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A Novel Sum-Product detection Algorithm for Faster-Than-Nyquist Signaling: A Deep Learning Approach

IEEE TRANSACTIONS ON COMMUNICATIONS 2021年第9期69卷 5975-5987页

作者： Liu, Bryan Li, Shuangyang Xie, Yixuan Yuan, Jinhong Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2033 Australia

A deep learning assisted sum-product detection algorithm (DL-SPDA) for faster-than-Nyquist (FTN) signaling is proposed in this paper. The proposed detection algorithm works on a modified factor graph which concatenates a neural network function node to the variable nodes of the conventional FTN factor graph to approach the maximum a posterior probabilities (MAP) error performance. In specific, the neural network performs as a function node in the modified factor graph to deal with the residual intersymbol interference (ISI) that is not considered by the conventional detector with a limited complexity. We modify the updating rule in the conventional sum-product algorithm so that the neural network assisted detector can be complemented to a turbo equalization receiver. Furthermore, we propose a compatible training technique to improve the detection performance of the proposed DL-SPDA with turbo equalization. In particular, the neural network is optimized in terms of the mutual information between the transmitted sequence and the extrinsic information. We also investigate the maximum-likelihood bit error rate (BER) performance of a finite length coded FTN system. Simulation results show that the error performance of the proposed algorithm approaches the MAP performance, which is consistent with the analytical BER.

关键词： Artificial neural networks Detectors Training detection algorithms Complexity theory Receivers Signal processing algorithms Faster-than-Nyquist signaling signal detection deep learning

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Generative Threat Variants for detection Tools Assessment

IEEE Reliability Magazine

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IEEE Reliability Magazine 2024年第3期1卷 43-53页

作者： Shiuhpyng Winston Shieh Shanhsin Lee Mengru Tsai Ping-Ching Chen Yu-Tung Lin Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan Woodpecker Technology Inc Hsinchu Taiwan

Advanced persistent threats (APTs) and ransomware attacks are sophisticated cyberattacks orchestrated by skilled threat actors, capable of persisting undetected within a victim’s network for extended periods. These attacks pose significant challenges to network defense, often resulting in substantial financial and reputational losses for targeted enterprises. Typically involving multiple stages, these attacks leverage highly sophisticated techniques and stealth to effectively impact their targets. Enterprises targeted by such attacks continually strengthen their defenses against these critical cyber threats. Common solutions like antivirus software, extended detection and response (XDR), and security information and event management (SIEM) systems are dedicated to detecting and responding to potential threats. Through early threat identification, effective response strategies, and mitigation efforts, enterprises aim to minimize damage to their networks and sensitive data. However, hackers have become adept at generating threat variants to evade detection through indicators of compromise (IOCs) or indicators of behavior (IOBs) [1] . For instance, software packers and obfuscators can alter a file’s hash value, circumventing IOC-based detection methods. Additionally, hackers may disguise themselves as legitimate browser applications, utilizing domain name system (DNS) or hyper text transfer protocol secure (HTTPS) protocols for command and control (C&C) communication. These evasion techniques often render detection tools ineffective in lab environments, leading to a barrage of false alarms and alert fatigue. Furthermore, investigations by Microsoft and Symantec reveal that hackers can operate undetected for over six months in most APT incidents. This discrepancy underscores the misalignment between existing detection assessment methods and the complex realities of real-world attacks, highlighting a significant gap in current detection capabilities [2] .

关键词： Computer hacking Payloads Emulation Task analysis Ransomware Threat assessment Cyberattack detection algorithms

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detection of Community Structures in Dynamic Social Networks Based on Message Distribution and Structural/Attribute Similarities

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IEEE ACCESS 2021年 9卷 67028-67041页

作者： Zardi, Hedia Alharbi, Bushra Karamti, Walid Karamti, Hanen Alabdulkreem, Eatedal Qassim Univ Dept Comp Sci Coll Comp Buraydah 51452 Saudi Arabia Qassim Univ Coll Comp BIND Res Grp Buraydah 51452 Saudi Arabia Princess Nourah Bint Abdulrahman Univ Comp Sci Dept Coll Comp & Informat Sci Riyadh 11671 Saudi Arabia

