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Identifying malicious traffic under concept drift based on intraclass consistency enhanced variational autoencoder

science China(Information sciences) 2024年第8期67卷 238-252页

作者： Xiang LUO Chang LIU Gaopeng GOU Gang XIONG Zhen LI Binxing FANG Institute of Information Engineering Chinese Academy of Sciences School of Cyber Security University of Chinese Academy of Sciences School of Computer Science and Technology Harbin Institute of Technology (Shenzhen)

Accurate identification of malicious traffic is crucial for implementing effective defense countermeasures and has led to extensive research efforts. However, the continuously evolving techniques employed by adversaries have introduced the issues of concept drift, which significantly affects the performance of existing methods. To tackle this challenge, some researchers have focused on improving the separability of malicious traffic representation and designing drift detectors to reduce the number of false ***, these methods often overlook the importance of enhancing the generalization and intraclass consistency in the representation. Additionally, the detectors are not sufficiently sensitive to the variations among different malicious traffic classes, which results in poor performance and limited robustness. In this paper, we propose intraclass consistency enhanced variational autoencoder with Class-Perception detector(ICE-CP) to identify malicious traffic under concept drift. It comprises two key modules during training:intraclass consistency enhanced(ICE) representation learning and Class-Perception(CP) detector construction. In the first module, we employ a variational autoencoder(VAE) in conjunction with Kullback-Leibler(KL)-divergence and cross-entropy loss to model the distribution of each input malicious traffic flow. This approach simultaneously enhances the generalization, interclass consistency, and intraclass differences in the learned representation. Consequently, we obtain a compact representation and a trained classifier for nondrifting malicious traffic. In the second module, we design the CP detector, which generates a centroid and threshold for each malicious traffic class separately based on the learned representation, depicting the boundaries between drifting and non-drifting malicious traffic. During testing, we utilize the trained classifier to predict malicious traffic classes for the testing samples. Then, we use the CP det

关键词： concept drift malicious traffic identification variational autoencoder intrusion detection cyberspace security

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security Analysis of OAuth 2.0 Implementation

Security Analysis of OAuth 2.0 Implementation

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2023 Innovations in Power and Advanced Computing Technologies, i-PACT 2023

作者： Sharma, Swarag Jevitha, K.P. TIFAC-CORE in Cyber Security Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India Department of Computer Science and Engineering Amrita School of Computing Coimbatore Amrita Vishwa Vidyapeetham India

ISBN: (纸本)9798350325188

Many users all over the world routinely use open authentication and authorization providers based on OAuth 2.0 framework such as Google, Facebook etc. to sign in to third-party websites (relying party). The authentication (OpenID connect) and authorization (OAuth) protocol based on OAuth 2.0 framework enables the relying party application to authenticate its users and gain access to their protected resources without storing any of the sensitive user credentials such as usernames and passwords. Hence, the implementation of these protocols by the relying parties and identity providers play an important role in security of the user information and their privacy. The risk of attacks on improper implementation of these protocols can cause unauthorized access to the user accounts. In this work, we focused on testing the OAuth implementations done by the relying parties. For the secure implementation of OAuth 2.0 we found that following parameters must be present while generating OAuth request: client_id, state, response_type, redirect_uri and scope. We have analyzed our work with around 75 websites that were using Google as an identity provider to provide access to its users. Out of 75 relying parties, 30% were found vulnerable to CSRF attack due to missing state parameter, 13% used implicit flow which reveals id_token or access_token in the OAuth URL and 2% had secret parameters in OAuth URL. We developed a browser extension which successfully identify and alert its user on these issues. © 2023 IEEE.

