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2022 International Conference on Sustainable Computing and Data Communication Systems, ICSCDS 2022

作者： Ashodia, Namita Makadiya, Kishan Marwadi University Department of Cyber Security Gujarat Rajkot India Marwadi University Department of Computer Engineering Gujarat Rajkot India

ISBN: (纸本)9781665478847

Software Defined Networking (SDN) is an emerging technology, which provides the flexibility in communicating among network. Software Defined Network features separation of the data forwarding plane from the control plane which includes controller, resulting centralized network. Due to centralized control, the network becomes more dynamic, and resources are managed efficiently and cost-effectively. Network Virtualization is transformation of network from hardware-based to software-based. Network Function Virtualization will permit implementation, adaptable provisioning, and even management of functions virtually. The use of virtualization of SDN networks permits network to strengthen the features of SDN and virtualization of NFV and has for that reason has attracted notable research awareness over the last few years. SDN platform introduces network security challenges. The network becomes vulnerable when a large number of requests is encapsulated inside packet_in messages and passed to controller from switch for instruction, if it is not recognized by existing flow entry rules. which will limit the resources and become a bottleneck for the entire network leading to DDoS attack. It is necessary to have quick provisional methods to prevent the switches from breaking down. To resolve this problem, the researcher develops a mechanism that detects and mitigates flood attacks. This paper provides a comprehensive survey which includes research relating frameworks which are utilized for detecting attack and later mitigation of flood DDoS attack in Software Defined Network (SDN) with the help of NFV. © 2022 IEEE.

关键词： Software defined networking

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Criteria-based Unsupervised Feature Selection Techniques: A Comparative Study Over Chest CT Scan 25

Criteria-based Unsupervised Feature Selection Techniques: A ...

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25th International Arab Conference on Information technology, ACIT 2024

作者： Al-Daja, Shrouq Alyabrodi, Ala' Moubayed, Abdallah Injadat, MohammadNoor Elrashidi, Ali Zarqa University Data Science & Ai Department Zarqa Jordan Zarqa University Cyber Security Department Zarqa Jordan King Fahd University of Petroleum & Minerals Interdisciplinary Research Center for Intelligent Secure Systems Computer Engineering Department Dhahran Saudi Arabia University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia

ISBN: (纸本)9798331540012

Research on classifying chest CT scans as normal or abnormal using machine learning and deep learning has garnered significant attention. To address this, various feature selection (FS) methods are employed to reduce dimensionality and identify key features. This paper evaluates criteria tailored to unsupervised binary datasets, such as feature subsets, redundancy, and diversity, by comparing Mutual Information-Based Methods (MI) with Clustering-Based Feature Selection (Agglomerative Clustering) ones. Additionally, it examines Graph Structure, Spectral Properties, and Quality criteria by comparing Graph-Based Methods with Spectral Feature Selection-be (SPEC). For feature reduction, Agglomerative Clustering may offer a slight advantage due to better handling of diversity and redundancy, though the difference is minimal. If these aspects are crucial, Agglomerative Clustering might be preferred. Conversely, for Graph-Based vs. SPEC, SPEC Method is more suitable for feature reduction due to its coherent feature selection and focus on capturing significant patterns related to medical interventions. © 2024 IEEE.

关键词： Contrastive Learning

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Forward and Backward Private Searchable Symmetric Encryption with Efficient Updates 24

Forward and Backward Private Searchable Symmetric Encryption...

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24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data Science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Yin, Zhiming Li, Xiong Choo, Kim-Kwang Raymond Liu, Yuzhen Liang, Wei School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China Institute for Cyber Security School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China University of Texas at San Antonio Department of Information Systems and Cyber Security San AntonioTX78249-0631 United States

