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10th International Conference on Signal Processing and Integrated networks, SPIN 2023

作者： Ankit, Akash Inder, Shubham Sharma, Anuj Johari, Rahul Vidyarthi, Deo Prakash University School of Information Communication and Technology Guru Gobind Singh Indraprastha University SWINGER: Security Wireless IoT Network Group of Engineering and Research Dwarka New Delhi110078 India MelbourneVIC3000 Australia Jawaharlal Nehru University Parallel and Distributed System Lab School of Computer and Systems Sciences New Delhi110067 India

ISBN: (纸本)9781665490993

In this Internet era, we have become heavily dependent on the World Wide Web and it has become a basic amenity for humans. A normal user can be trapped easily in this vicious web if not careful enough. It is a field that requires users to be on their toes every time they try to connect to this enormous interconnection of networks. Reports and Surveys have said that in the coming years, Cybercrime could become the biggest threat. In this proposed work, the authors have deployed applications and designed malware for the purpose of simulating various kinds of cyber-attacks so that they could study their effects & affects and ultimately devise solutions to resolve the vulnerabilities in a system. These applications included networking concepts & protocols and a software based keylogger which was developed with no malicious intent, but to analyse the architectural view or design of a malware and to study the stages of a malware attack like entry, distribution, exploitation, infection and execution. Efforts have been made to demonstrate spooting attacks with an idea of identifying vulnerabilities and security loopholes in a particular system that can be exploited by an attacker if not resolved within time. © 2023 IEEE.

关键词： network security

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Gender-based Cyberbullying Detection for Under-resourced Bangla Language

Gender-based Cyberbullying Detection for Under-resourced Ban...

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International Conference on Electrical and computer engineering (ICECE)

作者： Hasnain Karim Rabib Mostafa Galib Takia Mosharref Nobo Tanjila Alam Sathi Mohammed Saidul Islam Abu Raihan Mostofa Kamal Md Azam Hossain Department of Computer Science and Engineering Network and Data Analysis Group (NDAG) Islamic University of Technology (IUT) Gazipur Bangladesh

The present study explores the detection of gender discrimination based cyberbullying in under-resourced Bangla language. While being spoken by 230 million people globally and being rich in diversity, the Bengali language lacks computational models and annotated resources for cyberbullying detection. To address this, we created GenDisc, a corpus of Bangla Facebook comments featuring gender-based cyberbullying. This study also presents a framework for cyberbullying detection. In our proposed approach, we used four different models to train a gender-based discriminatory text classifier, followed by an ensembling technique on those four models. Then we compared the individual prediction accuracies with the ensembled prediction accuracy. While training the dataset, we followed the stratified k-fold cross validation technique. We demonstrated that integrating gender-based discrimination variables improve a classifier’s capacity to discriminate against cyberbullying. Our evaluations yielded an accuracy of 68% in gender-based speech detection during cross-validation tests.

关键词： Voice activity detection Training Computational modeling Text categorization Cyberbullying

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Sentiment Analysis of COVID-19 Vaccination in Bangla Language with Code-Mixed Text from Social Media

Sentiment Analysis of COVID-19 Vaccination in Bangla Languag...

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International Conference on Electrical and computer engineering (ICECE)

作者： Mahamudur Rahaman Khan S.M. Nawsad Rahmatullah Md Fuadul Islam Abu Raihan Mostofa Kamal Md Azam Hossain Department of Computer Science and Engineering Network and Data Analysis Group (NDAG) Islamic University of Technology (IUT) Gazipur Bangladesh

The vaccination program which helps avert pandemics is facing new hurdles, including the emergence of hazardous new virus strains and public distrust. Analyzing the sentiment expressed in social media interactions related to vaccines may aid the health authority in implementing public safety procedures and guide the government in developing appropriate policies. The purpose of this research is to identify the public sentiments toward the COVID-19 vaccination in Bangladesh from social media comments. Comments posted on social media platforms often mix formal and informal language known as code-mixed text and do not adhere to any particular grammatical standards. In addition, the Bangla language lacks computational models and annotated resources for sentiment analysis. To overcome this, we created CoVaxBD, a Bangla-English code-mixed and sentiment-annotated corpus of Facebook comments. This paper also proposes a model for sentiment analysis based on the multilingual BERT. It achieves a validation accuracy of around 97.3 % and a precision score of approximately 97.4%.

