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MAIDS: malicious agent identification-based data security model for cloud environments

Cluster Computing 2024年第5期27卷 6167-6184页

作者： Gupta, Kishu Saxena, Deepika Gupta, Rishabh Singh, Ashutosh Kumar Department of Computer Science and Engineering National Sun Yat-sen University Kaohsiung80424 Taiwan School of Computer Science and Engineering The University of Aizu Aizuwakamatsu965-0006 Japan Department of Computer Science The University of Economics and Human Sciences Warsaw01-043 Poland Department of Computer Applications SRM Institute of Science & ampTechnology Delhi-NCR Campus Modinagar Uttar Pradesh Ghaziabad201204 India Department of Computer Science and Engineering Indian Institute of Information Technology Bhopal Bhopal462003 India

With the vigorous development of cloud computing, most organizations have shifted their data and applications to the cloud environment for storage, computation, and sharing purposes. During storage and data sharing across the participating entities, a malicious agent may gain access to outsourced data from the cloud environment. A malicious agent is an entity that deliberately breaches the data. This information accessed might be misused or revealed to unauthorized parties. Therefore, data protection and prediction of malicious agents have become a demanding task that needs to be addressed appropriately. To deal with this crucial and challenging issue, this paper presents a Malicious Agent Identification-based data Security (MAIDS) Model which utilizes XGBoost machine learning classification algorithm for securing data allocation and communication among different participating entities in the cloud system. The proposed model explores and computes intended multiple security parameters associated with online data communication or transactions. Correspondingly, a security-focused knowledge database is produced for developing the XGBoost Classifier-based Malicious Agent Prediction (XC-MAP) unit. Unlike the existing approaches, which only identify malicious agents after data leaks, MAIDS proactively identifies malicious agents by examining their eligibility for respective data access. In this way, the model provides a comprehensive solution to safeguard crucial data from both intentional and non-intentional breaches, by granting data to authorized agents only by evaluating the agent’s behavior and predicting the malicious agent before granting data. The performance of the proposed model is thoroughly evaluated by accomplishing extensive experiments, and the results signify that the MAIDS model predicts the malicious agents with high accuracy, precision, recall, and F1-scores up to 95.55%, 95.30%, 95.50%, and 95.20%, respectively. This enormously enhances the system’s sec

关键词： Forecasting

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On the Design of Cancer Tumor Type Prediction using Machine Learning Techniques for Efficient Diagnosis 2

On the Design of Cancer Tumor Type Prediction using Machine ...

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2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Thakkar, Hiren Kumar Kumar, Yogesh Kathirisetty, Nikhila School of Technology Pandit Deendayal Energy University Department of Computer Science and Engineering Gujarat 382007 India Vardhaman College of Engineering Department of Computer Science and Data Science Hyderabad Shamshabad India

ISBN: (纸本)9798350370188

In the recent past, cancer is designated as the life threatening disease causing significant deaths across the globe. Countries are putting efforts for early stage cancer detection, and mortality prediction. Machine Learning (ML) models are proven to efficiently find the correlation among the features for robust predictions over statistical approaches. In this paper, we used kaggle dataset that includes 569 cancer cells and around 30 features cancer cells' classification. The proposed study employs ML models (e.g., binary classifiers) for effective predictive analytics. The evaluation results indicate that Random Forest achieves better results with Area Under Curve (AUC) of 0.99 followed by Decision tree with AUC be 0.92. However, the Logistic Regression (LR) and Support Vector Machine (SVM) fails to capture the generalized behavior of the cancer data for predictive analysis. © 2024 IEEE.

关键词： Classification (of information)

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Drift Analysis with Fitness Levels for Elitist Evolutionary Algorithms

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Evolutionary computation 2025年第1期33卷 1-25页

作者： He, Jun Zhou, Yuren Department of Computer Science Nottingham Trent University Nottingham NG11 8NS United Kingdom School of Data and Computer Science Sun Yatsen University Guangzhou 510006 China

