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Community Hiding by Link Perturbation in Social Networks

IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2021年第3期8卷 704-715页

作者： Chen, Xianyu Jiang, Zhongyuan Li, Hui Ma, Jianfeng Yu, Philip S. Xidian Univ Sch Cyber Engn Xian 710071 Peoples R China Univ Illinois Dept Comp Sci Chicago IL 60607 USA

Complex social network is a kind of relationship system composed of many nodes according to social relations. Community detection helps scholars to understand this network topology and find out meaningful communities. Many scholars are therefore actively exploring new community detection algorithms. However, it brings privacy issues such as the disclosure of personal or group information of community members and goes against an individual or group desire to be hidden. Hence, how to hide a target community in a network to resist the community detection algorithms becomes critical. Given this, this article studies the community hiding problem, which has not been extensively focused so far and aims to properly hide a target community into other communities by perturbing a budget limited number of social links. First, we formalize the community hiding problem and design a gain function. Second, we prove the feasibility of link perturbation operations and propose an efficient algorithm to solve the community hiding problem. Finally, we conduct extensive experiments on varieties of real small- and large-scale social networks and compare our method with other community deception algorithms. The experimental results demonstrate that the proposed algorithm is more efficient than previously proposed algorithms.

关键词： Social networking (online) Perturbation methods detection algorithms Law enforcement Privacy Dispersion Security Community deception community detection community hiding community security link perturbation

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YOLOv5 Water Surface Garbage detection Algorithm Based on Deep Separable Convolution

YOLOv5 Water Surface Garbage Detection Algorithm Based on De...

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International Conference on Mechanical and Electronics Engineering (ICMEE)

作者： Xuchun Wu Ping Deng Peng Wang Tianli Ma Mountain River Basin Management Office of kashi area Xin Jiang China Electronic Information Engineering Xi'an Technological University Xi'an China

ISBN: (数字)9798350307146

ISBN: (纸本)9798350307153

The challenges posed by multiple parameters, high computational complexity, and a single convolutional kernel in YOLOv5 lead to subpar accuracy and slow detection rates when applied to water surfaces. To address these issues, we propose a novel YOLOv5 water surface target detection algorithm based on deep separable convolution. This algorithm employs deep separable convolution to reconstruct the Darknet53 network, effectively reducing computational complexity. Additionally, we integrate the Inception network structure into the detection network, elevating both complexity and detection accuracy. Evaluation of the algorithm on the dataset reveals outstanding performance, boasting an impressive 95.3% accuracy and a rapid detection rate of 65.34f/s. These results significantly surpass the original YOLOv5 water surface garbage detection algorithm, demonstrating the efficacy of our proposed method.

关键词： YOLO Training Surface reconstruction Convolution Refining Environmental monitoring detection algorithms

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Add Anchor to the Anchor-Free Object detection Algorithm

Add Anchor to the Anchor-Free Object Detection Algorithm

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Artificial Intelligence Technology (ACAIT), Asian Conference on

作者： Heng Song Nan Hu Tianbao Geng Anhui Shuzhi Construction Research Institute Co. Ltd China Railway No.4 Engineering Group Co. Ltd Hefei China

ISBN: (数字)9798350359145

ISBN: (纸本)9798350359152

Anchor free framework is more in line with the principle of visual semantics, but it falls into the problem of visual regression of the target shape (width, height and rotation angle) directly, with low accuracy. In this work, we propose a method to improve the accuracy of anchor-free frames by using anchor techniques. On the basis of object location information estimation, only a few anchor-assisted target shape regressions are generated. The method integrates preset boxes into anchor-free framework skillfully and avoids the huge number of anchors in anchor-based scheme. In addition, we have implemented two detection algorithms for horizontal and rotating frames. Compared with the existing anchor-based and anchor-free schemes, our scheme achieves an optimal balance between detection accuracy and computation. The experiments on the two public data sets, MS COCO and HRS2016, fully showed that the precision of our AP reached state-of-the-art.

