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SDT-CNN-based network security situation awareness

Journal of Information security and Applications 2025年 93卷

作者： Dongmei Zhao Huiqian Song Shuiguang Zeng College of Computer and Cyber Security Hebei Normal University Shijiazhuang 050024 China Hebei Key Laboratory of Network and Information Security Shijiazhuang 050024 China Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics and Data Security Shijiazhuang 050024 China Medical Technology College Hebei Medical University Shijiazhuang 050017 China

In the scenario of large-scale network data, existing network security situation awareness methods have low accuracy of situation understanding and prediction, usually rely on hand-crafted feature selection, and take a long time for situation awareness. To this end, this paper proposes a situation awareness scheme based on SDT-CNN (Spatial & Depthwise Separable & Time Factor-Convolution Neural Network). Specifically, spatial and depthwise separable convolution are utilized to construct a composite series structure unit with fewer parameters and more depth than traditional convolution. A time factor is designed to take advantage of the time series data and control the fusion of the input data. Then, the awareness scheme is proposed with the data preprocessing, the proposed unit, time factor, and other optimizations such as imbalanced data adjustment. Finally, the experimental results on the datasets of KDD Cup 99, AWID, and UNSW-NB15 have shown that our proposed scheme outperforms the existing methods in wired networks, wireless networks, and modern network traffic and attack scenarios and shows much broader applicability than other methods.

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Federated Learning-Based Intelligent Indoor Smoke and Fire Detection System for Smart Buildings

Federated Learning-Based Intelligent Indoor Smoke and Fire D...

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Telecommunications and Intelligent Systems (ICTIS), International Conference on

作者： Mohamed Rafik Aymene Berkani Ammar Chouchane Yassine Himeur Anes Abdennebi Seref Sagiroglu Abbes Amira Research Laboratory in Advanced Electronics Systems (LSEA) University Yahia Fares of Medea Medea Algeria Laboratory of LI3C University Biskra University Center of Barika Barika Algeria College of Engineering and Information Technology University of Dubai Dubai United Arab Emirates Software and Information Technology Engineering École de Technologie Supérieure Montréal Canada Artificial Intelligence and Big Data Analytics Security R&D Center Gazi University Ankara Turkey Department of Computer Science University of Sharjah Sharjah United Arab Emirates

ISBN: (数字)9798331527396

ISBN: (纸本)9798331527402

Ensuring safety in smart buildings is crucial due to the increasing prevalence of smoke and fire hazards in modern environments. This paper introduces a novel privacy-preserving FL approach based on a CNN1D for smoke and fire detection in smart buildings. Our system integrates data from wearable environmental sensors to train a lightweight, edge-deployable DL-CNN1D model, ensuring data privacy while enabling collaborative learning across distributed clients using Federated Averaging (FedAvg) aggregation. Experiments conducted on a comprehensive air measurement dataset for smoke and fire detection demonstrate exceptional performance, with the global model achieving 99.97% accuracy, 99.96% precision, in smoke and fire recognition. Our model demonstrates a low communication cost of 0.4 MB, underscoring its efficiency for real-time applications. Our FL-based approach represents a significant step towards balancing the need for robust safety systems with growing privacy concerns in smart building environments.

关键词： Privacy Smart buildings Costs Federated learning Atmospheric modeling data models Convolutional neural networks Wearable sensors Monitoring Intelligent sensors

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Reduce the rank calculation of a high-dimensional sparse matrix based on network controllability theory

arXiv

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arXiv 2021年

作者： Zhao, Chen Liu, Yuqing Hu, Li Yuan, Zhengzhong College of Computer and Cyber Security Hebei Normal University Shijiazhuang050024 China Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics & Data Security Shijiazhuang050024 China Hebei Key Laboratory of Network and Information Security Shijiazhuang050024 China School of Mathematics and Statistics Minnan Normal University Zhangzhou363000 China Fujian Key Laboratory of Data Science and Statistics Minnan Normal University Zhangzhou363000 China Institute of Meteorological Big Data-Digital Fujian Minnan Normal University Zhangzhou363000 China

Numerical computing of the rank of a matrix is a fundamental problem in scientific computation. The datasets generated by the internet often correspond to the analysis of high-dimensional sparse matrices. Notwithstanding recent advances in the promotion of traditional singular value decomposition (SVD), an efficient estimation algorithm for the rank of a high-dimensional sparse matrix is still lacking. Inspired by the controllability theory of complex networks, we converted the rank of a matrix into maximum matching computing. Then, we established a fast rank estimation algorithm by using the cavity method, a powerful approximate technique for computing the maximum matching, to estimate the rank of a sparse matrix. In the merit of the natural low complexity of the cavity method, we showed that the rank of a high-dimensional sparse matrix can be estimated in a much faster way than SVD with high accuracy. Our method offers an efficient pathway to quickly estimate the rank of the high-dimensional sparse matrix when the time cost of computing the rank by SVD is unacceptable. Copyright © 2021, The Authors. All rights reserved.

