检索结果-内蒙古大学图书馆

CAED: A Comprehensive Android Emulator Detection Framework with Data Augmentation

IEEE Internet of Things Journal 2025年第13期12卷 22847-22861页

作者： Yan, Ran Niu, Weina Hou, Qinsheng Su, YuChi Gong, Jiacheng Zhang, Xiaosong School of Computer Science and Engineering Institute for Cyber Security Chengdu611731 China Shanghai Jiao Tong University School of Cyber Science and Engineering China

Anti-emulation is crucial for Android and IoT security as it helps apps determine whether they are running on a real mobile device or in an emulation environment. This prevents apps from being analyzed, debugged, or reverse-engineered in emulators, ultimately stopping criminals from making illegal profits. Current emulator detection methods cannot balance accuracy, universality, robustness, and compatibility. Their universality is often hindered by limited data diversity and accessibility. To address these issues, we propose the Comprehensive Android Emulator Detection (CAED) framework. The Preprocessing Module of CAED collects and normalizes data from both phones and emulators. We propose the first data augmentation method for emulator detection, EDA-GAN, which is tailored to the characteristics of our data and effectively enhances data diversity. The classifier module MFBoost employs an adaptive imputation algorithm and multiple Classification and Regression Trees (CART) for precise classification. Experiments on 324 devices show that CAED improves detection rate by at least 12.5% and up to 44.71% over state-of-the-art (SOTA) methods. The EDA-GAN data augmentation method boosts classifier accuracy, achieving a performance of up to 99.62%. Additionally, CAED’s unique loss function and imputation algorithm enhance the robustness and compatibility of CAED, with a 24% smaller accuracy drop than other methods when features are modified or unavailable. This study presents the CAED framework as an effective solution for protecting apps against real-world security threats in Android and IoT environments. © 2014 IEEE.

关键词： Crime

来源：评论

学校读者我要写书评

暂无评论

Synthetic Data Generation for Document Text Recognition 1

Synthetic Data Generation for Document Text Recognition

引用

1st International Conference on Cognitive, Green and Ubiquitous Computing, IC-CGU 2024

作者： Ashlin Deepa, N.R. Srija, Boda Jabeen, Maria Kankar, Kushi Reddy Lakshmi, Bollepalli Jhansi Vijayalata, Y. Gokaraju Rangaraju Institute of Engineering and Technology Computer Science and Engineering Hyderabad India Vardhaman College of Engineering Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350361780

Handwritten text recognition software is used to recognize and extract text from scanned documents. The fun-damental goal of this technology is to transform printed or handwritten text into an easily readable electronic format. The numerous characters and enormous quantity of the information for Indian languages, however, causes explicit preprocessing to take time. This necessity for explicit preprocessing has been replaced by synthetic data creation techniques, which overcome several difficulties and speed up the process. Artificially produced data that closely mimics actual observations is referred to as synthetic data. In situations where getting real data is difficult or expensive, it offers a workable substitute for training machine learning models. In this work, we propose a data preprocessing technique that creates synthetic data files from the already-existing collection of Indian languages. We use a pre-trained language model called FastText model, which is capable of creating word embeddings, to generate synthetic dataset from the real-time dataset. With the aid of the generated synthetic datasets, document text recognition systems can undergo extensive training and testing, allowing them to grasp the complexities of Indian languages and perform accurate text extraction from scanned documents. © 2024 IEEE.

关键词： Embeddings

来源：评论

学校读者我要写书评

暂无评论

Utilizing Ensemble Learning to enhance the detection of Malicious URLs in the Twitter dataset 4

Utilizing Ensemble Learning to enhance the detection of Mali...

