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2021 International Conference on Internet of Things and Machine Learning, IoTML 2021

作者： Zhang, Yu Zhou, Yangbo Ma, Xiaowei Tianjin Key Laboratory of Network and Data Security Technology College of Cyber Science Nankai University Tianjin China

ISBN: (数字)9781510653269

ISBN: (纸本)9781510653252

network intrusion detection system (NIDS) is a tool that can detect various network attacks by analyzing network traffic. In recent years, traditional machine learning and deep learning methods have been widely used in NIDS. And these works achieved high detection rate and low false positive rate when identifying common attacks (such as DoS). However, the existing models performed poor in detecting rare or unseen network attacks. In this paper, we design a novel network intrusion detection model based on two-phase detection and manually labeling. This two-phase detection model (TPDM) is a multi-classification system, and it can classify network traffic into benign or specific attack type. TPDM gains the overall accuracy 98.35%, and average F1-score 99.23% on UNSW-NB15 dataset. The experiment shows that TPDM performs better than state of the art in detecting rare attacks with few training samples. Besides, it can detect unknown network attacks and then label these attacks. © SPIE.

关键词： Intrusion detection

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A Novel Distributed Anomaly Detection Algorithm for Low-density data

A Novel Distributed Anomaly Detection Algorithm for Low-dens...

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2020 IEEE International Conference on Advances in Electrical Engineering and Computer Applications, AEECA 2020

作者： Mei, Lin Zhang, Fengli University of Electronic Science and Technology of China Network and Data Security Key Laboratory of Sichuan Province Chengdu China

ISBN: (纸本)9781728165202

Anomaly detection becomes an interesting and significant problem in data mining area. It, in turn, generates many promising applications such as Intrusion Detection, Fraud Detection, Mobile cellular network fraud, and etc. Most of the existing anomaly detection methods are specifically modeled on certain tasks or datasets. And the density-based model has widely applied in anomaly detection. However, for many real-world applications, domain experts usually confront with large scale low-density data. This paper aims to solve the anomaly detection problem of large-scale low-density data. The Relative Local Density-based Outlier Factor (RLDOF) is a highly efficient and promising method for low-density data. While it is not easy to achieve considerable results concerning large-scale data. In this paper, we propose a distributed RLDOF model for low-density data on a large scale. The experimental results indicate that, compared with the existing methods, the model can effectively improve the anomaly detection performance of large-scale low-density data. © 2020 IEEE.

关键词： Anomaly detection

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Euphemism Detection by Transformers and Relational Graph Attention network 3

Euphemism Detection by Transformers and Relational Graph Att...

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3rd Workshop on Figurative Language Processing, FigLang 2022, as part of EMNLP 2022

作者： Wang, Yuting Liu, Yiyi Zhang, Ruqing Fan, Yixing Guo, Jiafeng CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781959429111

Euphemism is a type of figurative language broadly adopted in social media and daily conversations. People use euphemisms for politeness or to conceal what they are discussing. Euphemism detection is a challenging task because of its obscure and figurative nature. Even humans may not agree on if a word expresses euphemism. In this paper, we propose to employ bidirectional encoder representations transformers (BERT), and relational graph attention network in order to model the semantic and syntactic relations between the target words and the input sentence. The best performing method of ours reaches a macro F1 score of 84.0 on the euphemism detection dataset of the third workshop on figurative language processing shared task 2022. © 2022 Association for Computational Linguistics.

关键词： Semantics

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CarfRCNN: A Two-stage Effective Model for Instance Segmentation

CarfRCNN: A Two-stage Effective Model for Instance Segmentat...

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2021 International Conference on Intelligent Manufacturing and Industrial Automation, CIMIA 2021

作者： Chen, Yujie Zhao, Pengbiao Chen, Julin Network and Data Security Key Laboratory of Sichuan Province University of Electr Onic Science and Technology of China China

With the rapid development of deep learning, many instance segmentation models have achieved good results in accuracy and time. But here are still many problems. In this paper, we proposed a two-stage model CarfRCNN. We proposed CAResNet to change the structure of the backbone, making the feature extraction of the input image more refined. At the same time, we also added the CRF module to add smooth constraints to the pixels, so that the segmentation mask fits the target contour more closely. We train and test on the COCO datasets, and compare with the Mask R-CNN. The experimental results prove that the model we proposed can greatly improve the accuracy. © Published under licence by IOP Publishing Ltd.