ISBN: (纸本)9798350319934

In dynamic searchable symmetric encryption, clients can search and update encrypted data stored on the server (assumed to be semi-honest). In such schemes, forward privacy guarantees that prior queries are not related to subsequent updated entries, while backward privacy ensures that the previously deleted files are not retrievable from the subsequent queries. However, most of the existing schemes have poor update efficiency, for example incurring significant computational and communication overheads since the use of the complex structures and cryptographic primitives used. Hence, we propose a Forward and Backward private searchable symmetric encryption scheme with Efficient uPdates, called FBEP. Specifically, FBEP achieves efficient updates based on Bloom filters and lightweight symmetric cryptographic primitives. It enables the client to remove file identifiers without interacting with the server and insert them efficiently, while maintaining high search efficiency. We precisely define the leakage of FBEP, and demonstrate that FBEP supports forward and Type-II backward privacy with formal security analysis. Besides, the experiments show that FBEP is much better than other related schemes in terms of comprehensive performance. Specially, the update efficiency of FBEP outperforms at least 1.6×, 25×, 525× than Mitra, SDd and Janus ++, respectively, while the search time is at least 2×, 702× faster than Mitra and Janus ++, respectively. © 2022 IEEE.

关键词： Efficiency

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An Efficient Classifier: Kernel SVM-LDA 1

An Efficient Classifier: Kernel SVM-LDA

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1st International engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2022

作者： Tehrani, Fahimeh Jamshidian Nasihatkon, Behrooz Al-Qawasmi, Khaled Al-Mousa, Mohammad Rasmi Boostani, Reza School of Computer Engineering Khajeh Nasir University of Technology Tehran Iran Zarqa University Faculty of Information Technology Department of Cyber Security Zarqa Jordan Shiraz University Shiraz Faculty of Electrical and Computer Engineering Iran

ISBN: (纸本)9798350332742

This study aims at designing an efficient combinatorial classifier, which fuses linear discriminant analysis (LDA) and kernel support vector machine (SVM) classifiers. The proposed method is called kernel SVM-LDA which benefits from global property of LDA, simultaneous with localized capability of SVM along with mapping ability of RBF kernel to project input data into a more separable high dimensional space. To assess the proposed scheme, Kernel SVM-LDA was applied to some standard datasets derived from UCI database and then compared to standard LDA and kernel SVM classifiers. Kernel SVM-LDA was also employed in cue-based brain computer interface to classify the left and right imagery movements. The results indicate that the introduced method is more superior to that of LDA and kernel SVM because it surpasses the counterparts in terms of robustness, complexity and performance. © 2022 IEEE.

关键词： Support vector machines

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An Explainable Deep Learning-based Approach for Multivariate Time Series Anomaly Detection in IoT 20

An Explainable Deep Learning-based Approach for Multivariate...

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20th International Conference on Frontiers of Information technology, FIT 2023

作者： Toor, Aafan Ahmad Lin, Jia-Chun Gran, Ernst Gunnar Lee, Ming-Chang Department of Information Security and Communication Technology Gjovik Norway Electrical Engineering and Mathematical Sciences Department of Computer Science Bergen Norway

ISBN: (纸本)9798350395785

Detecting anomalies from Internet of Things (IoT) data is important for a smooth flow of events. Timely detection of anomalies and raising alarms can help avoid any potential health and safety hazards in an IoT environment such as detecting falling people by activity recognition using wearable sensors. However, most IoT data lacks labels and sometimes when the labels are available, the context is missing, which makes anomaly detection and inference a challenging task. In this paper, we propose an explainable deep learning-based approach to detect anomalies from multivariate time series data using Long Short-Term Memory (LSTM) and the results are explained using the Local Interpre table Model-agnostic Explanations (LIME). The experiments show promising classification results with good accuracy, precision, recall, and F1 Score values. LIME provided useful explanations for anomalous and non-anomalous instances. The effectiveness of LIME for time series anomaly detection is discussed, and its limitations are also highlighted. © 2023 IEEE.

