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EAODroid: Android Malware Detection Based on Enhanced API Order

Chinese Journal of Electronics 2023年第5期32卷 1169-1178页

作者： HUANG Lu XUE Jingfeng WANG Yong QU Dacheng CHEN Junbao ZHANG Nan ZHANG Li School of Computer Science and Technology Beijing Institute of Technology Department of Media Engineering Communication University of Zhejiang

The development of smart mobile devices brings convenience to people's lives, but also provides a breeding ground for Android malware. The sharp increasing malware poses a disastrous threat to personal privacy in the information age. Based on the fact that malware heavily resorts to system application programming interfaces(APIs) to perform its malicious actions,there has been a variety of API-based detection *** of them do not consider the relationship between APIs. We contribute a new approach based on the enhanced API order for Android malware detection, named EAODroid, which learns the similarity of system APIs from a large number of API sequences and groups similar APIs into clusters. The extracted API clusters are further used to enhance the original API calls executed by an app to characterize behaviors and perform classification. We perform multi-dimensional experiments to evaluate EAODroid on three datasets with ground truth. We compare with many state-of-the-art works, showing that EAODroid achieves effective performance in Android malware detection.

关键词： Performance evaluation Privacy Programming Information age Malware Mobile handsets Behavioral sciences

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A Review of Blockchain based Decentralised Authentication Solutions and their improvement through Metamask 2

A Review of Blockchain based Decentralised Authentication So...

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2nd International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2024

作者： Khan, Andleeb Nand, Parma Bhushan, Bharat Hameed, Alaa Ali Jamil, Akhtar Sharda School of Engineering & Technology Department of Computer Science & Engineering Greater Noida India Istinye University Department of Computer Engineering Istanbul Turkey National University of Computer and Emerging Sciences Department of Computer Science Islamabad Pakistan

ISBN: (纸本)9798331529529

With the advancing technology, it becomes difficult to cope up with novel trends and configurations. Similarly, it is difficult to secure the systems against each emerging threat. With this the loopholes in conventional forms of system security based on centralized format are now visible. Hence there are explorations in various alternatives of centralized system authentication and authorization. Blockchain technology has appeared as a means of securing the systems as it works on decentralization thereby by passing the loopholes that exist in centralized authentication. Hence, this study lays down the recent advancements in the field of blockchain authorization and authentication techniques. It further supplements the study with an explanation of MetaMask extension which connects the user to the blockchain network thereby enabling decentralization in system security. This study further proposes the future potentials of MetaMask and blockchain integration. © 2024 IEEE.

关键词： Authentication Protocol

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Determination of Brain Tumors with Neural Networks and CT Scan Imagery 2

Determination of Brain Tumors with Neural Networks and CT Sc...

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2nd International Conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Ragam, Prashanth Gorantla, Bhavitha Sonakshi Eshtamsetti, Yasaswini Maji, Sayanatika Pavithra, Danaveni School of Computer Science and Engineering VIT-AP University Andhrapradesh India Gokaraju Rangaraju Institute of Engineering and Technology Department of Computer Science Engineering Hyderabad India

ISBN: (纸本)9798350366570

The process of brain tumor detection is paramount in the field of medical diagnostics. Early iden- tification of a brain tumor significantly enhances the chances of a patient restoring from it. In recent years, Artificial Intel- ligence has emerged as a promising tool that has efficiently assisted brain tumor detection. This research paper intro-duces an Artificial Intelligence system specifically designed to detect brain tumors based on medical imaging data, like Computed Tomography scans. It is harnessing the power of advanced machine learning algorithms, including deep learning and accuracy of brain tumor detection, facilitating timely medical action and proper treatment planning for patients. We have taken models Support Vector Machine (SVM), Radial Basis Function Neural Network (RBFNN), and Back Propagation Neural Network (BPNN) to train on the Computed Tomography images of the brain collected from the Kaggle and achieved an accuracy of 88.88% using RBFNN and BPNN models. © 2024 IEEE.