关键词： Deep learning

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Locally smoothed neural networks 9

Locally smoothed neural networks

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9th Asian Conference on Machine Learning, ACML 2017

作者： Pang, Liang Lan, Yanyan Xu, Jun Guo, Jiafeng Cheng, Xueqi CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

Convolutional Neural networks (CNN) and the locally connected layer are limited in capturing the importance and relations of different local receptive fields, which are often crucial for tasks such as face verification, visual question answering, and word sequence prediction. To tackle the issue, we propose a novel locally smoothed neural network (LSNN) in this paper. The main idea is to represent the weight matrix of the locally connected layer as the product of the kernel and the smoother, where the kernel is shared over different local receptive fields, and the smoother is for determining the importance and relations of different local receptive fields. Specifically, a multi-variate Gaussian function is utilized to generate the smoother, for modeling the location relations among different local receptive fields. Furthermore, the content information can also be leveraged by setting the mean and precision of the Gaussian function according to the content. Experiments on some variant of MNIST clearly show our advantages over CNN and locally connected layer. © 2017 L. Pang†∗, Y. Lan†, J. Xu†, J. Guo† & X. Cheng†.

关键词： Convolution

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ICTNET at Trec 2019 Conversational Assistance Track 28

ICTNET at Trec 2019 Conversational Assistance Track

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28th Text REtrieval Conference, TREC 2019

作者： Hao, Changying Zhang, Yuanyuan Yang, Weifeng Zhao, Heng CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

In this paper we report on our participation in the TREC 2019 Conversational Assistance Track which focuses on Conversational Information Seeking（CIS）namely understand the dialogue context and retrieve candidate response information from collections provided. We convert the CIS task into a standard information retrieval task and use both traditional IR model and neural IR model to rerank the baseline official evaluation results. We compare the results of models in two categories(four models in total), and give a summary for the solution of our work. © 2019 28th Text REtrieval Conference, TREC 2019 - Proceedings. All Rights Reserved.

关键词： Information retrieval

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In-situ synthesized SiC/TiC/SiTiOC hybrid nanofiber mats for broad-range temperature microwave absorption

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science China Technological sciences 2025年第6期 183-196页

作者： Yashan HUO Yuetong QIAN Minshan ZHAO Lei ZHANG Yujia TAN Zhihui HE Peng MIAO Fuchun ZHANG Wei CUI Renchao CHE School of Physics and Electronic Information Yan'an University Shaanxi Key Laboratory of Intelligent Processing for Big Energy Data Laboratory of Advanced Materials Shanghai Key Lab of Molecular Catalysis and Innovative Materials Academy for Engineering &Technology Advanced Coatings Research Center of Ministry of Education of China Fudan University School of Materials and Chemical Engineering Xi'an Technological University School of Materials Science & Engineering Tongji University College of Physics Donghua University

Efficient, temperature-insensitive electromagnetic attenuation materials are critical for high microwave absorption at high temperatures. This study presents SiC/TiC/SiTiOC hybrid nanofiber mats constructed using an in-situ synthesis method;these mats incorporate SiC/TiC as the microwave loss unit and SiTiOC as the antioxidant unit, offering an innovative approach to high-temperature microwave absorption. The introduction of high-conductivity TiC not only optimizes the dielectric constant and impedance matching of the material but also enhances its thermal cycling stability. This design ensures steady electromagnetic attenuation, enabling the hybrid nanofiber mats to maintain robust absorption performance in the X-band, even at high temperatures. At 873 K, the material demonstrates an effective absorption bandwidth of 3.6 GHz and a minimum reflection loss of-45.55 dB. Compared to single-system absorbers, such as SiC/C nanofiber materials, with a single system, the multicomponent SiC/TiC/SiTiOC hybrid nanofiber mats deliver reliable absorption performance in the temperature range of 293–873 K. This study confirms that highly conductive titanium-based ceramic materials used for dielectric property regulation effectively address the challenges of poor temperature stability and limited reusability in high-temperature carbon-ceramic absorbing materials, providing valuable insights and guidance for designing efficient microwave absorbers that can operate across a wide temperature range.

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Fuzzing Drone Control System Configurations Based on Quality-Diversity Enhanced Genetic Algorithm 1st

Fuzzing Drone Control System Configurations Based on Qualit...