关键词： Visualization Costs Shape Semantics Estimation Object detection detection algorithms

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LOVD: Land Vehicle detection in Complex Scenes of Optical Remote Sensing Image

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2022年 60卷

作者： Yan, Puti Liu, Xinrui Wang, Feng Yue, Chengfei Wang, Xin Harbin Inst Technol Dept Aerosp Engn Harbin 150001 Peoples R China Harbin Inst Technol Shenzhen Inst Space Sci & Appl Technol ISSAT Shenzhen 518055 Peoples R China Tsinghua Univ Dept Comp Sci & Technol Beijing 100084 Peoples R China

Nowadays, there is a growing body of research about object detection in remote sensing data. However, the detection algorithms for small targets in remote sensing areas are inadequate, largely because of the unavailability of high-quality datasets. Most remote sensing datasets are comprehensive, which means that they include bridges, airplanes, and lots of other common categories. Compared with other categories, the number and diversity of weak objects, such as vehicles, are quite insufficient. These limitations greatly affect the detection of small targets in remote sensing images. In order to promote the development of algorithms for the detection of small targets in remote sensing images and also allow access to remote sensing data, we have established a large-scale dataset for the detection of vehicle targets in optical remote sensing images and called it LOVD. It contains 1196 pictures and 541751 instances, covering 13 categories. For the dataset, we have proposed in this article: 1) it is the largest one in terms of vehicle category and the total number of vehicle instances;2) it contains images with various backgrounds in different weather and scenarios;and 3) all targets are marked by oriented bounding boxes (OBBs), and two label formats are provided. Finally, we test the state-of-the-art detection algorithms on our dataset and provide a benchmark for OBB detection.

关键词： Remote sensing Meteorology Training Optical imaging Object detection Detectors detection algorithms Dataset remote sensing vehicle detection

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High-knowledge shilling attack detection method based on genetic co-forest

High-knowledge shilling attack detection method based on gen...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Lingvue Su Yongli Wang School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

Shilling attack is the behavior of malicious users to elevate or suppress ratings in collaborative filtering recommendation system, which can destroy the balance of recommendation and disrupt the order of competition. In previous studies, shilling attack detection techniques can be divided into three categories according to the labeling scale of samples: supervised, semi-supervised and unsupervised methods. The semi-supervised method is better at using a small number of labeled samples and a large number of unlabeled samples to improve the detection efficiency. However, in practice, it is found that the existing semi-supervised methods perform poorly in the face of high-knowledge shilling attacks such as PUA and PIA, and the generalization ability is significantly reduced when the proportion of labeled data becomes smaller. Therefore, this paper proposes a semi-supervised detection method GA-CoF. Different from the existing divergent semi-supervisors, the proposed method regards the solution set of unlabeled samples as gene sequences, the error rate as the fitness function, and the aggregation degree as the penalty term. In the experiments, we extract the user behavior characteristics of standard shilling attacks, and compare our method with four representative shilling attack detection algorithms on two datasets, MovieLens-25M and Amazon-5Cores. The results show that our method can maintain high F1 score and is not sensitive to the attack scale when facing high-knowledge shilling attacks, and performs well on real datasets. The proposed method has good module plugging ability and expansion prospects.

关键词： Privacy Error analysis Collaborative filtering Genetics Security Labeling detection algorithms

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An Overlapping Community detection Algorithm for Label Propagation Based on Node Influence 3

An Overlapping Community Detection Algorithm for Label Propa...

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2021年第三届先进计算机技术、信息科学与通信国际会议(CTISC2021)

作者： QI LIU YAOFENG SU QICHAO PENG KEBIN CHEN YUNJUN LU College of Information and Communication National University of Defense Technology

ISBN: (纸本)9781665418683

LPA algorithm is an effective community detection algorithm. based on the original algorithm, a new algorithm based on node importance, similarity between nodes, influence of adjacent nodes and label propagation strategy is proposed in view of the randomness and instability of overlapping community detection in LPA algorithm. First, the importance of nodes, the similarity between nodes, the influence of adjacent nodes are calculated, and the label set of nodes is generated according to the calculation results. Through the label update strategy, the attribution coefficient of each node to the community is calculated, and the updated label set is iterated continuously. The results show that the algorithm has near linear time complexity and can effectively improve the accuracy and stability of large-scale overlapping community detection.

关键词： Information science Complex networks detection algorithms Time complexity

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Design and Implementation of Mask detection System Based on Improved YOLOv5s

Design and Implementation of Mask Detection System Based on ...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Changyu Zhao Zhuo Yan Huangxin Xu Xueliang Chen Zheng Li Xinyu Zhong Cuiwei Liu Anyan Xiao Xingyan Lv School of Artificial Intelligence Shenyang Aerospace University Shenyang China School of Computer Science Shenyang Aerospace University Shenyang China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

In this paper, we propose a lightweight mask detection algorithm and implement an intelligent vehicle system. The algorithm uses YOLOv5s as the backbone network, and at the same time incorporates the SE attention mechanism to optimize the timeliness, and is finally deployed on an intelligent vehicle system with BCM2711 as the control platform. Experiments prove that the algorithm proposed in this paper reduces the detection time by 30% while ensuring a higher MAP, which has certain value for promotion.