The fitness level method is a popular tool for analyzing the hitting time of elitist evolutionary algorithms. Its idea is to divide the search space into multiple fitness levels and estimate lower and upper bounds on the hitting time using transition probabilities between fitness levels. However, the lower bound generated by this method is often loose. An open question regarding the fitness level method is what are the tightest lower and upper time bounds that can be constructed based on transition probabilities between fitness levels. To answer this question, we combine drift analysis with fitness levels and define the tightest bound problem as a constrained multiobjective optimization problem subject to fitness levels. The tightest metric bounds by fitness levels are constructed and proven for the first time. Then linear bounds are derived from metric bounds and a framework is established that can be used to develop different fitness level methods for different types of linear bounds. The framework is generic and promising, as it can be used to draw tight time bounds on both fitness landscapes with and without shortcuts. This is demonstrated in the example of the (1+1) EA maximizing the TwoMax1 function. © 2024 Massachusetts Institute of Technology.

关键词： algorithm analysis drift analysis Evolutionary algorithm fitness levels hitting time Markov chain.

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Wi-SFDAGR: WiFi-Based Cross-Domain Gesture Recognition via Source-Free Domain Adaptation

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IEEE Internet of Things Journal 2025年第13期12卷 24159-24173页

作者： Yan, Huan Zhang, Xiang Huang, Jinyang Feng, Yuanhao Li, Meng Wang, Anzhi Ou, Weihua Wang, Hongbing Liu, Zhi Guizhou Normal University School of Big Data and Computer Science Guiyang550025 China University of Science and Technology of China CAS Key Laboratory of Electromagnetic Space Information Hefei230026 China Hefei University of Technology School of Computer and Information Hefei230601 China The University of Electro-Communications Department of Computer and Network Engineering Tokyo1828585 Japan

WiFi channel state information (CSI)-based gesture recognition offers unique advantages, including cost-effectiveness and enhanced privacy protection, and has garnered significant attention in recent years. However, existing WiFi-based gesture recognition solutions exhibit poor generalization ability when deployed in new environment, orientation, or location. Although some methods combine labeled source domain and unlabeled target domain to learn domain-independent features, factors, such as data privacy protection, hinder access to source data during practical environment adaptation. Consequently, we consider realistic scenario where source data is unavailable during adaptation of unlabeled test data, and instead, a trained source domain model is used. In this article, we propose Wi-SFDAGR, a WiFi-based source-free domain adaptation gesture recognition framework. Specifically, we treat cross-domain as an unsupervised clustering problem, aiming to ensure that features within local neighborhoods exhibit similar prediction results while those farther apart display different prediction outcomes in the feature space. We theoretically analyze the effect of enhanced prediction consistency between neighbor points extracted from gestures on generalization error. Furthermore, we employ an attraction-dispersion network to strengthen prediction consistency among closely located features in the feature space while reducing it for distantly located features. To mitigate noise introduced during nearest neighbor sample selection in the feature space (where predictions may not align with the input sample’s prediction), we progressively improve nearby sample feature aggregation by estimating uncertainty to reweight local neighborhood predictions. Finally, extensive experiments are conducted on the Widar 3.0 and XRF55 datasets and the results show our proposed framework outperforms most cross-domain methods. © 2014 IEEE.

关键词： Differential privacy

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Cicada species recognition based on acoustic signals using dynamic time warping-graph based GraphMix, graph convolution network 9th

Cicada species recognition based on acoustic signals using d...

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9th International Conference on computer science and Computational Intelligence, ICCSCI 2024

作者： Yohanes, Gabriel Prabowo, Abram Setyo Kurniadi, Felix Indra Computer Science Department Department School of Computer Science Bina Nusantara University Jakarta11530 Indonesia

Cicadas, known for their distinctive acoustic signals, have been subjects of classification research for years. Recent researches elaborated the species composition as effect of climate change, further raising the need of effective classification system. Traditional methods rely on manual classification by domain experts, while recent trends favor Artificial Intelligence (AI)-assisted approaches due to their efficiency. However, image-based recognition faces challenges due to cicadas' varied appearances and environmental factors. Deep learning approaches, particularly utilizing Mel-frequency cepstral coefficients (MFCC) spectrograms, have been effective but are limited by dataset size. Graph Neural Networks (GNN) have surfaced as a promising alternative, leveraging graph represen- tations to provide additional information like data relationships. In this study, we address the challenge of efficient classification with a small dataset while maximizing feature representation. We explore the effectiveness of MFCC and Chromagram features in a noisy environment, constructing unique graphs for each. Dynamic Time Warping (DTW) is employed to establish connec- tions between nodes. Our experiments on the cicada audio dataset demonstrate the superiority of Chromagram over MFCC, with graph-based approaches outperforming graph-less methods such as Recurrent Neural Networks (RNN). Our findings suggest the potential of graph neural networks in audio classification tasks and contribute to advancing the field's methodologies. © 2024 The Authors.

