检索结果-内蒙古大学图书馆

Resource investment for DDoS attack resistant SDN: a practical assessment

science China(Information sciences) 2023年第7期66卷 112-129页

作者： Bin YUAN Fan ZHANG Jun WAN Huan ZHAO Shui YU Deqing ZOU Qiangsheng HUA Hai JIN School of Cyber Science and Engineering Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security Shenzhen Huazhong University of Science and Technology Research Institute School of Computer Science and Technology Huazhong University of Science and Technology Cluster and Grid Computing Lab School of Computer Science University of Technology Sydney

Software-defined networks(SDNs) present a novel network architecture that is widely used in various datacenters. However, SDNs also suffer from many types of security threats, among which a distributed denial of service(DDoS) attack, which aims to drain the resources of SDN switches and controllers,is one of the most common. Once the switch or controller is damaged, the network services can be *** defense schemes against DDoS attacks have been proposed from the perspective of attack detection;however, such defense schemes are known to suffer from a time consuming and unpromising accuracy, which could result in an unavailable network service before specific countermeasures are taken. To address this issue through a systematic investigation, we propose an elaborate resource-management mechanism against DDoS attacks in an SDN. Specifically, by considering the SDN topology, we leverage the M/M/c queuing model to measure the resistance of an SDN to DDoS attacks. Network administrators can therefore invest a reasonable number of resources into SDN switches and SDN controllers to defend against DDoS attacks while guaranteeing the quality of service(QoS). Comprehensive analyses and empirical data-based experiments demonstrate the effectiveness of the proposed approach.

关键词： SDN security DDoS attacks resource management queueing theory QoS

来源：评论

学校读者我要写书评

暂无评论

rafPS:A shapley-based visual analytics approach to interpret traffic

引用

Computational Visual Media 2024年第6期10卷 1101-1119页

作者： Zezheng Feng Yifan Jiang Hongjun Wang Zipei Fan Yuxin Ma Shuang-Hua Yang Huamin Qu Xuan Song Department of Computer Science and Engineering Southern University of Science and TechnologyShenzhen 18055China Department of Computer Science and Engineering Hong Kong University of Science and TechnologyHong KongChina Department of Computer Science University of ReadingBerkshire RG66AHUK Center for Spatial Information Science the University of TokyoTokyo 113-00331Japan Shenzhen Key Laboratory of Safety and Security for Next Generation of Industrial Internet Southern University of Science and TechnologyShenzhen 518055China

Recent achievements in deep learning(DL)have demonstrated its potential in predicting traffic *** predictions are beneficial for understanding the situation and making traffic control ***,most state-of-the-art DL models are consi-dered“black boxes”with little to no transparency of the underlying mechanisms for end *** previous studies attempted to“open the black box”and increase the interpretability of generated ***,handling complex models on large-scale spatiotemporal data and discovering salient spatial and temporal patterns that significantly influence traffic flow remain *** overcome these challenges,we present TrafPS,a visual analytics approach for interpreting traffic prediction outcomes to support decision-making in traffic management and urban *** measurements region SHAP and trajectory SHAP are proposed to quantify the impact of flow patterns on urban traffic at different *** on the task requirements from domain experts,we employed an interactive visual interface for the multi-aspect exploration and analysis of significant flow *** real-world case studies demonstrate the effectiveness of TrafPS in identifying key routes and providing decision-making support for urban planning.

关键词： data visualization model interpretation urban planning urban visual analytics

来源：评论

学校读者我要写书评

暂无评论

Enhancing Wireless Sensor Network Data Collection through Aerial Unmanned Vehicles

Enhancing Wireless Sensor Network Data Collection through Ae...