关键词： Internet of things

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A Comprehensive Evaluation of Face Mask Identification System using Modified Deep Learning Methodology 10

A Comprehensive Evaluation of Face Mask Identification Syste...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Lakshman, Bijee Vanitha, R. Vasuki, R. Uparkar, Satyajit S. Arun, S. Thiagarajan, R. Women's Christian College PG Department of Data Science College Road Chennai India Jerusalem College of Engineering Department of Cyber Security Chennai India Dhanalakshmi Srinivasan University Department of Cse School of Engineering and Technology Trichy India Shri Ramdeobaba College of Engineering and Management Department of Computer Application Nagpur India Jerusalem College of Engineering Department of ECE Chennai India Prathyusha Engineering College Department of It Chennai India

ISBN: (纸本)9798350353068

Recent years have seen an explosion in the prevalence, infectiousness, and potential harm posed to humanity by the corona virus family of viruses. When one person breathes out the virus, it lands on surfaces and other people breathe them in, causing the sickness to spread from person to person. As a result, safeguarding ourselves and those close to us has assumed paramount importance. There are measures we may do to protect ourselves, including as keeping our distance from others, wearing a mask, washing our hands often (every two hours), and using hand sanitizer. Masks worn by the general public have recently grown ubiquitous across the globe. The situation in India is particularly severe because of the country's dense population packed into a relatively limited landmass. Using a cross-validation strategy involving the conventional learning model known as Convolutional Neural Network (CNN), this paper presents a method called Modified Learning based Mask Identification Rule (MLMIR) for detecting whether or not a face mask is worn in offices or other places with a high volume of employees. The model's performance using live video streaming was astonishing once it was trained with a dataset made from real-world data. Using a number of additional parameters and a large number of people placed at different heights and angles inside the picture, we further evaluate the model's accuracy. © 2024 IEEE.

关键词： Convolutional neural networks

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On the security of Smart Home Systems:A Survey

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Journal of computer Science & technology 2023年第2期38卷 228-247页

作者：袁斌万俊吴宇晗邹德清金海 School of Cyber Science and Engineering Huazhong University of Science and TechnologyWuhan 430074China Hubei Key Laboratory of Distributed System Security Huazhong University of Science and Technology Wuhan 430074China Hubei Engineering Research Center on Big Data Security Huazhong University of Science and Technology Wuhan 430074China National Engineering Research Center for Big Data Technology and System Huazhong University of Science and TechnologyWuhan 430074China Services Computing Technology and System Lab Huazhong University of Science and TechnologyWuhan 430074China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen 518057China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Cluster and Grid Computing Lab Huazhong University of Science and TechnologyWuhan 430074China

Among the plethora of IoT(Internet of Things)applications,the smart home is one of the ***,the rapid development of the smart home has also made smart home systems a target for ***,researchers have made many efforts to investigate and enhance the security of smart home *** a more secure smart home ecosystem,we present a detailed literature review on the security of smart home ***,we categorize smart home systems’security issues into the platform,device,and communication *** exploring the research and specific issues in each of these security areas,we summarize the root causes of the security flaws in today's smart home systems,which include the heterogeneity of internal components of the systems,vendors'customization,the lack of clear responsibility boundaries and the absence of standard security ***,to better understand the security of smart home systems and potentially provide better protection for smart home systems,we propose research directions,including automated vulnerability mining,vigorous security checking,and data-driven security analysis.

关键词： IoT(Internet of Things) smart home IoT security smart home security

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A Model Stealing Attack Against Multi-Exit Networks

A Model Stealing Attack Against Multi-Exit Networks

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Li, Pan Lv, Peizhuo Chen, Kai Zhang, Shengzhi Cai, Yuling Xiang, Fan SKLOIS Institute of Information Engineering Chinese Academy of Sciences China School of Cyber Security University of Chinese Academy of Sciences China Department of Computer Science Metropolitan College Boston University USA

ISBN: (纸本)9798350368741

Compared to traditional neural networks with a single output channel, a multi-exit network has multiple exits that allow for early outputs from the model's intermediate layers, thus significantly improving computational efficiency while maintaining similar main task accuracy. Existing model stealing attacks can only steal the model's utility while failing to capture its output strategy, i.e., a set of thresholds used to determine from which exit to output. This leads to a significant decrease in computational efficiency for the extracted model, thereby losing the advantage of multi-exit networks. In this paper, we propose the first model stealing attack against multi-exit networks to extract both the model utility and the output strategy. We employ Kernel Density Estimation to analyze the target model's output strategy and use performance loss and strategy loss to guide the training of the extracted model. Furthermore, we design a novel output strategy search algorithm to maximize the consistency between the victim model and the extracted model's output behaviors. In experiments across multiple multi-exit networks and benchmark datasets, our method always achieves accuracy and efficiency closest to the victim models. © 2025 IEEE.