关键词： Graph neural networks

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Vision Intelligence Assisted Lung Function Estimation Based on Transformer Encoder-Decoder Network With Invertible Modeling

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3336-3349页

作者： Chen, Liuyin Lu, Di Zhai, Jianxue Cai, Kaican Wang, Long Zhang, Zijun City University of HongKong School of Data Science Hong Kong Nanfang Hospital Southern Medical University Department of Thoracic Surgery Guangzhou510515 China University of Science and Technology Beijing Department of Computer Science and Technology Beijing100083 China University of Science and Technology Beijing Shunde Graduate School Foshan China

Lung function evaluation is important to many medical applications, but conducting pulmonary function tests is constrained by different conditions. This article presents a pioneer study of an integrated invertible deep learning method for lung function estimation via using computed tomography (CT) images. First, the projection method is proposed to flatten the three-dimensional (3-D) image onto a two-dimensional (2-D) plane, with preserving location information in 3-D. Next, the MBConv transformer-based encoder-decoder structure is developed to extract latent features. Finally, we develop an invertible normalizing flow (NF) model to infer lung function based on the extracted features and design two loss functions for two directions. The method enables both estimating the lung function based on CT images and metadata as well as generating the corresponding simulated CT image according to the lung function. Computational studies show that the proposed regression model outperforms all state-of-the-art image regression models. A comprehensive comparative analysis also demonstrates the effectiveness of using generated images and confirms the superiority of the proposed method. To the best of our knowledge, this work is the first of its kind in combining encoder-decoder network with NFs to ensure the effectiveness of the fully invertible framework, especially in lung CT image analysis. © 2020 IEEE.

关键词： Metadata

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Implementing Vision Transformer to Model Emotions Recognition from Facial Expressions 4

Implementing Vision Transformer to Model Emotions Recognitio...

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4th International Conference on Artificial Intelligence and data sciences, AiDAS 2023

作者： Chowanda, Andry Nadia Diana School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Cyber Security Program Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350318432

Emotions are essential to social interaction between interlocutors. It provides essential meaning to social interaction. It is important to recognise emotions along with verbal cues to fully understand the true meaning of a conversation in a social interaction setting. Several techniques can be applied to recognise emotions in the social interaction setting automatically. Emotions can be recognised and interpreted from the interlocutor's facial cues (e.g., facial expression recognition task) by using a sensor such as a camera. Recognising emotions from facial cues has several problems to be solved. This research aims to model robust emotion recognition from facial cues to recognise emotions (particularly from Asian people), as there is only limited research done in this area. This research aims to propose an emotion recognition model from facial cues by implementing Vision Transformers architecture. The dataset implemented in this research was explicitly performed by local (i.e., Indonesian) actors (The Indonesian Mixed Emotion dataset-IMED). The results show that the proposed model can achieve the best testing accuracy of 100% with the best training loss of 0.0001, with 0.53 hours of training times for 50 epochs. © 2023 IEEE.

关键词： Emotion Recognition

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Implementation of YOLO v7 algorithm in estimating traffic flow in Malang 9th

Implementation of YOLO v7 algorithm in estimating traffic fl...