引用

2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Ali Alheeti, Khattab M. Najim, Ali Hamza Satar, Nurhizam Safie Mohd Aliesawi, Salah A. Hashim, Aisha Hassan Abdallah Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq College of Computer and Information Technology Computer Networking Systems Department University of Anbar Anbar Iraq Department of Computer Technical Engineering Al-Diwaniyah Iraq Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia College of Computer and Information Technology Department of Computer Networking Systems University of Anbar Anbar Iraq Faculty of Engineering Department of Electrical and Computer Engineering International Islamic University Malaysia Selangor Malaysia

ISBN: (纸本)9798350369106

This study addresses the critical aspect of data collection within Wireless Sensor Networks (WSNs), which consist of autonomous, compact sensor devices deployed to monitor environmental conditions. These networks have limited processing capacity, storage, short-range communication capabilities, and constrained battery power. A primary challenge in WSN s revolves around data gathering, particularly when employing static sinks, leading to premature energy depletion in nodes near these sinks. Integrating mobile collectors introduces the potential for energy conservation by shortening data routes. This article introduces three distinct algorithms for optimizing data collection in WSN s. Our approach involves an in-depth analysis of diverse strategies to mitigate packet collisions and facilitate the retransmission of lost packets. Our findings underscore the considerable impact that packet collisions can exert on the overall network performance during data gathering within wireless sensor networks. Through empirical exploration and analysis, we highlight the significance of these algorithms in enhancing the efficiency, energy conservation, and reliability of data collection processes within WSNs. This research sheds light on these algorithms' vital role in addressing the pressing challenges of data gathering and network performance optimization in wireless sensor networks. © 2024 IEEE.

关键词： Aircraft accidents

来源：评论

学校读者我要写书评

暂无评论

Multi-Layer Perceptrons and Convolutional Neural Networks Based Side-Channel Attacks on AES Encryption

Multi-Layer Perceptrons and Convolutional Neural Networks Ba...

引用

2023 International Conference on engineering Technology and Technopreneurship, ICE2T 2023

作者： Ahmed, Amjed Abbas Hasan, Mohammad Kamrul Center for Cyber Security Faculty of Information Science and Technology Bangi 43600 Malaysia Department of Computer Techniques Engineering Baghdad Iraq

ISBN: (纸本)9798350304107

Side - channel attacks (SCA) exploit vulnerabilities introduced by insecure implementations in order to get information about the data that is being processed or the data itself via device leakages. The majority of modern attack techniques use AES systems, and several studies have exploited side-channel leakages. Exploiting leakages for the first and final rounds, often known as outer rounds, is frequently the foundation of the strategy. There is a possibility that outer round leakages cannot be attacked due to inadequate countermeasures or device design. The attacker has to focus their attention on the inner rounds. Deep learning side-channel attacks, whether they include profiling or not, have the potential to recover secret keys stored in cryptographic devices that use masked AES. Mask value profiling is being used in more recent techniques for the extraction of secret keys. The objective of our research is to defeat masked AES by deep learning methods like as multi-layer perceptrons and convolutional neural networks are used. In an environment when profiling is not taking place, we extract sensitive information using DDLA. The HW (Hamming Weight) model is used by our approach in order to carry out DDLA by means of an innovative binary labeling strategy. Using the ChipWhisperer power traces and the ASCAD dataset, we show profiling and non-profiling attacks. The accuracy of key recovery is at its maximum when the AES implementation is either masked or unmasked. © 2023 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Detecting Deepfake Videos using Face Recognition and Neural Networks 1

Detecting Deepfake Videos using Face Recognition and Neural ...

引用

1st IEEE International Conference on Cognitive Robotics and Intelligent Systems, ICC - ROBINS 2024

作者： Murugan, Muthu Aravind Mathu, T. Priya, S Jeba Karunya Institute of Technology and Sciences Computer Science and Engineering Coimbatore India Karunya Institute of Technology and Sciences Data Science and Cyber Security Coimbatore India

ISBN: (纸本)9798350372748

Deepfake videos created using advanced artificial intelligence techniques, pose a significant threat to digital media credibility. This project introduces a holistic strategy for identifying these videos, incorporating face recognition, feature extraction, and an innovative deep-learning model. The methodology involves pre-processing video data, extracting facial features using the face recognition library, and training a neural network model on processed face-only videos. The project filters videos based on frame count, extracts face, and creates a curated dataset for the detection model. Face-only videos are loaded and pre-processed to train a custom neural network model, which combines a pre-trained ResNext CNN with an LSTM layer for temporal feature extraction. The model is trained using Adam optimization with a cross-entropy loss function, and after completion, it has an accuracy of 95% and is capable of differentiate between fake and real videos using a confidence score. © 2024 IEEE.

