Raft and credit model to propose a Raft blockchain consensus algorithm based on credit model *** the node credit evaluation phase,RBF-based support vector machine is used as the anomaly detection method,and the node credit evaluation model is *** the Trust Nodes List(TNL)mechanism is introduced to make the consensus phase in a creditable network ***,the common node is synchronized to the consensus node to update the blockchain of the entire *** show that CRaft has better throughput and lower latency than the commonly used consortium blockchain consensus algorithm PBFT(Practical Byzantine Fault Tolerance).

关键词： credit model consensus algorithm consortium blockchain P2P network

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ALEDAR: An Attentions-based Encoder-Decoder and Autoregressive model for workload Forecasting of Cloud data Center 25

ALEDAR: An Attentions-based Encoder-Decoder and Autoregressi...

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25th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022

作者： Sun, Wei Xu, Xiaolong Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Big Data Security and Intelligent Processing School of Computer Science Nanjing China

ISBN: (纸本)9781665405270

Effective workload forecasting can provide a reference for resource scheduling in cloud data centers. Compared with the normal single data center, the multi-data center has a more complicated architecture design and provides more diverse computing services. The traditional load forecasting models need too much manual intervention to set up the parameters, meanwhile, the emerging neural network methods are not sensitive to the prediction scale and cannot capture the long-term associations between input features. To address these challenges, we propose a hybrid model named Attentions-based LSTM Encoder-Decoder network and Autoregressive model (ALEDAR), which combines neural network and statistical learning methods, to analyze the linear and nonlinear characteristics of the load sequence over time in a multi-cloud data center environment. ALEDAR uses a dual attention-based Encoder-Decoder architecture to extract the relationships between historical workload data, the design can also avoid the deterioration of prediction effect caused by long-range data scale, the output layer is composed of a three-layer perceptron. Moreover, ALEDAR employs an autoregressive module to capture the linear trend of the load sequence and eliminate scale insensitivity of input and output data. The experimental results show that our approach is adaptive and can improve the performance of host workload prediction in both single cloud data center and multi-cloud data center environments. We evaluate the performance of the proposed algorithm on real-world data sets and observe consistent improvement of 9.7% - 34.2% over the state-of-the-art baselines. © 2022 IEEE.

关键词： Forecasting

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A New Semi-supervised Approach for Network Encrypted Traffic Clustering and Classification 25

A New Semi-supervised Approach for Network Encrypted Traffic...

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25th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022

作者： Lin, Kunda Xu, Xiaolong Jiang, Yu School of Computer Science Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing China

ISBN: (纸本)9781665405270

Encrypted network traffic classification is an essential task in modern communications, which is used in a wide range of applications, such as network resource allocation, QoS (Quality of Service), malicious detection, etc. With the continuous evolution of network technology, approaches used to classify network encrypted traffic have become increasingly complex, and model training relies heavily on a large amount of labeled data. However, the acquisition of correct and massive labeled data of network traffic in the real environment remains a major challenge in this field. On the opposite, unlabeled traffic is extremely easy to obtain in a network. Therefore, the effective use of unlabeled data is of great significance to the development of modern communications. In this paper, we propose the Sauce model, which can effectively use unlabeled information to obtain high-quality clustering space. It is composed of Aux (auxiliary network) and AE (auto encoder), where Aux is designed for collaborative training with AE, affecting the distribution of samples in the latent space generated by AE. Sauce uses t-SNE (t- distributed stochastic neighbor embedding) to perform secondary dimensionality reduction on the latent code, and then uses a clustering algorithm for cluster analysis. Besides, sauce applies the self-training technique, replacing the real labeled data with pseudo-labeled data to reduce the reliance on labeled data and improve the utilization of unannotated data. We conduct experiments on two real network datasets. The experiments show that Sauce can achieve clustering accuracies of 98.4% (VPN dataset) and 98.0% (TOR dataset), outperforming other unsupervised or semi-supervised learning methods. © 2022 IEEE.

关键词： Cluster analysis

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Resource allocation in cognitive wireless powered communication networks under outage constraint 4

Resource allocation in cognitive wireless powered communicat...

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4th IEEE International Conference on Computer and Communications, ICCC 2018

作者： Li, Qun Wang, Longqi Xu, Ding Jiangsu Key Lab of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China Wireless Communication Key Lab of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (纸本)9781538683392

We investigate a multi-carrier cognitive wireless powered communication network (CWPCN), where the primary user (PU) is wirelessly powered by harvesting energy from the information signals from the secondary user (SU). The PU is protected by complying with the outage constraint which requires that a minimum rate is satisfied by the PU for a given outage probability. Both offline and online algorithms are proposed to jointly optimize time, subcarrier and power allocation to maximize the SU rate under the outage constraint. Simulation results are presented to verify the proposed algorithms. It is shown that the proposed algorithms outperform the benchmark algorithm with zero-outage constraint. It is also shown that the online algorithm achieves almost the same performance that the offline algorithm achieves. © 2018 IEEE.