引用

4th International Conference on Innovative Practices in Technology and Management, ICIPTM 2024

作者： Arora, Rakhi Gupta, Rishi Yadav, Pradeep Manipal University Computer Science & Engineering Jaipur India Institute of Technology & Management Computer Science & Engineering Gwalior India

ISBN: (纸本)9798350307757

Due of Twitter's widespread appeal as a platform for sharing activities, entertainment, and information, spammers have taken notice and are using it to propagate false information and harass clients. Researchers are faced with the issue of locating user profiles and malicious URLs on Twitter so that prompt action can be taken. Several prior studies have tackled this issue and stopped spammers' actions on Twitter by utilizing various tactics. In this study, we create multiple models to detect content and categorise the URLs in it as spam or not-spam, employing several features, including hybrid, content based, and profile based. To build a spam detection, we first gather and categorise a sizable dataset from Twitter. Next, we take different features out of the gathered dataset and combine them to generate a set of rich features. In addition, we utilise various ensemble learning methodologies to construct prediction models. We conducted a thorough analysis of various methods using the gathered dataset, evaluating their performance in terms of f1-score, accuracy, recall and precision. The outcomes demonstrated that distinct sets of learning strategies employed have produced better results in the classification of tweet spam. Most of the time, the numbers for several performance metrics are higher than 90%. According to these findings, utilising user profile, content, and hybrid data while detecting suspicious URLs helps in the development of more accurate prediction models. This paper provides a current overview of prominent ensemble algorithms used in malicious URL detection. Notably, the k-NN technique, when applied in both bagging and random forest ensemble methods, demonstrates the highest accuracy. © 2024 IEEE.

关键词： User profile

来源：评论

学校读者我要写书评

暂无评论

BXrayBLR: Experimental Analysis & Evaluation of the Performance for LR Model on BC Prediction 2

BXrayBLR: Experimental Analysis & Evaluation of the Performa...

引用

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

作者： Prusty, Sashikanta Prusty, Sushree Gayatri Priyadarsini Sahoo, Ghanashyam Rautaray, Jyotirmayee Dalai, Sasanka Sekhar Padhy, Manjog Department of Computer Science & Engineering Bhubaneswar751030 India Gita Autonomous College Department of Computer Science & Engineering Odisha Bhubaneswar India Outr Department of Computer Science & Engineering Odisha Bhubaneswar India

ISBN: (纸本)9798350370188

In the recent two decades, cancer has been the leading cause of death around the globe. Breast Cancer (BC) is the second-highest, causing a maximum number of deaths globally these days. This, comprises pharmaceuticals, medical and surgical supplies, medical gadgets and equipment, and other products required to assist doctors, nurses, and other healthcare individuals. This work aims to address the requirement for BC specimen collection within healthcare organizations, as well as to predict BC before it happens. Firstly, we have carried out a methodology to determine the necessary steps for predicting BC. Secondly, breast screening and testing using X-ray techniques. Thirdly, a model called logistic regression (LR) to implement on breast mammograms to predict the BC disease. In the end, performance for this model has been measured by using precision, recall, f1score, and accuracy. Finally, it was found that the LR model achieves an accuracy of 97.36 % while detecting BC from breast mammograms. However, an ROC (Receiver Operator Characteristic) curve has been plotted, representing the false positive rate (FPR) and true positive rate (TPR) on the x and y-axis respectively. Thus, defining the LR model predicts the BC closer to '1', which might be a greater advantage for the doctors. © 2024 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Blockchain-Based Communicator with Multicast Transmission 8

Blockchain-Based Communicator with Multicast Transmission

引用

8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Nair, Praanesh Balakrishnan Varun Adhitya, G.B. Vineetha, K.V. Ullas, S. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331505462

This paper examines the conceptualization and implementation of an instant messaging application, which is decentralized. The proposed work utilizes a blockchain with end-to-end encryption and digital signatures. The need for nodes dedicated to mining has been done away with, by introducing a new consensus mechanism called Proof of Message (PoM). The implementation demonstrates improved security over traditional centralized messaging systems while maintaining usability for small to medium-sized groups (50-100 users). When compared to conventional instant messaging applications which are centralized, the increase in latency has been found to be minimal, according to performance analysis. © 2024 IEEE.