关键词： OAuth 2.0 security Analysis of OAuth 2.0 Tokens Unauthorized Access

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AI-driven Dynamic Workload Balancing for Real-time Applications on Cloud Infrastructure 7

AI-driven Dynamic Workload Balancing for Real-time Applicati...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Sreeramulu, Madhusudhan Dasari Mohammed, Abdul Sajid Kalla, Dinesh Boddapati, Nagesh Natarajan, Yuvaraj American Express Department of Cyber Security United States University of the Cumberlands Department of School of Computer and Information Sciences KY United States Colorado Technical University Department of Computer Science CharlotteNC United States Microsoft Department of Computer Science CharlotteNC United States Sri Shakthi Institute of Engineering and Technology Department of Computer Science Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350350067

The specification provides a new, resource-efficient method of Dynamic Workload Balancing in AI-driven Real-time Applications over Cloud Infrastructure. The real-time application keeps processing data at high speeds, and it is too difficult to get or make this type of arrangement using our traditional cloud setups as the system employs artificial intelligence methods responsively reallocate resources between virtual machines in accordion with demanded quality of services slabs in behalf-to-capacity ratio. It is scheduling jobs to resources efficiently and delivering results in a timely fashion, which is necessary for real-time applications like video processing or stock trading. It uses machine learning models based on historical data to predict the resources required for upcoming tasks. These predictions are subsequently utilized to smartly assign resources across VMs, factoring in network latencies and inter-dependence. It allows the system to modify queues in real-time according to changes in workload patterns, performing a dynamic load balancing. © 2024 IEEE.

关键词： Adversarial machine learning

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Enhancing Gender Prediction Model Performance through Automatic Individual Entity Extraction and Class Balance

Enhancing Gender Prediction Model Performance through Automa...

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2025 IEEE International Conference on Big Data and Smart Computing, BigComp 2025

作者： Kim, Chaeyun Kim, Eunseo Kim, Yeonhee Sim, Jaehyeong Kim, Jongkil Ewha Womans University Artificial Intelligence Convergence Seoul Korea Republic of Ewha Womans University Department of Computer Science and Engineering Seoul Korea Republic of Ewha Womans University Department of Cyber Security Seoul Korea Republic of

To build a model that predicts the gender of named entities in text, a high-quality labeled dataset is required, which requires considerable manual effort and time. This paper proposes two major contributions to address this issue. First, we develop a mechanism to automatically extract individual entities from sentences using Named Entity Recognition (NER) and Part-of-Speech (PoS) tagging techniques. This approach automates the data generation process and reduces costs. Second, we ensure class balance in the dataset to optimize model performance. Experimental results demonstrate that the automated data generation method and balanced dataset significantly enhance the performance of the gender prediction model. This work makes a substantial contribution to data generation and the improvement of model performance in Natural Language Processing (NLP) tasks. © 2025 IEEE.

关键词： Natural language processing systems

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Text Summarization Generation Based on Improved Transformer Model

Text Summarization Generation Based on Improved Transformer ...

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2023 IEEE International Conference on Dependable, Autonomic and Secure Computing, 2023 International Conference on Pervasive Intelligence and Computing, 2023 International Conference on Cloud and Big Data Computing, 2023 International Conference on cyber science and Technology Congress, DASC/PiCom/CBDCom/cyberSciTech 2023

作者： Lin, Jingsong Guo, Xiaodong Dong, Chen Lyu, Chenxi Xu, Li Chen, Zhenyi College of Computer and Data Science Fuzhou University Fuzhou350116 China College of Computer and Cyber Security Fujian Normal University Fuzhou350007 China University of South Florida Department of Computer Science and Engineering TampaFL33620 United States

ISBN: (纸本)9798350304602

In the era of big data, the number of internet users is increasing yearly, and each user receives a massive amount of information every day. The low-value density of massive text data makes it difficult to utilize its information. In the face of this problem, text summarization generation technology that can extract the paper's leading content and condense the paper's main idea is a powerful solution. In text summarization generation, abstractive summarization techniques with high readability, clear expression, and closer proximity to human language habits are often favored. However, mainstream technologies still have issues like summarizations' detachment from focus. In this paper, keyword information is employed to join the text summarization generation task, and a keyword information module is added to the Transformer to better perform the summarization generation task. Finally, the ROUGE evaluation standard is used to compare the performance of the above models on the large-scale Chinese short text summarization (LCSTS) dataset. The experimental results show that the model generates higher-quality summarizations using keyword information, and the related methods have considerable potential. Future work can explore better model structures, more suitable methods for utilizing keyword information, and more accurate keyword extraction techniques to improve the quality of abstractive summarizations. © 2023 IEEE.