关键词： COVID-19 Training Sentiment analysis Analytical models Social networking (online) Computational modeling Bit error rate

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Evaluating the Inclusiveness of Artificial Intelligence Software in Enhancing Project Management Efficiency – A review and examples of quantitative measurement methods

Evaluating the Inclusiveness of Artificial Intelligence Soft...

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Artificial Intelligence, computer, Data Sciences and Applications (ACDSA), International Conference on

作者： Vasileios Alevizos Ilias Georgousis Akebu Simasiku Antonis Messinis Sotiria Karypidou Dimitra Malliarou Department of Learning Informatics Managament and Ethics Karolinska Institute Stockholm Sweden Department of Computer Science MLV Research Group International Hellenic University Kavala Greece Department Of Computer Software Engineering Zambia University Ndola Zambia HEDNO SA (Hellenic Electricity Distribution Network Operator) Department of Computer Science MLV Research Group International Hellenic University IntelliSolutions Athens Greece

The escalating integration of Artificial Intelligence (AI) in various domains, especially Project Management (PM), has brought to light the imperative need for inclusivity in AI systems. This paper investigates the role of AI software in augmenting both the inclusiveness and efficiency within the realm of PM. The research pivots around specific criteria that define and measure the inclusiveness of AI in PM, highlighting how AI, when developed with inclusiveness in mind, can significantly enhance project outcomes. However, there are inherent challenges in achieving this inclusiveness, primarily due to biases embedded in AI learning databases and the design and development processes of AI systems. The study offers a comprehensive examination of AI's potential to revolutionize PM by enabling managers to concentrate more on people-centric aspects of their work. This is achieved through AI’s ability to perform tasks such as data collection, reporting, and predictive analysis more consistently and efficiently than human counterparts. However, the incorporation of AI in PM extends beyond mere efficiency; it represents a paradigm shift in the epistemology of PM, calling for a deeper understanding of AI's role and impact on society. Despite these advantages, the adoption of AI comes with significant challenges, particularly in terms of bias and inclusiveness. Biased AI learning databases, which use shared and reusable datasets, often perpetuate initially discriminatory algorithms. Moreover, unconscious biases and stereotypes of AI designers, developers, and trainers can inadvertently influence the behavior of the AI systems they create. This necessitates a paradigmatic shift in how AI systems are developed and governed to ensure they do not replicate or exacerbate existing social inequalities. The research proposes a methodological approach involving the development of criteria for inclusion and exclusion, alongside data extraction, to evaluate the inclusiveness and efficien

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Decision Conflict Resolution Model for Equipment Manufacturing Enterprises Based on Human-machine Coordination

Decision Conflict Resolution Model for Equipment Manufacturi...

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Artificial Intelligence of Things and Systems (AIoTSys), International Conference on

作者： Jian An Hongyi Zhu Feifei Wang Siyuan Wu Xiaolin Gui School of Computer Science and Technology Xi’an Jiaotong University Shaanxi Province Key Laboratory of Computer Network Xi’an China School of Computer Science and Technology Xi’an Jiaotong University Xi’an China Henan Jiaoyuan Engineering Technology Group Co. LTD China

The equipment manufacturing industry plays a pivotal role in determining a country’s overall competitive standing within the manufacturing sector. Nevertheless, conventional equipment manufacturing enterprises frequently grapple with challenges related to untimely information sharing and a lack of synergy among various departments during their operational processes. These issues often lead to analytical complexities and decision-making conflicts. Acknowledging the inherent subjectivity in interdepartmental decision-making, we introduce reinforcement learning as a framework to scrutinize and derive decisions that best align with the enterprise’s requirements. Furthermore, owing to the volatile nature of both internal and external enterprise environments, relying solely on machine-generated decisions typically falls short of meeting the evolving developmental needs of the enterprise. To address this, we advocate for a human-machine collaborative approach to adapt effectively to environmental changes. Consequently, this paper proposes a decision conflict resolution framework tailored specifically for equipment manufacturing enterprises, grounded in the principles of human-machine coordination. Empirical validation through experiments conducted on an openly accessible dataset, sourced from the Allstate Purchase Prediction Challenge, underscores the superiority of our model when it comes to executing decision conflict resolution as compared to existing methodologies.