Community detection is a crucial challenge in social network analysis. This task is important because it gives leads to study emerging phenomena. Indeed, it makes it possible to identify the different communities representing individuals with common interests and/or strong connections between them. In addition, it allows tracking the transformation of these communities over time. In this work, we propose a dynamic community detection approach called Attributes, Structure, and Messages distribution-based approach (ASMsg). In addition to the node attributes and the topological structure of the network, we use the rate of transferred messages as the key concept of this approach. Therefore, we obtain communities with similar members that are strongly connected and also frequently interacting. Furthermore, the proposed approach is able to detect all possible communities' transformations even if the communities are overlapped. To demonstrate its efficiency, we widely test ASMsg on artificial and real-world dynamic networks and compare it with representative methods. The results show the superiority of our approach in terms of detected communities.

关键词： Image edge detection Social networking (online) Heuristic algorithms Partitioning algorithms Licenses Support vector machines detection algorithms Community detection dynamic social networks attributed networks community evolution overlapping communities

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Density Sensitive Random Walk for Local Community detection

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IEEE ACCESS 2021年 9卷 27773-27782页

作者： Yi, Yuqi Jin, Luhua Yu, Heng Luo, Haoran Cheng, Fan Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ AI Inst MoE Key Lab Artificial Intelligence Shanghai 200240 Peoples R China

Given a network, local community detection aims at finding the community that contains a set of query nodes (seed nodes). Random walk (RW) based algorithms have shown great success in various local community detection scenarios. Starting from the seed nodes, RW based algorithms continuously sample random walk paths to get the clustering result. However, current RW based algorithms for local clustering are faced with the following two problems. The random walker is insensitive to the community boundary and might have an unbalanced walk. These problems would have a negative effect on the clustering result of RW based algorithms. In this paper, we propose a density sensitive random walk algorithms (DSRW) for local community detection. By integrating the graph density information into the random walk process, the problems are resolved and the clustering result is improved. We provide the convergence proof of DSRW algorithm and perform extensive experiments on both real and synthetic datasets. Results show that our DSRW algorithm has achieved the state-of-the-art result in most scenarios.

关键词： Clustering algorithms detection algorithms Convergence Mathematical model Licenses Probability distribution Linear programming Local community detection random walk density sensitive

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Mining Stable Communities in Temporal Networks by Density-Based Clustering

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IEEE TRANSACTIONS ON BIG DATA 2022年第3期8卷 671-684页

作者： Qin, Hongchao Li, Rong-Hua Wang, Guoren Huang, Xin Yuan, Ye Yu, Jeffrey Xu Northeastern Univ Dept Comp Sci Shenyang 110004 Liaoning Peoples R China Beijing Inst Technol Dept Comp Sci Beijing 100081 Peoples R China Hong Kong Baptist Univ Dept Comp Sci Hong Kong Peoples R China Chinese Univ Hong Kong Dept Syst Engn & Engn Management Hong Kong Peoples R China

Community detection is a fundamental task in graph data mining. Most existing studies in contact networks, collaboration networks, and social networks do not utilize the temporal information associated with edges for community detection. In this article, we study a problem of finding stable communities in a temporal network, where each edge is associated with a timestamp. Our goal is to identify the communities in a temporal network that are stable over time. To efficiently find the stable communities, we develop a new community detection algorithm based on the density-based graph clustering framework. We also propose several carefully-designed pruning techniques to significantly speed up the proposed algorithm. We conduct extensive experiments on four real-life temporal networks to evaluate our algorithm. The results demonstrate the effectiveness and efficiency of the proposed algorithm.