关键词： Defense Model Stealing Attack Multi-Exit Neural Networks

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Exploring LLM-Based Multi-Agent Situation Awareness for Zero-Trust Space-Air-Ground Integrated Network

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IEEE Journal on Selected Areas in Communications 2025年第6期43卷 2230-2247页

作者： Cao, Xinye Nan, Guoshun Guo, Hongcan Mu, Hanqing Wang, Long Lin, Yihan Zhou, Qinchuan Li, Jiayi Qin, Baohua Cui, Qimei Tao, Xiaofeng Fang, He Du, Haitao Quek, Tony Q.S. Beijing University of Posts and Telecommunications National Engineering Research Center for Mobile Network Technologies China Fujian Normal University College of Computer and Cyber Security Fuzhou350007 China Professorate senior engineer at China Mobile Research Institute China Singapore University of Technology and Design 487372 Singapore Yonsei University Yonsei Frontier Lab Korea Republic of

Space-air-ground integrated network (SAGIN), which integrates satellite systems, aerial networks, and terrestrial communications, offers ubiquitous coverage for a multitude of applications. Nevertheless, the highly dynamic and open nature of SAGIN increases the network's vulnerability. Hence, zero-trust security, operating on the principle of "never trust, always verify", holds the significant potential of securing SAGIN. However, implementing zero-trust SAGIN in practice presents three primary challenges: 1) understanding massive unstructured threat information across diverse domains, 2) performing adaptive security assessments, and 3) making in-depth security decisions. This motivates us to propose SAG-Attack and LLM-SA to enhance zero-trust SAGIN. SAG-Attack serves as a simulator that aims to mimic various attacks in SAGIN. Our LLM-SA is a novel situation awareness method that explores the multiple agents of large language model (LLM). Specifically, the output logs of SAG-Attack will be fed into LLM-SA, and LLM-SA fuses vast amounts of heterogeneous threat information from various domains, thus tackling the first challenge. Then, our LLM-SA relies on multiple LLM-based agents to perform adaptive security assessments, utilizing the chain-of-thought capabilities of LLMs to automatically generate in-depth defense strategies, thereby addressing the second and third challenges. Experiments on five benchmarks demonstrate the superiority of the proposed SAG-Attack and LLM-SA. Notably, our method based on open-sourced Llama3-8B even outperforms ChatGPT-4 under the same setting, despite involving significantly fewer parameters. To foster further research in this area, we will release our platform to the community, facilitating the advancement of zero-trust SAGIN. © 1983-2012 IEEE.

关键词： Satellite communication systems

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Predicting Mental States: Machine Learning Techniques for Personalized Mental Health Support 15

Predicting Mental States: Machine Learning Techniques for Pe...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Hepzibah, Grace Babu, Nirmal Varghese Division of Computer Science Engineering Karunya Institute of Technology and Science Karunya Nagar Tamil Nadu Coimbatore641114 India Division of Data Science and Cyber Security Karunya Institute of Technology and Science Karunya Nagar Tamil Nadu Coimbatore641114 India

ISBN: (纸本)9798331300579

As we know that individuals around the globe work difficult to keep up with this hustling world. In any case, due to this each person is managing with diverse wellbeing issues, one of the foremost known issue is sadness or stretch which may in the long run lead to passing or other brutal exercises. This project focuses on leveraging machine learning algorithms to predict psychological instability based on various attributes related to mental health. Developed as a web application using Django, users can upload datasets containing information such as age, gender, family history, and workplace environment. The data undergoes preprocessing, including handling missing values and label encoding. Subsequently, several classifiers including Decision Tree, Random Forest, SVC, MLP Classifier, KNeighbors Classifier, and Linear Discriminant Analysis are trained on the preprocessed data. Evaluation metrics such as accuracy, precision, recall, and F1-score are computed for each classifier. Users can also input personal data to obtain predictions about their psychological well-being. The system aims to offer insights into mental health conditions, enabling early detection and intervention. This project underscores the potential of machine learning in addressing mental health challenges through data-driven approaches. © Grenze Scientific Society, 2024.

关键词： Decision trees

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