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9th International Conference on computer science and Computational Intelligence, ICCSCI 2024

作者： Saputri, Hanis Amalia Avrillio, Matthew Christofer, Louis Simanjaya, Victorius Alam, Islam Nur Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

The escalating prevalence of traffic congestion in Malang, which has worsened over time, has resulted in increased inconvenience and traffic-related issues. As a solution, we propose the development of an intelligent transportation system based on traffic flow estimation. While this endeavor presents challenges, advancements in traffic data accessibility and deep learning algorithms, including You Only Look Once (YOLO), R-CNN based ResNet101, Inception V2, and others, with YOLOv7 demonstrating superior performance, continue to enhance traffic flow estimation. Consequently, this study aims to describe and assess the YOLOv7 model's suitability for estimating traffic flow. To achieve this, we propose a traffic monitoring system utilizing the YOLOv7 model in conjunction with a SORT filter, capable of tracking vehicle count and average speed using a custom dataset comprising car images and CCTV footage of Malang's traffic. Subsequently, the number and average speed of cars passing on the road were computed by delineating two virtual boxes. According to the findings, our YOLOv7 system exhibited higher average precision, achieving 61.3%, 78.6%, and 62.1% for each test, and boasted a faster average inference time of 90.67 seconds, meeting the requirements of an intelligent transportation system. Nevertheless, with an average percent deviation in vehicle count exceeding 30% compared to the pre-trained model, vehicle detection is suboptimal, consequently impacting average vehicle speed detection adversely. Hence, our custom model necessitates further optimization and training before practical implementation in real-world traffic estimation. © 2024 The Authors.

关键词： Traffic congestion

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Recognition of Facial Expressions and Emotions on the Face using the YOLOv5 Algorithm

Recognition of Facial Expressions and Emotions on the Face u...

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2023 International Workshop on Artificial Intelligence and Image Processing, IWAIIP 2023

作者： Gunawan, Alexander A. S. Kusnadi, Maximilianus Raymond Joseph, James Tristan Josan, William Edbert, Ivan Sebastian Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Cyber Security Program Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350382914

This study examines the significant influence of emotional states on individuals, recognizing that people are often confronted with numerous challenges and problems in their lives. Such challenges can give rise to feelings of despair and tension, especially in those who struggle to manage them effectively. Depression, which is not just limited to adults, can also have a significant impact on children who may not fully grasp their socio-economic conditions. One of the serious consequences of unresolved emotional distress, particularly depression, is an elevated risk of suicide. Within this framework, facial recognition technology presents itself as a powerful instrument for identifying human emotions. The purpose of this study is to assess the efficacy of facial recognition systems in detecting indicators of depression. Using the YOLOv5 algorithm and comparing it with its precursors, YOLOv4 and YOLOv7, the research conducts a simulation experiment to examine the ability of the system to precisely interpret a range of emotional states, including happiness, sadness, and anger. The research findings suggest that the YOLO algorithm, particularly in its most recent versions, displays exceptional precision in identifying and classifying intricate facial expressions, thus showcasing its potential as a beneficial device in mental health evaluations. © 2023 IEEE.

关键词： Face recognition

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Exploring the Spectrum of Biometric Technologies: A Systematic Literature Review of Conventional and Unconventional Modalities 9th

Exploring the Spectrum of Biometric Technologies: A Systemat...

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9th International Conference on computer science and Computational Intelligence, ICCSCI 2024

作者： Aldo Arista, W.P. Bryan Fernando, K.S. Sidharta, Sidharta Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Biometric recognition is a process of validating and identifying people based on their distinct physiological or behavioral traits. Through this literature review, we strive to reduce the knowledge gap among the public about existing biometric modalities, how the biometrics technology is applied, what algorithms are often used in processing the biometrics data, and what common benchmarks are often used to test and measure the performance of each biometrics. This paper provides a systematic literature review with the Kitchenham and Cochrane method, aimed at exploring variant types of biometric modalities and how each of them collects data, how biometric modalities are applied in various industries, common algorithms or methods used in biometric technologies, and establishing metrics and benchmarks for evaluating the performance of biometric technology. As a result, each biometric modality has its way of collecting data, for example, fingerprint recognition uses techniques like contrast correction, noise filtering, and sharpening to extract the fingerprint pattern. Biometric modalities are already used in many sectors of industries, and fingerprint readers or facial feature detection cameras are used to reduce the authentication process in the banking sector. Minutiae-based algorithm commonly used for fingerprint biometric modality, Eigenface for facial detection, and Euclidean distance or Support Vector Machine (SVM) for gait detection. Accuracy, False Acceptance Rate (FAR), False Rejection Rate (FRR), Equal Error Rate (EER), and Receiver Operating Characteristic (ROC) metrics and benchmarks are used to evaluate the performance of biometric technology. © 2024 The Authors.

关键词： Face recognition

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