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arXiv 2018年

作者： Xu, Ding Li, Qun Wireless Communication Key Lab of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing210003 China Jiangsu Key Lab of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing210046 China

We consider a cognitive wireless powered communication network (CWPCN) sharing the spectrum with a primary network who faces security threats from eavesdroppers (EAVs). We propose a new cooperative protocol for the wireless powered secondary users (SU) to cooperate with the primary user (PU). In the protocol, the SUs first harvest energy from the power signals transmitted by the cognitive hybrid access point during the wireless power transfer (WPT) phase, and then use the harvested energy to interfere with the EAVs and gain transmission opportunities at the same time during the wireless information transfer (WIT) phase. Taking the maximization of the SU ergodic rate as the design objective, resource allocation algorithms based on the dual optimization method and the block coordinate descent method are proposed for the cases of perfect channel state information (CSI) and collusive/noncollusive EAVs under the PU secrecy constraint. More PU favorable greedy algorithms aimed at minimizing the PU secrecy outage probability are also proposed. We furthermore consider the unknown EAVs' CSI case and propose an efficient algorithm to improve the PU security performance. Extensive simulations show that our proposed protocol and corresponding resource allocation algorithms can not only let the SU gain transmission opportunities but also improve the PU security performance even with unknown EAVs' CSI. Copyright © 2018, The Authors. All rights reserved.

关键词： Cognitive radio

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Maximum Likelihood Estimation based on Random Subspace EDA: Application to extrasolar planet detection

arXiv

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arXiv 2017年

作者： Liu, Bin Chen, Ke-Jia School of Computer Science Nanjing University of Posts and Telecommunications Nanjing Jiangsu210023 China Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing Jiangsu210023 China

This paper addresses maximum likelihood (ML) estimation based model fitting in the context of extrasolar planet detection. This problem is featured by the following properties: 1) the candidate models under consideration are highly nonlinear;2) the likelihood surface has a huge number of peaks;3) the parameter space ranges in size from a few to dozens of dimensions. These properties make the ML search a very challenging problem, as it lacks any analytical or gradient based searching solution to explore the parameter space. A population based searching method, called estimation of distribution algorithm (EDA), is adopted to explore the model parameter space starting from a batch of random locations. EDA is featured by its ability to reveal and utilize problem structures. This property is desirable for characterizing the detections. However, it is well recognized that EDAs can not scale well to large scale problems, as it consists of iterative random sampling and model fitting procedures, which results in the well-known dilemma curse of dimensionality. A novel mechanism to perform EDAs in interactive random subspaces spanned by correlated variables is proposed and the hope is to alleviate the curse of dimensionality for EDAs by performing the operations of sampling and model fitting in lower dimensional subspaces. The effectiveness of the proposed algorithm is verified via both benchmark numerical studies and real data analysis. Copyright © 2017, The Authors. All rights reserved.

关键词： Maximum likelihood estimation

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A Privacy-preserving and Collusion-resisting Top-k Query processing in WSNs

A Privacy-preserving and Collusion-resisting Top-k Query Pro...

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International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Jianguo Zhou Hua Dai Jie Zhu Rongqi Qi Geng Yang Jian Xu Nanjing University of Post and Telecommunication Nanjing China Jiangsu Security and Intelligent Processing Lab of Big Data Nanjing University of Post and Telecommunication Nanjing China

ISBN: (数字)9781728199160

ISBN: (纸本)9781728192369

In the wireless sensor networks, it is a challenging issue to protect the data privacy from curious users while providing top-k query services. In this paper, a novel privacy-preserving and collusion-resisting top-k query processing interactive protocol is proposed for WSNs. To the best of our knowledge, it is the first work providing the privacy preservation and collusion resistance simultaneously in top-k query processing in WSNs. data encryption with different private keys, the bloom filter and HMAC are adopted to achieve data privacy preservation even there are a few sensors colluding with the adversaries. During the interactive procedures of the query processing, two rounds of secure interactions between the sink and sensors are performed to obtain the query results. The protocol analysis indicates that the protocol can preserve data privacy even a few sensors collude with the adversaries, while the experiment result shows that the proposed protocol has good performance on network communication cost.

关键词： Wireless communication Wireless sensor networks data privacy Protocols Query processing Sensors Encryption

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Implementation and performance op timization of dynamic random forest

Implementation and performance op timization of dynamic rand...