关键词： Outages

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Acoustic backdoor attacks on speech recognition via frequency offset perturbation

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Applied Soft Computing 2025年 177卷

作者： Tang, Yu Xu, Xiaolong Sun, Lijuan Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications No. 9 Wenyuan Road Jiangsu Nanjing210023 China School of Computer Science Nanjing University of Posts and Telecommunications No. 9 Wenyuan Road Jiangsu Nanjing210023 China School of computer Science and Technology Huaiyin Normal University Jiangsu Huai'an223300 China

With the increasing deployment of deep learning-based speech recognition systems, backdoor attacks have become a serious security threat, enabling adversaries to implant hidden triggers that activate malicious behaviors while preserving model performance on benign inputs. However, existing acoustic backdoor attacks, whether in the time or frequency domain, often struggle to achieve sufficient stealthiness, as poisoned samples either disrupt semantic integrity or introduce perceptible artifacts. Moreover, these methods typically fail to strike an effective balance among attack efficacy, stealthiness, and robustness. To address these limitations, we propose Shadow Frequency (SF), a novel backdoor attack that leverages psychoacoustic-guided frequency offset perturbations to inject imperceptible yet model-sensitive signals near dominant spectral components. This design ensures auditory imperceptibility while maintaining high attack effectiveness and robustness. Experimental results show that SF achieves over 96% ASR with minimal impact on clean data accuracy, and remains effective under common defenses, validating its practicality for real-world deployment. © 2025 Elsevier B.V.

关键词： Deep learning

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Secure communications for primary users in cognitive radio networks with collusive eavesdroppers

Secure communications for primary users in cognitive radio n...

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作者： Xu, Ding Li, Qun Wireless Communication Key Lab of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing China Jiangsu Key Lab of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China

This letter studies physical-layer security in a cognitive radio (CR) network, where a primary user (PU) is eavesdropped by multiple collusive eavesdroppers. Under the PU secrecy outage constraint to protect the PU, the secondary users (SUs) are assumed to be allowed to transmit. The problem of joint SU scheduling and power control to maximize the SU ergodic transmission rate is investigated for both the scenarios of perfect and imperfect channel state information (CSI). It is shown that, although collusive eavesdroppers degrade the PU performance compared to noncollusive eavesdroppers, the SU performance is actually improved when the number of eavesdroppers is large. It is also shown that our proposed scheme with imperfect CSI can guarantee that the PU performance is unaffected by imperfect CSI. © 2018 The Institute of Electronics, Information and Communication Engineers.

关键词： Cognitive radio

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CABAC: A content-driven attribute-based access control model for big data 5th

CABAC: A content-driven attribute-based access control model...

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5th International Conference on Artificial Intelligence and security, ICAIS 2019

作者： Ma, Ke Yang, Geng Nanjing University of Posts and Telecommunications Nanjing210023 China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing210023 China

ISBN: (纸本)9783030242701

In recent years, attribute-based access control (ABAC) models have been widely used in big data and cloud computing. However, with the growing importance of data content, using data content to assist authorization for access controls has become more common. In this paper, we propose a dynamic content-driven attribute-based access control model (CABAC) for large-scale unstructured data. CABAC is a fine-grained access control model that use two-layer authorization to balance efficiency and accuracy. The first-layer authorization uses attributes to grant users basic authority and the second-layer authorization uses data content to grant broader authority over "related" data. Experimental results show that CABAC has acceptable efficiency and it can expand the authority of users without reducing security. © Springer Nature Switzerland AG 2019.

关键词： big data

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Topic modeling for noisy short texts with multiple relations 30

Topic modeling for noisy short texts with multiple relations

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30th International Conference on Software Engineering and Knowledge Engineering, SEKE 2018

作者： Liu, Chiyu Liu, Zheng Li, Tao Xia, Bin Jiangsu Key Laboratory of Big Data Security and Intelligent Processing School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China

ISBN: (纸本)1891706446

Understanding contents in social networks by inferring high-quality latent topics from short texts is a significant task in social analysis, which is challenging because social network contents are usually extremely short, noisy and full of informal vocabularies. Due to the lack of sufficient word co-occurrence instances, well-known topic modeling methods such as LDA and LSA cannot uncover high-quality topic structures. Existing research works seek to pool short texts from social networks into pseudo documents or utilize the explicit relations among these short texts such as hashtags in tweets to make classic topic modeling methods work. In this paper, we explore this problem by proposing a topic model for noisy short texts with multiple relations called MRTM (Multiple Relational Topic Modeling). MRTM exploits both explicit and implicit relations by introducing a document-Attribute distribution and a two-step random sampling strategy. Extensive experiments, compared with stateof-the-Art topic modeling approaches, demonstrate that MRTM can alleviate the word co-occurrence sparsity and uncover highquality latent topics from noisy short texts. © 2018 Universitat zu Koln. All rights reserved.

关键词： Quality control

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