关键词： Large dataset

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Personalized Federated Learning for Heterogeneous Residential Load Forecasting

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Big Data Mining and Analytics 2023年第4期6卷 421-432页

作者： Xiaodong Qu Chengcheng Guan Gang Xie Zhiyi Tian Keshav Sood Chaoli Sun Lei Cui the Shanxi Key Laboratory of Advanced Control and Equipment Intelligence Taiyuan University of Science and TechnologyTaiyuan 030024China the Faculty of Engineering and Information Technology University of Technology SydneyUltimo 2007Australia the Centre for Cyber Security Research and Innovation Deakin UniversityMelbourne 3125Australia the School of Computer Science and Technology Taiyuan University of Science and TechnologyTaiyuan 030024China

Accurate load forecasting is critical for electricity production,transmission,and *** learning(DL)model has replaced other classical models as the most popular prediction ***,the deep prediction model requires users to provide a large amount of private electricity consumption data,which has potential privacy *** nodes can federally train a global model through aggregation using federated learning(FL).As a novel distributed machine learning(ML)technique,it only exchanges model parameters without sharing raw ***,existing forecasting methods based on FL still face challenges from data heterogeneity and privacy ***,we propose a user-level load forecasting system based on personalized federated learning(PFL)to address these *** obtained personalized model outperforms the global model on local ***,we introduce a novel differential privacy(DP)algorithm in the proposed system to provide an additional privacy *** on the principle of generative adversarial network(GAN),the algorithm achieves the balance between privacy and prediction accuracy throughout the *** perform simulation experiments on the real-world dataset and the experimental results show that the proposed system can comply with the requirement for accuracy and privacy in real load forecasting scenarios.

关键词： load forecasting personalized federated learning differential privacy

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Multi-residual generative adversarial networks for QR code Deblurring

Multi-residual generative adversarial networks for QR code D...

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2022 International Conference on Electronic Information Technology, EIT 2022

作者： Wang, Mingyue Chen, Kecheng Lin, Fanqiang College of Computer Science and Cyber Security Chengdu University of Technology Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781510655096

Motion blur and ambient noise are the main reasons that affect quick response (QR) code recognition. In this paper, we propose a novel deep learning approach to deblur the QR codes and realize the effective recognition of deblurring QR codes by using generative adversarial networks (GANs). We estimate the blur kernel and ambient noise of the blur QR code in the dataset using GANs, so as to realize the transformation from the blur QR code image to the sharp image. We also propose an expansion method of QR codes dataset, and achieve better generalization performance of the model. The experimental results show that our approach can effectively estimate the blur kernel and ambient noise that can realize the deblurring of QR code. © 2022 SPIE.

关键词： Generative adversarial networks

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Neuromorphic-Driven Agentic AI for Autonomous Decision-Making Systems 4

Neuromorphic-Driven Agentic AI for Autonomous Decision-Makin...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Manjunath Kamath, K. Samata Mehta, S. Akshaya, H.L. Shilpashree, N. Jadhav, Girish Mitra, Abhijit Department of Cyber Security Karkala India Bengaluru India Sarada Vilas College Department of Computer Science Mysuru India Parul institute of Engineering and Technology Parul University Faculty of Engineering and Technology Department Of Electrical Engineering Post Limda India Pune India