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第九届网络分布式计算与知识发现国际会议( 2017 International Conference on Cyber-enabled distributed computing and knowledge discovery)

作者： Xiaolong Xu Wen Chen School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and

Bernard combines the weight updating of the boosting algorithm with the Random Forest(RF),and proposes a new RF induction algorithm called Dynamic Random Forest(DRF).The idea with DRF is to grow only trees that would fit the sub-forest already built,use the existing forest to update the weight of each randomly selected training instance,force the next tree to pay attention to those samples that can not be classified well by the current forest,thus improving the RF ***,this weight updating method is still flawed,which does not make a good distinction between the samples classified correctly and the samples classified wrongly by the current *** this paper,we implement the DRF algorithm,and propose a new weight update method,that is,giving higher weight to the samples classified wrongly by the current forest,giving lower weight to the samples classified correctly by the current forest,so that the next tree will be more concerned with those misclassified *** results show that our method is better than DRF algorithm and traditional RF algorithm.

关键词： Random forest Boosting Dynamic random forest Weight updating

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SCAlabLE ATTRIBUTION OF ADVERSARIAL ATTACKS VIA MULTI-TASK LEARNING

arXiv

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arXiv 2023年

作者： Guo, Zhongyi Han, Keji Ge, Yao Ji, Wei Li, Yun Nanjing University of Posts and Telecommunications Nanjing China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China

Deep neural networks (DNNs) can be easily fooled by adversarial attacks during inference phase when attackers add imperceptible perturbations to original examples, i.e., adversarial examples. Many works focus on adversarial detection and adversarial training to defend against adversarial attacks. However, few works explore the tool-chains behind adversarial examples, which can help defenders to seize the clues about the originator of the attack, their goals, and provide insight into the most effective defense algorithm against corresponding attacks. With such a gap, it is necessary to develop techniques that can recognize tool-chains that are leveraged to generate the adversarial examples, which is called Adversarial Attribution Problem (AAP). In this paper, AAP is defined as the recognition of three signatures, i.e., attack algorithm, victim model and hyperparameter. Current works transfer AAP into single label classification task and ignore the relationship between these signatures. The former will meet combination explosion problem as the number of signatures is increasing. The latter dictates that we cannot treat AAP simply as a single task problem. We first conduct some experiments to validate the attributability of adversarial examples. Furthermore, we propose a multi-task learning framework named Multi-Task Adversarial Attribution (MTAA) to recognize the three signatures simultaneously. MTAA contains perturbation extraction module, adversarial-only extraction module and classification and regression module. It takes the relationship between attack algorithm and corresponding hyperparameter into account and uses the uncertainty weighted loss to adjust the weights of three recognition tasks. The experimental results on MNIST and ImageNet show the feasibility and scalability of the proposed framework as well as its effectiveness in dealing with false alarms. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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Sequential monte carlo method for bayesian multiple testing of pairwise interactions among large number of neurons 14

Sequential monte carlo method for bayesian multiple testing ...

引用

14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2018

作者： Liu, Bin Vinci, Giuseppe Snyder, Adam C. Kass, Robert E. School of Computer Science Jiangsu Key Lab of Big Data Security Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing210023 China Department of Statistics Rice University Houston United States Visual Neuroscience Lab University of Pittsburgh Pittsburgh United States Basis of Cognition Carnegie Mellon University Machine Learning Department Center for the Neural Department of Statistics Pittsburgh United States

ISBN: (纸本)9781538680971

The problem of multiple testing arises in many contexts, including testing for pairwise interaction among a large number of neurons. Recently a method was developed to control false positives when covariate information, such as distances between pairs of neurons, is available. This method, however, relies on computationally-intensive Markov Chain Monte Carlo (MCMC). Here we develop an alternative, based on Sequential Monte Carlo, which only requires one pass of the data. This scheme considers data items sequentially, with relevant probabilities being updated at each step. Simulation experiments demonstrate that the proposed algorithm delivers results as accurately as the previous MCMC method. We illustrate the method by using it to analyze neural recordings from extrastriate cortex in a macaque monkey. © 2018 IEEE.