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DDoS Attack Detection Based on CNN and Federated Learning 9

DDoS Attack Detection Based on CNN and Federated Learning

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9th International Conference on Advanced Cloud and Big Data, CBD 2021

作者： Lv, Dingyang Cheng, Xinyu Zhang, Jinghui Zhang, Wei Zhao, Wei Xu, He School of Computer Science and Engineering Southeast University Nanjing China Digital Department China United Network Communications Group Co. Ltd. China Institute for Artificial Intelligence Hefei Comprehensive National Science Center Hefei China School of Computer Science and Technology Anhui University of Technology Hefei China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (纸本)9781665407458

Distributed Denial of Service (DDoS) attack, which seriously affects the availability of the Internet, is one of the most dangerous network attacks. Machine learning is widely used in DDoS attack detection. However, due to data privacy and transmission overhead problems, the traffic data distributed on different network devices will not be easily concentrated together, which leads to difficulties in model training. In this paper, we combine federated learning (FL) and neural network and propose a FLDDoS system to counter DDoS attacks. We designed a CNN model and a data preprocessing algorithm to fully mine the features of DDoS traffic data. The federated learning framework is used for the model training, which can make full use of the data and computing performance of the terminal devices, and protect the data privacy. In addition, we also consider non-IID cases of the data and use a model mixing approach to provide a more personalized model for each client. The experimental results show that the proposed FLDDoS has a good performance. The accuracy of DDoS attack detection and multiclass classification is 99% and 90%, respectively. And the personalized method can overcome the non-IID problem of data in FL effectively. The accuracy of the CNN model proposed in this paper is 20% higher than that of the traditional model. Under the condition of using the same model, the model accuracy obtained by FL is basically the same as that obtained by centralized training. © 2022 IEEE.

关键词： Denial-of-service attack

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Deep Cut-informed Graph Embedding and Clustering

arXiv

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arXiv 2025年

作者： Ning, Zhiyuan Wang, Zaitian Zhang, Ran Xu, Ping Liu, Kunpeng Wang, Pengyang Ju, Wei Wang, Pengfei Zhou, Yuanchun Cambria, Erik Chen, Chong Computer Network Information Center CAS China University of Chinese Academy of Sciences China Department of Computer Science Portland State University United States Department of Computer and Information Science The State Key Laboratory of Internet of Things for Smart City University of Macau China College of Computer Science Sichuan University China School of Computer Science and Engineering Nanyang Technological University Singapore Future City Lab Terminus Group Singapore

Graph clustering aims to divide the graph into different clusters. The recently emerging deep graph clustering approaches are largely built on graph neural networks (GNN). However, GNN is designed for general graph encoding and there is a common issue of representation collapse in existing GNN-based deep graph clustering algorithms. We attribute two main reasons for such issues: (i) the inductive bias of GNN models: GNNs tend to generate similar representations for proximal nodes. Since graphs often contain a non-negligible amount of inter-cluster links, the bias results in error message passing and leads to biased clustering;(ii) the clustering guided loss function: most traditional approaches strive to make all samples closer to pre-learned cluster centers, which causes a degenerate solution assigning all data points to a single label thus making all samples similar and less discriminative. To address these challenges, we investigate graph clustering from a graph cut perspective and propose an innovative and non-GNN-based Deep Cut-informed Graph embedding and Clustering framework, namely DCGC. This framework includes two modules: (i) cut-informed graph encoding;(ii) self-supervised graph clustering via optimal transport. For the encoding module, we derive a cut-informed graph embedding objective to fuse graph structure and attributes by minimizing their joint normalized cut. For the clustering module, we utilize the optimal transport theory to obtain the clustering assignments, which can balance the guidance of "proximity to the pre-learned cluster center". With the above two tailored designs, DCGC is more suitable for the graph clustering task, which can effectively alleviate the problem of representation collapse and achieve better performance. We conduct extensive experiments to demonstrate that our method is simple but effective compared with benchmarks. © 2025, CC BY.

关键词： Graph embeddings

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A High-Accuracy Unknown Traffic Identification Method Based on Multi-View Contrastive Learning

A High-Accuracy Unknown Traffic Identification Method Based ...

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

作者： Yuwei Xu Zizhi Zhu Chufan Zhang Kehui Song Guang Cheng School of Cyber Science and Engineering Southeast University Nanjing China NSFOCUS Technologies Group Co. Ltd. Purple Mountain Laboratories for Network and Communication Security Nanjing China College of Computer Science Tiangong University Tianjin China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