关键词： Radial basis function networks

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A NEWBORN EMBODIED TURING TEST FOR COMPARING OBJECT SEGMENTATION ACROSS ANIMALS AND MACHINES 12

A NEWBORN EMBODIED TURING TEST FOR COMPARING OBJECT SEGMENTA...

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12th International Conference on Learning Representations, ICLR 2024

作者： Garimella, Manjulata Pak, Denizhan Wood, Justin N. Wood, Samantha M.W. Department of Informatics School of Informatics Computing & Engineering Department of Psychological & Brain Sciences Cognitive Science Program Program in Neuroscience

Newborn brains rapidly learn to solve challenging object perception tasks, including segmenting objects from backgrounds and recognizing objects across new viewing situations. Conversely, modern machine learning (ML) algorithms are "data hungry," requiring more training data than brains to reach similar performance levels. How do we close this learning gap between brains and machines? Here, we introduce a new benchmark-a Newborn Embodied Turing Test (NETT) for object segmentation-in which newborn animals and machines are raised in the same environments and tested with the same tasks, permitting direct comparison of their learning. First, newborn chicks were raised in controlled environments containing a single object rotating on a single background, then their recognition performance was tested across new backgrounds and viewpoints. Second, we performed "digital twin" experiments in which artificial agents were reared and tested in virtual environments that mimicked the rearing and testing conditions of the chicks. We inserted a variety of ML "brains" into the artificial agents and measured whether those algorithms learned common object recognition behavior as chicks. All newborn chicks solved this one-shot object segmentation task, successfully learning background-invariant object representations that generalized across new backgrounds and viewpoints. In contrast, none of the artificial agents solved the task, instead learning background-dependent representations that failed to generalize across new backgrounds and viewpoints. This digital twin design exposes core limitations in current ML algorithms in developing brain-like object perception. Our NETT is publicly available for comparing ML algorithms with newborn chicks. We argue that NETT benchmarks can help researchers build embodied AI systems that learn as efficiently and robustly as newborn brains. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Animals

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Optimized Deep Learning Framework for Personalized Nutritional Recommendations Across the Menstrual Cycle 5

Optimized Deep Learning Framework for Personalized Nutrition...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Logapriya, E. Surendran, R. Poornima, D. Kartheesan, L. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Sathyabama Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Avadi Chennai India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530013

This study presents a deep learning architecture designed to monitor a woman's menstrual cycle and provide personalized dietary suggestions to improve health. The system employs complex models, including CNNs, RNNs, Transformers, T-GNNs, VAEs, and GANs, to predict the intensity of symptoms, determine the phases of the menstrual cycle, and offer personalized nutritional advice. By analysing data from menstrual diaries, food consumption, and symptom tracking, the method establishes a strong association between hormonal fluctuations, symptoms, and nutritional needs. Model accuracy is enhanced through data preprocessing techniques, including imputation, normalization, and augmentation, which provide high-quality training inputs. Research shows that Transformer models outperform other approaches in phase recognition (96% accuracy) and phase-aware nutritional recommendation accuracy. This research highlights how deep learning can transform women's healthcare by providing personalized nutritional recommendations based on data that align with the phases of a woman's menstrual cycle, ultimately contributing to improved health for women. © 2024 IEEE.