关键词： YOLO Privacy Intelligent vehicles Real-time systems Security Task analysis detection algorithms

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Efficient Correlation Power Analysis (CPA) Focusing on Byte-Wise Calculation Points

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IEEE ACCESS 2021年 9卷 74275-74285页

作者： Jeon, Youngbae Jung, Ji Hyuk Yoon, Ji Won Korea Univ Sch Cybersecur Seoul 02841 South Korea

Correlation Power Analysis (CPA) uses a lot of computational resources for deploying side-channel analysis to power traces. For every guessing key, it searches all data points and all power traces to reveal the secret key implemented in the encryption module. In this work, we propose a novel technique to narrow down the target region for applying CPA to the power traces. As byte-wise operations occur in every clock period, the assumed amplitude value based on the power consumption model and the actual amplitude are most correlated at the points where the peaks are observed. We extract the calculation points of each power trace using signal processing and machine learning techniques, and select the region where the CPA will be applied to. Our proposed approach achieves an approximately 20% improvement in accuracy and requires less computational resources compared to conventional CPA.

关键词： Clocks Power demand Correlation Encryption Power measurement Time complexity detection algorithms Side-channel analysis correlation power analysis simple power analysis peak detection k-means algorithm AES

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IMDoC: Identification of Malicious Domain Campaigns via DNS and Communicating Files

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IEEE ACCESS 2021年 9卷 45242-45258页

作者： Lazar, David Cohen, Kobi Freund, Alon Bartik, Avishay Ron, Aviv Ben Gurion Univ Negev Dept Comp Sci IL-8410501 Beer Sheva Israel Ben Gurion Univ Negev Sch Elect & Comp Engn IL-8410501 Beer Sheva Israel IBM Cyber Secur Ctr Excellence IL-8489325 Beer Sheva Israel

Cyber attacks have become more sophisticated and frequent over the years. Detecting the components operated during a cyber attack and relating them to a specific threat actor is one of the main challenges facing cyber security systems. Reliable detection of malicious components and identification of the threat actor is imperative to mitigate security issues by Security Operations Center (SOC) analysts. The Domain Name System (DNS) plays a significant role in most cyber attacks observed nowadays in that domains act as a Command and Control (C&C) in coordinated bot network attacks or impersonate legitimate websites in phishing attacks. Thus, DNS analysis has become a popular tool for malicious domain identification. In this collaborative research associating Ben-Gurion University and IBM, we develop a novel algorithm to detect malicious domains and relate them to a specific malware campaign in a large-scale real-data DNS traffic environment, dubbed Identification of Malicious Domain Campaigns (IMDoC) algorithm. Its novelty resides in developing a framework that combines the existence of communicating files for the observed domains and their DNS request patterns in a real production environment. The analysis was conducted on real data from Quad9 (9.9.9.9) DNS recursive resolvers combined with malicious communicating files extracted from VirusTotal, and confirms the strong performance of the algorithm on a real large-scale data production environment.

关键词： Malware Servers IP networks Security Production Prediction algorithms Reliability Cyber security domain name system (DNS) clustering methods detection algorithms

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Evaluating and Comparing the IGraph Community detection algorithms

Evaluating and Comparing the IGraph Community Detection Algo...

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Brazilian Conference on Intelligent Systems (BRACIS)

作者： Fabiano Berardo de Sousa Liang Zhao Inst. of Math. & Comput. Sci. Universidade de Sao Paulo Sao Paulo SÃ£o Paulo BR School of Philosophy Sciences and Literatures University of São Paulo Ribeirão Preto SP Brazil

Complex networks became a very important tool in machine learning field, helping researchers to investigate and mine data. They can model real dynamic networks, aiding to unveil information's about the systems they model. Communities are notable groups that may exist in a complex network and the community detection problem is the focus of attention of many researchers. The igraph library implements a good set of community detection algorithms, allowing researchers to easily apply them to data mining tasks. But each algorithm uses a different approach, leading to different performances. In this paper, the community detection algorithms implemented in the igraph library are investigated and ranked according to their performances in a set of different scenarios. Results show walktrap and multi-level got the highest scores while leading eigenvector and spinglass got the lowest ones. These findings are an important contribution for aiding researchers to select or discard algorithms in their own experiments using igraph library.

关键词： Communities Mathematical model Libraries Equations Graphics Algorithm design and analysis detection algorithms

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