关键词： Clustering algorithms Big Data Collaboration Image edge detection detection algorithms Computer science Data mining Community detection temporal networks stable community structural graph clustering density-based clustering

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DET: Enabling Efficient Probing of IPv6 Active Addresses

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IEEE-ACM TRANSACTIONS ON NETWORKING 2022年第4期30卷 1629-1643页

作者： Song, Guanglei Yang, Jiahai Wang, Zhiliang He, Lin Lin, Jinlei Pan, Long Du, Chenxin Quan, Xiaowen Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol Beijing 100084 Peoples R China Tsinghua Univ Inst Network Sci & Cyberspace Beijing 100084 Peoples R China

Fast IPv4 scanning significantly improves network measurement and security research. Nevertheless, it is infeasible to perform brute-force scanning of the IPv6 address space. Alternatively, one can find active IPv6 addresses through scanning the candidate addresses generated by state-of-the-art algorithms. However, the probing efficiency of such algorithms is often very low. In this paper, our objective is to improve the probing efficiency of IPv6 addresses. We first perform a longitudinal active measurement study and build a high-quality dataset, hitlist, including more than 1.95,B IPv6 addresses distributed in 58.2,K BGP prefixes and collected over 17 months period. Different from the previous works, we probe the announced BGP prefixes using a pattern-based algorithm. This results in a dataset without uneven address distribution and low active rates. Further, we propose an efficient address generation algorithm, DET, which builds a density space tree to learn high-density address regions of the seed addresses with linear time complexity and improves the active addresses' probing efficiency. We then compare our algorithm DET against state-of-the-art algorithms on the public hitlist and our hitlist by scanning 50,M addresses. Our analysis shows that DET increases the de-aliased active address ratio and active address (including aliased addresses) ratio by 10%, and 14%, respectively. Furthermore, we develop a fingerprint-based method to detect aliased prefixes. The proposed method for the first time directly verifies whether the prefix is aliased or not. Our method finds that 10.64% of the public aliased prefixes are false positive.

关键词： Internet IEEE transactions detection algorithms Velocity measurement Soft sensors Resource management Probes IPv6 hitlist address probing network measurement aliased prefix

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Comprehensive Review on Facemask detection Techniques in the Context of Covid-19

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IEEE ACCESS 2021年 9卷 106839-106864页

作者： Nowrin, Afsana Afroz, Sharmin Rahman, Md. Sazzadur Mahmud, Imtiaz Cho, You-Ze Bangladesh Univ Profess Dept Informat & Commun Technol ICT Dhaka 1216 Bangladesh Jahangirnagar Univ Inst Informat Technol Dhaka 1342 Bangladesh Kyungpook Natl Univ Sch Elect & Elect Engn Daegu 41566 South Korea

The outbreak of Coronavirus Disease 2019 (Covid-19) had an enormous impact on humanity. Till May 2021, almost 172 million people have been affected globally due to the contagious spread of Covid-19. Although the distribution of vaccines has been started, the worldwide mass distribution is yet to happen. According to the World Health Organization (WHO), wearing a facemask can reduce the contagious spread of Covid-19 significantly. The governments of different countries have recommended implementing the "no mask, no service" method to impede the spread of Covid-19. However, even the improper wearing of a facemask can obstruct the goal and lead to the spread of the virus. Therefore, to ensure public safety, a system for monitoring facemasks on faces, commonly known as a facemask detection algorithm, is essential for overcoming this crisis. The facemask detection algorithms are part of the object detection algorithms which are used to detect objects in an image. Among the various object detection algorithms, deep learning showed tremendous performance in facemask detection for its excellent feature extraction capability than the traditional machine learning algorithms. However, there remains a lot of scope for future research to build an efficient facemask detection system. Therefore, this study aims to draw attention to the researchers by providing a narrative and meta-analytic review on all the published works related to facemask detection in the context of Covid-19. Because facemask detection algorithms are run mainly by adopting object detection algorithms, this paper also explores the progress of object detection algorithms over the last few decades. A comprehensive analysis of different datasets used in facemask detection techniques by many studies has been explored. The performance comparison among these algorithms is discussed in narrative and meta-analytic approaches. Finally, this study concludes with a discussion of some of the major challenges and future sco

关键词： COVID-19 Feature extraction Coronaviruses detection algorithms Object detection Machine learning algorithms Faces Covid-19 convolutional neural network deep neural network facemask detection Covid-19 health object detection machine learning

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Deployment of Information Diffusion for Community detection in Online Social Networks: A Comprehensive Review

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2021年第5期8卷 1083-1107页

作者： Das, Soumita Biswas, Anupam Natl Inst Technol Silchar Dept Comp Sci & Engn Silchar 788010 India