关键词： Instant messaging

来源：评论

学校读者我要写书评

暂无评论

Classical Chinese poetry generation from vernacular Chinese: a word-enhanced supervised approach

引用

Multimedia Tools and Applications 2023年第25期82卷 39139-39156页

作者： Wu, Dongze Chen, Arbee L. P. Department of Computer Science National Tsing Hua University Hsinchu Taiwan Department of Computer Science and Information Engineering Asia University Taichung Taiwan

In recent years, with the rapid development of deep learning, natural language processing has achieved great progress in many aspects. In the field of text generation, classical Chinese poetry, as an important part of Chinese culture, also attached growing attention. However, the existing researches on neural-network-based classical Chinese poetry generation ignore the semantics contained in Chinese words. A sentence in Chinese is a sequence of characters without spaces, and thus it is of great significance to segment the sentence properly for understanding the original text correctly. Therefore, supposing that the model knows how to segment the sentence, the meaning of the sentence will be more accurately understood. In this paper, we propose a novel model, namely WE-Transformer (Word-Enhanced Transformer), to generate classical Chinese poetry from vernacular Chinese in a supervised approach, which incorporates external Chinese word segmentation knowledge. Our model learns word semantics based on character embeddings by bidirectional LSTM and enhances the quality of generated classical poems based on the Transformer with extra word encoders. Compared to the baselines and state-of-the-art models, our experiments on automatic and human evaluations have demonstrated that our method can bring better performance. © 2023, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

A Comprehensive Study of Plant Disease Detection Through Image Processing and Machine Learning 2

A Comprehensive Study of Plant Disease Detection Through Ima...

引用

2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Pachori, Madan Kant, Shashi Surya, Anupma Kumar, Manish Greater Noida Institute of Technology Computer Science and Engineering Greater Noida India Sharda University Computer Science and Engineering Greater Noida India JECRC University Computer Science and Engineering Jaipur India

ISBN: (纸本)9798350368109

Agriculturalists can greatly benefit from techniques that forecast the kinds of illnesses that plants will encounter during their lives. This article provides information on these methods through a thorough survey that included a range of image-based plant leaf diseases. These illnesses often cause great harm and can affect any section of the plant. These illnesses cause enormous cumulative losses that lower output and raise financial losses in agriculture. The agriculture sector must persevere through these challenges and grow as a result to be very lucrative. This study examined the diagnosis of plant diseases using machine learning algorithms and general image processing methods that may be applied at various stages of a plant's life cycle. Numerous techniques based on automation or computer vision concepts are currently in place for identifying plant diseases. However, they lack precision and are hard to interpret. Furthermore, no single method can detect all conditions. For cultivation to be effective, the plant's status must be monitored at every growth stage. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

An Intelligent Completion Time Aware Task Scheduling (CTATS) Algorithm for Fog Computing Environment for Delay Sensitive IoT Applications 3

An Intelligent Completion Time Aware Task Scheduling (CTATS)...

引用

3rd International Conference on Machine Learning and Data engineering, ICMLDE 2024

作者： Behera, Ranjit Kumar Manjula Gururaj, H. Roy, Diptendu Sinha Tripathy, Siba Computer Science and Engineering Silicon University Bhubaneswar India Information Science and Engineering NMAM Institute of Technology NITTE University Karnataka Mangalore India Computer Science and Engineering NIT Meghalaya Shilong India Computer Science and Engineering SOA University Bhubaneswar India

Fog computing also known as Edge computing is emerging as an attention gaining computing technology for providing Cloud services to IoT applications which requires instantaneous and real-time responses. In absence of an efficient and systematic task scheduling algorithm, Fog with limited computing power and resources may fail to handle high traffic and hence may result in delays. Although many studies have been carried out and many task scheduling models has been proposed by researchers pertaining to different application areas, many of these models have some inherent drawbacks like increase in response time and execution time, as well as migration delays which hinders these models from being the most competent ones particularly the case of delay sensitive IoT applications. Hence, being mindful of the mentioned issues and challenges, in this paper, an efficient Completion Time Aware Task Scheduling (CTATS) algorithm has been proposed at Fog Computing layer. We have implemented reinforcement learning (RL), a machine learning strategy that is environment-adaptive, to enhance performance with respect to predict the suitable VM for a delay sensitive task and for this we have used our own dataset. To check the efficacy of the proposed model, the simulation has been carried out using CloudSim simulator and the obtained results is compared with the existing models for the comparison of average response time and overall completion time. Obtained results shows the superior efficacy of CTATS over the QBT and Prioritized Scheduled Mechanism. © 2025 The Author(s).