关键词： Singular value decomposition

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Dynamic Concept Reduction Methods Based on Local Information

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International Journal of Approximate Reasoning 2025年

作者： Mei-Zheng Li Lei-Jun Li Ju-Sheng Mi Qian Hu College of Computer and Cyber Security Hebei Normal University Shijiazhuang 050024 China Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics and Data Security Shijiazhuang 050024 China School of Mathematics Science Hebei Normal University Shijiazhuang 050024 China Hebei Key Laboratory of Computational Mathematics and Applications Shijiazhuang 050024 China

Knowledge reduction is one of the core research issues in formal concept analysis. As a new technique of knowledge reduction, concept reduction has received increasing attention recently. One typical method of calculating concept reducts is based on representative concept matrix (RC-matrix, for short), which can obtain all concept reducts. However, it is confronted with the following challenges: (1) before the construction of the RC-matrix, all concepts of the formal context need to be calculated, which is both time and space consuming; (2) there is a lot of redundant information in the constructed RC-matrix, which is not helpful to calculate the concept reducts; (3) when the data changes dynamically, the concept reducts need to be calculated for scratch. To address these issues, dynamic concept reduction methods based on local information are proposed in this paper. Firstly, the characteristics of the minimal elements (with respect to set inclusion) in the RC-matrix are analyzed, and all the minimal elements are directly labeled from the formal context; secondly, the advantage of local information is taken to construct each minimal elements of the RC-matrix, from which all the concept reducts can be obtained; besides, a new simplified version of RC-matrix, named as Type-I minimal RC-matrix, is further constructed to compute one concept reduce; and finally, when data dynamically changes, the connections between concept reducts of the original formal context and those of the new one are analyzed, consequently, two dynamic concept reduction algorithms are proposed.

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Noise-Tolerant Learning for Audio-Visual Action Recognition

arXiv

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arXiv 2022年

作者： Han, Haochen Zheng, Qinghua Luo, Minnan Miao, Kaiyao Tian, Feng Chen, Yan The National Engineering Lab for Big Data Analytics Xi’an Jiaotong University Xi’an710049 China The School of Computer Science and Technology Xi’an Jiaotong University Xi’an710049 China The Key Laboratory of Intelligent Networks and Network Security Xi’an Jiaotong University Ministry of Education Xi’an710049 China The School of Cyber Science and Engineering Xi’an Jiaotong University Xi’an710049 China

Recently, video recognition is emerging with the help of multi-modal learning, which focuses on integrating distinct modalities to improve the performance or robustness of the model. Although various multi-modal learning methods have been proposed and offer remarkable recognition results, almost all of these methods rely on high-quality manual annotations and assume that modalities among multi-modal data provide semantically relevant information. Unfortunately, the widely used video datasets are usually coarse-annotated or collected from the Internet. Thus, it inevitably contains a portion of noisy labels and noisy correspondence. To address this challenge, we use the audio-visual action recognition task as a proxy and propose a noise-tolerant learning framework to find anti-interference model parameters against both noisy labels and noisy correspondence. Specifically, our method consists of two phases that aim to rectify noise by the inherent correlation between modalities. First, a noise-tolerant contrastive training phase is performed to make the model immune to the possible noisy-labeled data. Despite the benefits brought by contrastive training, it would overfit the noisy correspondence and thus provide false supervision. To alleviate the influence of noisy correspondence, we propose a cross-modal noise estimation component to adjust the consistency between different modalities. As the noisy correspondence existed at the instance level, we further propose a category-level contrastive loss to reduce its interference. Second, in the hybrid-supervised training phase, we calculate the distance metric among features to obtain corrected labels, which are used as complementary supervision to guide the training. Furthermore, due to the lack of suitable datasets, we establish a benchmark of real-world noisy correspondence in audio-visual data by relabeling the Kinetics dataset. Extensive experiments on a wide range of noisy levels demonstrate that our method significant

关键词： Modal analysis

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Comparative analysis of SIMILAR to RCD ONE(SRO)family from tetraploid cotton species and their diploid progenitors depict their significance in cotton growth and development

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Journal of Cotton Research 2024年第1期7卷 45-57页