The flow of information through active users in online social networks (OSNs) plays a major role in forming natural social groups, popularly known as communities. Although structural and topological aspects of the network had been central to most of the community detection approaches, incorporation of information flow for community detection has been an emerging topic in the recent past. Often, the flow of information is studied as a traceable process called information diffusion. The flow of information in the network affects various factors like temporal characteristics, network attributes, or social attributes. The information diffusion process helps to extract this information including where and when information is generated and in what fashion the dispersion occurs. Thus, it has the potential to aid the community detection process in social networks. In this article, the deployment of the information diffusion process for community detection has been studied extensively. The study is mainly focused on how information flow affects various network properties and social facets and explored the possibility of deployment for community detection. Various information diffusion models and community detection algorithms have been discussed in the context of network properties and social facets. Current challenges, future directions, and modalities for the deployment of information diffusion in community detection have been discussed. In addition, various widely used datasets, evaluation metrics as well as evaluation methods for evaluating community detection algorithms are also detailed.

关键词： Diffusion processes Social networking (online) Image edge detection detection algorithms Topology Mathematical model Dispersion Community detection disjoint communities information diffusion online social networks (OSNs) overlapping communities

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Finding Core-Periphery Structures With Node Influences

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2022年第2期9卷 875-887页

作者： Shen, Xin Aliko, Sarah Han, Yue Skipper, Jeremy, I Peng, Chengbin Ningbo Univ Coll Informat Sci & Engn Ningbo 315000 Peoples R China London Interdisciplinary Biosci Consortium London WC1E 6BT England UCL Expt Psychol London WC1E 6BT England

Detecting core-periphery structures is one of the outstanding issues in complex network analysis. Various algorithms can identify core nodes and periphery nodes. Recent advances found that many networks from real-world data can be better modeled with multiple pairs of core-periphery nodes. In this study, we propose to use an influence propagation process to detect multiple pairs of core-periphery nodes. In this framework, we assume each node can emit a certain amount of influence and propagate it through the network. Then we identify nodes with large influences as core nodes, and we utilize a maximum influence chain to construct a node-pairing network to determine core-periphery pairs. This approach can take node interactions into consideration and can reduce noises in finding pairs. Experiments on randomly generated networks and real-world networks confirm the efficiency and accuracy of our algorithm.

关键词： detection algorithms Complex networks Image edge detection Periodic structures Stochastic processes Psychology Proteins Core-periphery structure complex networks node influences computational modeling unsupervised learning

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QMIX Aided Routing in Social-Based Delay-Tolerant Networks

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2022年第2期71卷 1952-1963页

作者： Han, Chenchen Yao, Haipeng Mai, Tianle Zhang, Ni Guizani, Mohsen Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Sixth Res Inst China Elect Corp Beijing 100846 Peoples R China Mohamed Bin Zayed Univ Artificial Intelligence MB Machine Learning Dept Abu Dhabi U Arab Emirates

Delay-tolerant network (DTN) is a network that's designed to operate effectively in heterogeneous networks that may lack continuous network connectivity. It is characterized by their lack of instantaneous end-to-end paths, resulting in difficulties in designing effective DTN routing protocols. Traditional routing algorithms largely rely on greedy schemes. Such schemes can not guarantee the packets will be eventually transmitted to their destinations, thereby presenting a poor transmission efficiency. Recently, the social-based method has attracted a large amount of attention in wireless network routing. It can use the community and the centrality information to increase the delivery rate of the whole network. Therefore, in this article, we introduce the social-based mechanism to our DTN routing design. Besides, how the distributed nodes can learn the collaboration strategies is another challenge. Inspired by the recent success of multi-agent learning in online control, we adopt a centralized training and distributed execution learning paradigm and design a hierarchical social-based DTN architecture. Based on this, we propose a collaborative multi-agent reinforcement learning (termed as QMIX) aided routing algorithm.

关键词： Routing Computer architecture Routing protocols detection algorithms Training Heuristic algorithms Relays Dec-POMDP delay-tolerant networks QMIX routing protocol social attributes

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