关键词： Face recognition

来源：评论

学校读者我要写书评

暂无评论

Advancing Multiclass Emotion Recognition with CNN-RNN Architecture and Illuminating Module for Real-time Precision using Facial Expressions

Advancing Multiclass Emotion Recognition with CNN-RNN Archit...

引用

2024 International Conference on Advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： Prince, Simrith C. Babu, Nirmal Varghese Computer Science And Engineering Karunya Institute Of Technology And Sciences Coimbatore India Data Science And Cyber Security Karunya Institute Of Technology And Sciences Coimbatore India

ISBN: (纸本)9798350371567

Emotions, intrinsic to the human experience, serve as the catalyst for a groundbreaking technological innovation in multiclass emotion recognition through facial expressions. This study introduces a revolutionary system that harnesses advanced deep learning techniques, specifically Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), to unravel the intricate interplay between emotions and cutting-edge technology. Commencing with a nuanced exploration of the evolutionary origins of emotions, the research lays a robust foundation for understanding the neural mechanisms behind emotional responses. The proposed algorithm unfolds in two pivotal stages: a meticulously designed CNN extracts intricate features from raw facial images, capturing nuanced expressions, while an adaptive RNN establishes temporal dependencies between sequential frames, allowing for a comprehensive understanding of the dynamic nature of emotional states. To address real world challenges, an innovative illuminating module is seamlessly integrated, accounting for contextual factors such as lighting conditions. Empirical evaluations on benchmark datasets underscore the system's exceptional accuracy, achieving an impressive 87.2% accuracy post hyperparameter tuning, surpassing state-of-the-art methods. This transformative method not only precisely captures static facial features but dynamically interprets evolving emotional expressions in real-time, marking a paradigm shift in the field of emotion recognition. Beyond its technical achievements, the proposed system holds profound implications across psychology, human-computer interaction, and market research, positioning itself as a pioneering force in the evolving landscape of emotion-centric technologies. © 2024 IEEE.

关键词： Emotion Recognition

来源：评论

学校读者我要写书评

暂无评论

A Super Learner Ensemble-based Intrusion Detection System to Mitigate Network Attacks 5

A Super Learner Ensemble-based Intrusion Detection System to...

引用

5th International Conference on Data Analytics for Business and Industry, ICDABI 2024

作者： Ajayi, Ojo John Sodiya, Adesina Simon Bagiwa, Mustapha Aminu Olowookere, Toluwase Ayobami Department of Cyber Security Abuja Nigeria Federal University of Agriculture Department of Computer Science Abeokuta Nigeria Ahmadu Bello University Department of Computer Science Zaria Nigeria Redeemer's University Department of Computer Science Ede Nigeria

ISBN: (纸本)9798350368710

Governments and corporate institutions are now mostly reliant on integrated digital infrastructures. These digital infrastructures are usually targets of cyber threats such as intrusion, for which intrusion detection systems (IDS) have emerged. One of the key needs for a robust IDS includes reducing the rate of false positives and thus improving accuracy. In this study, three traditional machine learning (ML) algorithms, including K-Nearest Neighbor (KNN), Naive Bayes (NB), and Decision Tree (DT), and three ensemble Machine Learning (ML) algorithms, including Random Forest (RF), Light Gradient Boosting Machine (LGBM), and Extreme Gradient Boosting (XGBOOST), were used on the UNSW-NB15 dataset from the Australian Centre for cyber security's cyber Range Lab, to train intrusion detection models. A super-learner ensemble model was then built using the best two ensemble models (XGBOOST and RF) along with the best traditional model (KNN) as its base learners. The super-learner ensemble model was able to reduce false positives and improve detection accuracy with 98% accuracy. The model was then deployed in an IDS application to mitigate network attacks effectively and efficiently. © 2024 IEEE.

关键词： and xgboost decision tree ids k-nearest neighbour lgbm machine learning naïve bayes random forest super learning

来源：评论

学校读者我要写书评

暂无评论

Unveiling the Nexus: Exploring the Societal Implications of AI-Driven cybersecurity and Data Analysis 13

Unveiling the Nexus: Exploring the Societal Implications of ...