The technology for AI-based encrypted traffic classification (ETC) is advancing rapidly. However, many current studies are conducted in closed network environments where traffic is classified into pre-determined classes. In the actual network environment, new traffic is constantly emerging, making anomaly detection of unknown traffic a pressing issue. The current research attempts different approaches for unknown traffic identification (UTI) from the perspective of model construction, including UTI based on n-classification, UTI based on multiple 2-classifiction, and UTI baed on (n+1)-classification. The above three approaches mentioned are prone to misclassification and have low accuracy due to issues with threshold setting, poor generalization of binary classifiers, and low credibility of generated samples. Researchers have used contrastive learning (CL) for UTI because of its strengths in feature representation. However, there are two shortcomings in the current studies. First, the feature representation of a single view cannot fully capture different classes of traffic features. Second, the existing CL schemes distinguish whether they belong to the same category by constructing pairs of positive and negative samples, but they are still unable to distinguish between known classes and unknown classes in the feature space, resulting in low accuracy. To address the above shortcomings, we propose UTI-MCL, an unknown traffic identification method using multi-view contrastive learning. Firstly, in terms of feature expression, we extract the packet length sequence and the packet byte sequence respectively to learn a more comprehensive feature representation. Secondly, in terms of model construction, we introduce an anchor and compare the distance with both positive and negative samples to help the model better separate different classes in the feature space, enhancing feature distinction. Furthermore, the distance between samples is optimized through the adaptive weight

关键词： Adaptation models Privacy Accuracy Adaptive systems Contrastive learning Pressing Feature extraction Cryptography Anomaly detection

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Harmonic Current Correction for Common Mode Voltage Reduction and Narrow Pulse Elimination Method in DC-AC Matrix Converter

Harmonic Current Correction for Common Mode Voltage Reductio...

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China Power Supply Society Electromagnetic Compatibility Conference (CPEMC)

作者： Guangcheng Ye Hao Qin Abhishek Kumar Bikash Sah Raj M. Naidoo Yan Deng Research Group for Energy Network Transition (ReGENT) College of Electrical Engineering Zhejiang University Hangzhou China Hochschlue Bonn-Rhein-Sieg & Fraunhofer Institute for Energy Economics and Energy System Technology (IEE) Germany Department of Electrical Electronic and Computer Engineering University of Pretoria Pretoria South Africa

ISBN: (数字)9798331509552

ISBN: (纸本)9798331509569

The expected functionality of a single-stage isolated AC-DC matrix converter involves meeting the multifunctional deployment requirements of different AC/DC systems. Its design facilitates bidirectional power conversion while ensuring electrical isolation. Conversely, recent methods have greatly magnified the complexity of modulation and control programs, neglecting in-depth analysis of mathematical models. This paper presents a novel strategy of symmetric-SVM (S-SVM) to operate in discontinuous conduction mode (DCM), which improves performance in DC bias, narrow pulse, and common mode voltage. In addition, an in-depth study was conducted on the precise correlation between vector modulation ratio and three-phase sinusoidal current. The proposed method effectively reduces total harmonic distortion (THD) in three-phase currents and achieves efficient bidirectional power transmission.

关键词： Support vector machines Total harmonic distortion Statistical analysis Electromagnetic interference Modulation Switches Harmonic analysis Vectors Matrix converters Voltage control

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Distributed Potential iLQR: Scalable Game-Theoretic Trajectory Planning for Multi-Agent Interactions

Distributed Potential iLQR: Scalable Game-Theoretic Trajecto...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Zach Williams Jushan Chen Negar Mehr Department of Electrical and Computer Engineering University of Illinois Urbana-Champaign Urbana IL USA Raytheon BBN Technologies in the Network & Cyber Technologies Group Cambridge MA USA Department of Aerospace Engineering University of Illinois Urbana-Champaign Urbana IL USA

In this work, we develop a scalable, local tra-jectory optimization algorithm that enables robots to interact with other robots. It has been shown that agents' interactions can be successfully captured in game-theoretic formulations, where the interaction outcome can be best modeled via the equilibria of the underlying dynamic game. However, it is typically challenging to compute equilibria of dynamic games as it involves simultaneously solving a set of coupled optimal control problems. Existing solvers operate in a centralized fashion and do not scale up tractably to multiple interacting agents. We enable scalable distributed game-theoretic planning by leveraging the structure inherent in multi-agent interactions, namely, interactions belonging to the class of dynamic potential games. Since equilibria of dynamic potential games can be found by minimizing a single potential function, we can apply distributed and decentralized control techniques to seek equi-libria of multi-agent interactions in a scalable and distributed manner. We compare the performance of our algorithm with a centralized interactive planner in a number of simulation studies and demonstrate that our algorithm results in better efficiency and scalability. We further evaluate our method in hardware experiments involving multiple quadcopters. 1 1 Code Repository - https://***/labicon/dp-ilqr

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