作者： SHABAN Muhammad TABASSUM Riaz RANA Iqrar Ahmad ATIF Rana Muhammad AZMAT Muhammad Abubakkar IQBAL Zubair MAJEED Sajid AZHAR Muhammad Tehseen Department of Plant Breeding and Genetics University of Agriculture FaisalabadSub-Campus BurewalaVehari 61010Pakistan Department of Chemistry and Biochemistry University of AgricultureFaisalabad 38400Pakistan Centre of Agricultural Biochemistry and Biotechnology University of AgricultureFaisalabad 38400Pakistan Department of Plant Breeding and Genetics University of AgricultureFaisalabad 38400Pakistan Precision Agriculture and Analytics Lab National Centre in Big Data and Cloud ComputingCentre for Advanced Studies in Agriculture and Food SecurityUniversity of Agriculture FaisalabadFaisalabad 38400Pakistan Department of Plant Pathology University of CaliforniaDavisCA 95616USA State Key Laboratory of Cotton Biology Institute of Cotton ResearchChinese Academy of Agricultural SciencesAnyangHenan 455000China Federal Seed Certification and Registration Department Ministry of National Food Security and ResearchIslamabad 44090Pakistan School of Agriculture Sciences Zhengzhou UniversityZhengzhou 450000China

Background SRO(Similar to RCD1)genes family is largely recognized for their importance in the growth,develop-ment,and in responding to environmental ***,genome-wide identification and functional characteri-zation of SRO genes from cotton species have not been reported so *** A total of 36 SRO genes were identified from four cotton *** analysis divided these genes into three groups with distinct *** and chromosomal distribution analysis indicated uneven distribu-tion of GaSRO,GrSRO,GhSRO,and GbSRO genes on A2,D5 genomes,Gh-At,Gh-Dt,Gb-At,and Gb-Dt subgenomes,*** duplication analysis revealed the presence of six duplicated gene pairs among GhSRO *** promoter analysis,several elements responsive to the growth,development and hormones were found in GhSRO genes,implying gene induction during cotton growth and *** miRNAs responsive to plant growth and abiotic stress were predicted to target 12 GhSRO ***-specific expression profiling demonstrated the roles of GhSRO genes in one or more *** addition,specific expression pattern of some GhSRO genes dur-ing ovule development depicted their involvement in these developmental *** The data presented in this report laid a foundation for understanding the classification and functions of SRO genes in cotton.

关键词： Cotton SRO miRNAs Gene duplications Gene expression Ovule development

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Explaining multimodal deceptive news prediction models 13

Explaining multimodal deceptive news prediction models

引用

13th International Conference on Web and Social Media, ICWSM 2019

作者： Volkova, Svitlana Ayton, Ellyn Arendt, Dustin L. Huang, Zhuanyi Hutchinson, Brian Data Sciences and Analytics Group National Security Directorate Pacific Northwest National Laboratory 902 Battelle Blvd RichlandWA99354 United States Visual Analytics Group National Security Directorate Pacific Northwest National Laboratory 902 Battelle Blvd RichlandWA99354 United States

In this study we present in-depth quantitative and qualitative analyses of the behavior of multimodal deceptive news classification models. We present several neural network architectures trained on thousands of tweets that leverage combinations of text, lexical, and, most importantly, image input signals. The behavior of these models is analyzed across four deceptive news prediction tasks. Our quantitative analysis reveals that text only models outperform those leveraging only the image signals (by 3-13% absolute in F-measure). Neural network models that combine image and text signals with lexical features e.g., biased and subjective language markers perform even better e.g., F-measure is as high as 0.74 for binary classification setup for distinguishing between verified and deceptive content identified as disinformation and propaganda. Our qualitative analysis of model performance, that goes beyond the F-score, performed using a novel interactive tool ERRFILTER1 allows a user to characterize text and image traits of suspicious news content and analyze patterns of errors made by the various models, which in turn will inform the design of future deceptive news prediction models. Copyright © 2019, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Neural network models

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Volatile Memory Extraction-Based Approach for Level 0-1 CPS Forensics

Volatile Memory Extraction-Based Approach for Level 0-1 CPS ...

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2019 IEEE International Symposium on Technologies for Homeland security, HST 2019

作者： Awad, Rima Asmar Lopez, Juan Rogers, Mike National Security Sciences Direct. Oak Ridge National Laboratory Cyber and Data Analytics Division Oak RidgeTN United States Collage of Engineering Tennessee Technological University Computer Science Department CookevilleTN United States