引用

13th International Conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Arif, Mohd Ahmad, Shahnawaz Nazim, Mohd Ahmad, Javed Onyarin, Ogala Justin Galgotias University Department of Computer Science Engineering and Technology Greater Noida India School of Computer Science Engineering and Technology Bennett University Greater Noida India Noida Institute of Engineering and Technology Department of Computer Science Greater Noida India University of Delta Pmb Cyber Security Department Delta State Agbor2090 Nigeria

ISBN: (纸本)9798350380583

AI when integrated with cybersecurity and data analysis forms a new field that will have great impact on the society. The present research focuses on the social consequences of integrating AI for cybersecurity and big data analytics, thus filling the gap in the existing literature. Using both qualitative and quantitative points of view, quantitative analysis and qualitative examination, the research offers an all-embracing understanding of this convergence. It aims to review and analyze the history, present usage, future prognosis of AI related cybersecurity and data analysis, including the reflection of ethical issues, privacy aspects and relevant laws. The lessons learnt are discussed based on case studies, best practices analysis, and the study provides recommendations for the effective use of AI-based solutions throughout their life cycle. The analysis broadens the knowledge on the effects of AI technology for cybersecurity and data analytics, and helps researchers, policymakers, and industry stakeholders apply the new knowledge. © 2024 IEEE.

关键词： cybersecurity

来源：评论

学校读者我要写书评

暂无评论

Crime Against Women in India: Statistical Analysis Using Linear Regression 2

Crime Against Women in India: Statistical Analysis Using Lin...

引用

2nd International Conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Mim, Zarin Tasnim Paul, Rittika Foysal, Mollah Md Tohfa, Fairuz Nowshin Mahi, Shah Abrar Bangalore India Bangalore India B-Tech of Computer Science and Engineering East Delta University Chattagram Bangladesh B-Tech of Computer Science and Engineering-Cyber Security Chandigarh University Delhi India

ISBN: (纸本)9798331519056

The crime reports of offenses occurring against women all over India are increasing day by day. It has become one of today's most concerning socio-economic and cultural issues. Crime against women hampers the overall prosperity and development of the nation. This crime violates human rights, impacts women's physical and mental health, and becomes an educational barrier to women's empowerment. This paper surveys the reasons for the influence of reported Crime Against Women (CAW) in 35 states and union territories in India from 2001-2012. The dataset describes different types of Crime Against Women (CAW): rape, kidnapping, abduction, assault on women, insult to the modesty of women, cruelty by husband or relative. This paper also contains a report on the arrested rate for crimes against women in India from 2001-2012. According to the comparison between these two datasets, the person arrest rate is more than the crime against women rate in every state in India. We used machine learning-based linear regression analysis to format the relationship between two datasets, where crime against women is the dependent variable and people arrested rate is the independent variable. This paper discusses two types of linear regression: Simple Linear Regression and Multiple Linear Regression. The relation between these two datasets helps to predict the hot location of crimes, and the activeness of law enforcement regarding the crimes and encourages victims to take action. For the datasets, P-values, R-squared, and adjusted R-squared values are used to evaluate the statistically significant prediction in explaining the variation in crime rates. This prediction is used to reduce the rate of crime against women all over India. © 2024 IEEE.

关键词： Multiple linear regression

来源：评论

学校读者我要写书评

暂无评论

Breast Cancer Detection using Domain-Adversarial Training (DANN) with Invariant Risk Minimization (IRM) Hybrid Approach 3

Breast Cancer Detection using Domain-Adversarial Training (D...

引用

3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Akash, K.V. Daniel, Esther Seetha, S. Durga, S. Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India Cmr Institute of Technology Department of Information Science & Engineering India Amrita Vishwa Vidyapeetham Tifac Core in Cyber Security Amrita School of Engineering India

ISBN: (纸本)9798331509675

The breast cancer detection performs a key function in the health care network. The precise and early detection of cancer in the breast could aid to save life of the sufferer. The traditional machine learning methods struggle due to the data used to train is different from data used later this is known as Domain shifts. This project uses a hybrid model known as Domain-Adversarial Training of Neural Networks (DANN) with Invariant Risk Minimization (IRM) Hybrid Method to identification of the tumor in the breast. Through integrating the (DANN) and IRM works perfectly among the various type of data and also with the various image from the various sources. This model is more precise and reliable than any other older *** that this model act as an effective tool for detecting the breast cancer in the early stages. © 2025 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：