关键词： Scheduling algorithms

来源：评论

学校读者我要写书评

暂无评论

A Secure and Energy-Efficient Routing Protocol for Wireless Sensor Networks (WSNs) 5

A Secure and Energy-Efficient Routing Protocol for Wireless ...

引用

5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Srivastava, Animesh Aparna Luthra, Pooja Dhondiyal, Shiv Ashish Graphic Era Hill University Department of Computer Science & Engineering Dehradun India Chandigarh University Department of Computer Science &engineering Punjab Mohali India Department of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798350379945

Wireless sensor networks (WSNs) can be utilized in several industries, including agriculture and the military, as part of the Internet of Things (IoT). Nevertheless, developing a robust and dependable authentication system for WSNs that can be implemented in the IoT. Because routing is done wirelessly, it is necessary to do it safely to keep a strategic distance from data loss and other attacks. When it comes to safe routing and trust in node-recognized proof, the existing frameworks present themselves as accessible options. This Proposed model called IBE-ECC, after the merge of best parameters, which is responsible for monitoring the authentication strategy that is both energy-conscious and effective. It is necessary to implement a verification methodology to provide authentication that is both efficient and safe. The approaches that are based on public keys, on the other hand, eliminate the security procedure;nevertheless, the verification process takes more resources and time. As a consequence of this, the need for authentication power consumption is regarded as the most significant limitation of this study. The purpose of this innovative technique is to provide high levels of security while simultaneously achieving energy economy and rapid authentication. Furthermore, to secure the authentication process, it decreases the size of the signature, which implies that it would speed up the verification of signatures quickly and efficiently. © 2024 IEEE.

关键词： Wireless sensor networks

来源：评论

学校读者我要写书评

暂无评论

Stress Detection Using EEG Signals: A SVM-Based Classification and Feature Selection Approach 12

Stress Detection Using EEG Signals: A SVM-Based Classificati...

引用

12th Electrical Power, Electronics, Communications, Controls and Informatics Seminar, EECCIS 2024

作者： Sifaunnufus, Fi Imanur Bachtiar, Fitra Abdurrachman Prasetio, Barlian Henryranu Brawijaya University Faculty of Computer Science Department of Computer Engineering Malang Indonesia Brawijaya University Faculty of Computer Science Department of Informatics Engineering Malang Indonesia

ISBN: (纸本)9798350379914

Stress is a problem with various adverse effects, such as mental disorders and suicide. With these problems arising, a stress detection system is needed as an initial examination of the level of stress experienced. Generally, stress detection is done with a stress test at a hospital. However, medical examinations done in the hospital require high costs. Another alternative to examine stress levels is by using a biosignal electroencephalogram (EEG), which provides more objective and accurate results. In addition, there is a need to develop a portable device that can be used to detect stress. This study tries to detect stress levels using EEG Signals. The data used in this study is primary data that simulates several stress-level activities. In total, 55 subjects participated in this stress detection research. The received signal goes through preprocessing to clean it from noise and artifacts. The data will be extracted using Power Spectral Density (PSD) into power value for each signal frequency. The preprocessed input signals are then classified into indications of stress levels. This study uses Random Forest, SVM, and Decision Tree. The model is stored on a portable system with Raspberry Pi as the main component. EEG signals are taken using electrodes on Muse 2 and sent to the system. The classification model generates predicted stress levels and is displayed on a 16×2 LCD. SVM as the best model test result achieved a testing accuracy of 1, and an average computation time of 0.92. © 2024 IEEE.

关键词： Electroencephalography

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：