关键词： Neurons

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LogPal: A Generic Anomaly Detection Scheme of Heterogeneous Logs for Network Systems

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security and Communication Networks 2023年第1期2023卷

作者： Sun, Lei Xu, Xiaolong Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing210023 China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China

As a key resource for diagnosing and identifying problems, network syslog contains vast quantities of information. And it is the main source of data for anomaly detection of systems. Syslog presents the characteristics of large scale, diverse types and sources, data noise, and quick evolvement, which makes the detection methods not generic enough. To effectively address problem of log anomaly labelling caused by massive heterogeneous logs, we propose LogPal, a generic anomaly detection scheme of heterogeneous logs for network systems, which innovatively combines template sequences and raw log sequences to construct and generate log pattern events. By improving the self-attention mechanism of transformer, LogPal proactively synthesizes self-attention and handles log pattern events in a unique way. The model can make full use of log template and sequence semantic information, by automatically becoming aware of the pattern of logs. We implemented experiments to evaluate the performance of LogPal on publicly available datasets, and the outcome of the experiments shows that LogPal automatically adapts to log type changes and improves precision, recall, and F1 score to 99% on publicly available datasets. © 2023 Lei Sun and Xiaolong Xu.

关键词： Anomaly detection

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On Link Formation in Heterogeneous Information Networks: A View Based on Multi-label Learning

On Link Formation in Heterogeneous Information Networks: A V...

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International Conference on Advances in Social Network Analysis and Mining, ASONAM

作者： Ke-Jia Chen Shijun Xue Yun Li Bin Liu Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing Jiangsu China College of Computer Science Nanjing University of Posts and Telecommunications Nanjing Jiangsu China

ISBN: (数字)9781450349932

ISBN: (纸本)9781450349932

This paper studies the problem of relationship prediction in heterogeneous information networks. Our goal is not only to predict links/relationships more accurately but also to provide more viable paths to facilitate the formation of new links/relationships. A relationship prediction method based on multi-label learning named ML 3 P is proposed. In ML 3 P, each meta-path between nodes is regarded as a type of relationship and is given a label. Under the framework of multi-label learning, any potential relationship including the target relationship can be predicted. The results of comparative experiments in DBLP and Twitter datasets show that ML 3 P better uses heterogeneous information in supervised learning process and thus achieves better performance. Moreover, our method can output the correlation between relationships.

关键词： Twitter Task analysis Feature extraction Training Measurement Correlation

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MNN: Mixed Nearest-Neighbors for Self-Supervised Learning

arXiv

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arXiv 2023年

作者： Long, Xianzhong Peng, Chen Li, Yun School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing210023 China

In contrastive self-supervised learning, positive samples are typically drawn from the same image but in different augmented views, resulting in a relatively limited source of positive samples. An effective way to alleviate this problem is to incorporate the relationship between samples, which involves including the top-K nearest neighbors of positive samples. However, the problem of false neighbors (i.e., neighbors that do not belong to the same category as the positive sample) is an objective but often overlooked challenge due to the query of neighbor samples without supervision information. In this paper, we present a simple self-supervised learning framework called Mixed Nearest-Neighbors for Self-Supervised Learning (MNN). MNN optimizes the influence of neighbor samples on the semantics of positive samples through an intuitive weighting approach and image mixture operations. The results demonstrate that MNN exhibits exceptional generalization performance and training efficiency on four benchmark datasets. © 2023, CC BY.

关键词： Supervised learning

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SLBERT: An Improved FLAT-BERT for Named Entity Recognition

SSRN

引用

SSRN 2022年

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

Named Entity Recognition (NER) aims to identify named entities mentioned in unstructured text and classify them into predefined named entity classes. Although models using word fusion have achieved relatively high performance, the recognition of ambiguous entities in text remains challenging, and the performance of the models depends on a sufficient amount of manually labeled data;exploring models that fuse more feature dimensions and schemes that fuse existing datasets is of farreaching interest. In this work, we propose an improved scheme of FLAT-BERT, SLBERT, that can be used in the field of named entity *** scheme consists of two phases that more fully exploit the global semantics of the text. In the first stage, the model is trained based on the current dataset, another dataset is predicted for cross-dataset fusion, and the correctness of label annotation is improved by entity confidence modeling. In the second stage, the global semantics of the text is extracted using Sentence BERT and embedded into the feature matrix encoded using Lattice BERT for subsequent Token classification. We have conducted experiments on several datasets, and the experimental results demonstrate the effectiveness of the proposed scheme in this paper. In terms of F1 values, compared with FLAT-BERT, the improvement is 0.15%, 0.34%, 0.61%, and 0.58% on datasets OntoNotes, MSRA, Resume, and Weibo, respectively. © 2022, The Authors. All rights reserved.

关键词： Semantics

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