ISBN: (数字)9781728150925

ISBN: (纸本)9781728150925

Most security analyzers operate on system state that is far removed from end-point components in cyber-physical systems (CPS) identified as level 0-1 in the Purdue Architecture Reference Architecture (PERA) [1]. For example, many operate on system logs and other data dumps to disks. Tremendous value that can be gained in cyber security forensics if low level details such as dynamic changes to volatile memory can be extracted and provided to more sophisticated analysis tools. However, obtaining detailed and dynamic system state at the level of volatile memory is extremely challenging [2]. Here, we attempt to apply IT memory forensic mechanisms to CPS end-point devices and statistically evaluate them. Our focus is to extract volatile and dynamically changing internal information form CPS 0-1 level devices, and design preliminary schemes to exploit that extracted information. This new capability of generating a sequence of volatile memory snapshots for offline, detailed and sophisticated analysis opens a new class of cyber security schemes for CPS forensic analysis. As a case study for our ongoing research, we apply the proposed methodology to Modicon PLC using Modbus protocol. We extract the memory layout and subject the device to read operations at the most critical regions of memory. Similarly, write operations are initiated to carefully determine memory locations (for example, bytes that hold the firmware version number). This capability of generating a sequence of volatile memory snapshots for offline, detailed and sophisticated analysis opens a new class of cyber security schemes for CPS forensic analysis. Also, the ability to dynamically make controlled modifications to specific memory locations opens the potential for new mechanisms such as taint analysis and watermarking. © 2019 IEEE.

关键词： Dynamics

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Comparative analysis of SIMILAR to RCD ONE(SRO) family from tetraploid cotton species and their diploid progenitors depict their significance in cotton growth and development

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Journal of Cotton Research 2024年第4期 438页

作者： SHABAN Muhammad TABASSUM Riaz RANA Iqrar Ahmad ATIF Rana Muhammad AZMAT Muhammad Abubakkar IQBAL Zubair MAJEED Sajid AZHAR Muhammad Tehseen Department of Plant Breeding and Genetics University of Agriculture Faisalabad Sub-Campus Burewala Department of Chemistry and Biochemistry University of AgricultureFaisalabad Centre of Agricultural Biochemistry and Biotechnology University of AgricultureFaisalabad Department of Plant Breeding and Genetics University of AgricultureFaisalabad Precision Agriculture and Analytics Lab National Centre in Big Data and Cloud Computing Centre for Advanced Studies in Agriculture and Food Security University of Agriculture Faisalabad Department of Plant Pathology University of California State Key Laboratory of Cotton Biology Institute of Cotton ResearchChinese Academy of Agricultural Sciences Federal Seed Certification and Registration Department Ministry of National Food Security and Research School of Agriculture Sciences Zhengzhou University

Background SRO(Similar to RCD1) genes family is largely recognized for their importance in the growth,development,and in responding to environmental ***,genome-wide identification and functional characterization of SRO genes from cotton species have not been reported so *** A total of 36 SRO genes were identified from four cotton *** analysis divided these genes into three groups with distinct *** and chromosomal distribution analysis indicated uneven distribution of GaSRO,GrSRO,GhSRO,and GbSRO genes on A2,D5 genomes,Gh-At,Gh-Dt,Gb-At,and Gb-Dt subgenomes,*** duplication analysis revealed the presence of six duplicated gene pairs among GhSRO *** promoter analysis,several elements responsive to the growth,development and hormones were found in GhSRO genes,implying gene induction during cotton growth and *** miRNAs responsive to plant growth and abiotic stress were predicted to target12 GhSRO ***-specific expression profiling demonstrated the roles of GhSRO genes in one or more *** addition,specific expression pattern of some GhSRO genes during ovule development depicted their involvement in these developmental *** The data presented in this report laid a foundation for understanding the classification and functions of SRO genes in cotton.

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Predicting foreign language usage from english-only social media posts

Predicting foreign language usage from english-only social m...

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2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2018

作者： Volkova, Svitlana Ranshous, Stephen Phillips, Lawrence Data Sciences and Analytics National Security Dirct. Pacific Northwest National Laboratory 902 Battelle Blvd RichlandWA99354 United States

ISBN: (纸本)9781948087292

Social media is known for its multi-cultural and multilingual interactions, a natural product of which is code-mixing. Multilingual speakers mix languages they tweet to address a different audience, express certain feelings, or attract attention. This paper presents a largescale analysis of 6 million tweets produced by 27 thousand multilingual users speaking 12 other languages besides English. We rely on this corpus to build predictive models to infer non-English languages that users speak exclusively from their English tweets. Unlike native language identification task, we rely on large amounts of informal social media communications rather than ESL essays. We contrast the predictive power of the state-of-theart machine learning models trained on lexical, syntactic, and stylistic signals with neural network models learned from word, character and byte representations extracted from English only tweets. We report that content, style and syntax are the most predictive of non- English languages that users speak on Twitter. Neural network models learned from byte representations of user content combined with transfer learning yield the best performance. Finally, by analyzing cross-lingual transfer - the influence of non-English languages on various levels of linguistic performance in English, we present novel findings on stylistic and syntactic variations across speakers of 12 languages in social media. © 2018 Association for Computational Linguistics.

关键词： Neural network models

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