ISBN: (纸本)9798350352931

Agentic AI represents a paradigm shift in the development of intelligent systems capable of adaptive and proactive interactions in dynamic and complex environments. By integrating reinforcement learning (RL) with cognitive frameworks, Agentic AI goes beyond traditional rule-based and reactive models, enabling autonomous systems to make informed decisions, anticipate future states, and learn from experience. This paper explores the theoretical foundations and practical applications of Agentic AI, highlighting its potential to transform a variety of fields, including robotics, autonomous driving, finance, and healthcare. Through a detailed review of state-of-the-art research, we illustrate how cognitive architectures such as ACT-R and Soar, combined with advanced RL techniques like Deep Q-Networks (DQN) and Proximal Policy Optimization (PPO), contribute to the development of AI agents with human-like reasoning and decision-making capabilities. Experimental results demonstrate that Agentic AI significantly outperforms conventional AI approaches in terms of adaptability, learning efficiency, and decision accuracy. The findings suggest that Agentic AI offers a robust framework for creating intelligent systems capable of complex problem-solving, long-term planning, and proactive behavior, paving the way for the next generation of AI-driven applications. © 2024 IEEE.

关键词： Intelligent systems

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An enhanced LSTM-CRF based approach for Knowledge Integration based Sentiment Analysis

An enhanced LSTM-CRF based approach for Knowledge Integratio...

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International Symposium on Advanced Computing and Communication (ISACC)

作者： Allam Balaram M. SilpaRaj K. Likhitha Sree V. Pramodh Kumar D. Vinay Kumar K. Venu Kumar Reddy Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India Department of Computer Science and Engineering (Cyber Security) CVR College of Engineering Hyderabad India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Sentiment analysis is a key element for expert reviews but is at a loss in terms of detection of sarcasm and irony, which distort the actual sentiments. This paper proposes an approach that will fuse syntactic and contextual knowledge to respond to these requirements. Steps for preprocessing include tokenization, stop word removal, lemmatization, followed by feature extraction using BERT embedding. In addressing high-dimensional output, the Relief algorithm is then used to extract the relevant features. The classification model utilizes a combination of Deep Learning for sequence LSTM networks and Maximum entropy methods, i.e., Conditional Random Fields (CRF), and enables sequential dependency learning to give better accuracy. This application of data pertaining to time and context can effectively address subtle nuances such as sarcasm and irony and classify the text as positive, negative, or neutral. It brings several advancements with respect to traditional models, thus improving the understanding of emotion in text.

关键词： Sentiment analysis Accuracy Reviews Viterbi algorithm Syntactics Feature extraction Conditional random fields Tokenization Classification algorithms Long short term memory

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Rapid Prototype Development Approach for Genetic Programming

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Journal of computer and Communications 2024年第2期12卷 67-79页

作者： Pei He Lei Zhang School of Computer Science and Cyber Engineering Guangzhou University Guangzhou China School of Cyberspace Security Guangzhou University Guangzhou China

Genetic Programming (GP) is an important approach to deal with complex problem analysis and modeling, and has been applied in a wide range of areas. The development of GP involves various aspects, including design of genetic operators, evolutionary controls and implementations of heuristic strategy, evaluations and other mechanisms. When designing genetic operators, it is necessary to consider the possible limitations of encoding methods of individuals. And when selecting evolutionary control strategies, it is also necessary to balance search efficiency and diversity based on representation characteristics as well as the problem itself. More importantly, all of these matters, among others, have to be implemented through tedious coding work. Therefore, GP development is both complex and time-consuming. To overcome some of these difficulties that hinder the enhancement of GP development efficiency, we explore the feasibility of mutual assistance among GP variants, and then propose a rapid GP prototyping development method based on πGrammatical Evolution (πGE). It is demonstrated through regression analysis experiments that not only is this method beneficial for the GP developers to get rid of some tedious implementations, but also enables them to concentrate on the essence of the referred problem, such as individual representation, decoding means and evaluation. Additionally, it provides new insights into the roles of individual delineations in phenotypes and semantic research of individuals.

关键词： Genetic Programming Grammatical Evolution Gene Expression Programming Regression Analysis Mathematical Modeling Rapid Prototype Development

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