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1st International Conference on Artificial Intelligence Security and Privacy, AIS and P 2023

作者： Chang, Zhiwei Zhang, Hanfeng Yang, Yue Jia, Yan Xu, Sihan Li, Tong Liu, Zheli College of Cyber Science Tianjin Key Laboratory of Network and Data Security Technology Nankai University Tianjin China

ISBN: (纸本)9789819997848

As drones are becoming widely used in various fields, drone security is a growing challenge nowadays. Drone control systems use various configuration parameters to control their positions and attitudes. If these parameters are misconfigured, drones will fall into abnormal flight states, such as trajectory deviation and crash to the ground. Existing works mainly focus on system memory errors which lead to obvious system failure but don’t apply to drone flight state anomalies. This paper focuses on abnormal drone flight states caused by configuration parameter errors. We propose a novel state-guided fuzzing system called APFuzzer, which searches for incorrect configuration parameter values that would trigger abnormal flight states. To enhance the capability of searching for multiple optimal solutions, we design a quality-diversity enhanced genetic algorithm (QDGA) to mutate configurations to search for incorrect configuration parameter values and consider the effects of environmental factors and flight missions on the flight states. We evaluated APFuzzer on the drone control system ArduPilot and successfully searched 3389 incorrect configuration parameter values and triggered all predefined five abnormal flight states. In addition, APFuzzer automatically analyzed the fuzzing results and found five software bugs related to configurations. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Genetic algorithms

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Research on Improved MobileViT Image Tamper Localization Model

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Computers, Materials & Continua 2024年第8期80卷 3173-3192页

作者： Jingtao Sun Fengling Zhang Huanqi Liu Wenyan Hou School of Computer Science and Technology Xi’an University of Posts and TelecommunicationsXi’an710121China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an University of Posts and TelecommunicationsXi’an710121China

As image manipulation technology advances rapidly,the malicious use of image tampering has alarmingly escalated,posing a significant threat to social *** the realm of image tampering localization,accurately localizing limited samples,multiple types,and various sizes of regions remains a multitude of *** issues impede the model’s universality and generalization capability and detrimentally affect its *** tackle these issues,we propose FL-MobileViT-an improved MobileViT model devised for image tampering *** proposed model utilizes a dual-stream architecture that independently processes the RGB and noise domain,and captures richer traces of tampering through dual-stream ***,the model incorporating the Focused Linear Attention mechanism within the lightweight network(MobileViT).This substitution significantly diminishes computational complexity and resolves homogeneity problems associated with traditional Transformer attention mechanisms,enhancing feature extraction diversity and improving the model’s localization *** comprehensively fuse the generated results from both feature extractors,we introduce the ASPP architecture for multi-scale feature *** facilitates a more precise localization of tampered regions of various ***,to bolster the model’s generalization ability,we adopt a contrastive learning method and devise a joint optimization training strategy that leverages fused features and captures the disparities in feature distribution in tampered *** strategy enables the learning of contrastive loss at various stages of the feature extractor and employs it as an additional constraint condition in conjunction with cross-entropy *** a result,overfitting issues are effectively alleviated,and the differentiation between tampered and untampered regions is *** evaluations on five benchmark datasets(IMD-20,CASIA,NIST-16,Columbia and Coverage)validat

关键词： Image tampering localization focused linear attention mechanism MobileViT contrastive loss

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An Overview of General Theory of Security

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China Communications 2017年第7期14卷 1-10页

作者： Min Lei Yixian Yang Xinxin Niu Yu Yang Jie Hao Information Security Center Beijing University of Posts and Telecommunications Guizhou University Guizhou Provincial Key Laboratory of Public Big Data Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science & Technology

Cyber security lacks comprehensive theoretical guidance. General security theory, as a set of basic security theory concepts, is intended to guide cyber security and all the other security work. The general theory of security aims to unify the main branches of cyber security and establish a unified basic theory. This paper proposal an overview on the general theory of security, which is devoted to constructing a comprehensive model of network security. The hierarchical structure of the meridian-collateral tree is described. Shannon information theory is employed to build a cyberspace security model. Some central concepts of security, i.e., the attack and defense, are discussed and several general theorems on security are presented.

关键词： cyber security network attack and defense meridian-collateral theory

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