关键词： Generative adversarial networks

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Congruent Feature Selection Method to Improve the Efficacy of Machine Learning-Based Classification in Medical Image Processing

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computer Modeling in engineering & sciences 2025年第1期142卷 357-384页

作者： Mohd Anjum Naoufel Kraiem Hong Min Ashit Kumar Dutta Yousef Ibrahim Daradkeh Department of Computer Engineering Aligarh Muslim UniversityAligarh202002India College of Computer Science King Khalid UniversityAbha61413Saudi Arabia School of Computing Gachon UniversitySeongnam13120Republic of Korea Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityRiyadh13713Saudi Arabia Department of Computer Engineering and Information College of Engineering in Wadi AlddawasirPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia

Machine learning(ML)is increasingly applied for medical image processing with appropriate learning *** applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws/diseases for *** primary concern of ML applications is the precise selection of flexible image features for pattern detection and region *** of the extracted image features are irrelevant and lead to an increase in computation ***,this article uses an analytical learning paradigm to design a Congruent Feature Selection Method to select the most relevant image *** process trains the learning paradigm using similarity and correlation-based features over different textural intensities and pixel *** similarity between the pixels over the various distribution patterns with high indexes is recommended for disease ***,the correlation based on intensity and distribution is analyzed to improve the feature selection ***,the more congruent pixels are sorted in the descending order of the selection,which identifies better regions than the ***,the learning paradigm is trained using intensity and region-based similarity to maximize the chances of ***,the probability of feature selection,regardless of the textures and medical image patterns,is *** process enhances the performance of ML applications for different medical image *** proposed method improves the accuracy,precision,and training rate by 13.19%,10.69%,and 11.06%,respectively,compared to other models for the selected *** mean error and selection time is also reduced by 12.56%and 13.56%,respectively,compared to the same models and dataset.

关键词： computer vision feature selection machine learning region detection texture analysis image classification medical images

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sEMG-based Sarcopenia risk classification using empirical mode decomposition and machine learning algorithms

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Mathematical Biosciences and engineering 2024年第2期21卷 2901-2921页

作者： Kumar, Konki Sravan Lee, Daehyun Jamsrandoj, Ankhzaya Soylu, Necla Nisa Jung, Dawoon Kim, Jinwook Mun, Kyung Ryoul Center for Artificial Intelligence Korea Institute of Science and Technology Seoul Korea Republic of KHU-KIST Department of Converging Science and Technology Graduate School Kyung Hee University Seoul Korea Republic of Department of Human Computer Interface and Robotics Engineering University of Science and Technology Daejeon Korea Republic of Department of Computer Science Ozyegin University Istanbul Turkey

Early detection of the risk of sarcopenia at younger ages is crucial for implementing preventive strategies, fostering healthy muscle development, and minimizing the negative impact of sarcopenia on health and aging. In this study, we propose a novel sarcopenia risk detection technique that combines surface electromyography (sEMG) signals and empirical mode decomposition (EMD) with machine learning algorithms. First, we recorded and preprocessed sEMG data from both healthy and at-risk individuals during various physical activities, including normal walking, fast walking, performing a standard squat, and performing a wide squat. Next, electromyography (EMG) features were extracted from a normalized EMG and its intrinsic mode functions (IMFs) were obtained through EMD. Subsequently, a minimum redundancy maximum relevance (mRMR) feature selection method was employed to identify the most influential subset of features. Finally, the performances of state-of-the-art machine learning (ML) classifiers were evaluated using a leave-one-subject-out cross-validation technique, and the effectiveness of the classifiers for sarcopenia risk classification was assessed through various performance metrics. The proposed method shows a high accuracy, with accuracy rates of 0.88 for normal walking, 0.89 for fast walking, 0.81 for a standard squat, and 0.80 for a wide squat, providing reliable identification of sarcopenia risk during physical activities. Beyond early sarcopenia risk detection, this sEMG-EMD-ML system offers practical values for assessing muscle function, muscle health monitoring, and managing muscle quality for an improved daily life and well-being. © 2024 American Institute of Mathematical sciences. All rights reserved.

关键词： Empirical mode decomposition

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From Posts to Personas: Classifying Users in the Social Media Landscape 5

From Posts to Personas: Classifying Users in the Social Medi...

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5th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2024

作者： Nair, Lekshmi S. Cheriyan, Jo Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Department of Computer Science and Engineering Saintgits College of Engineering Kerala Kottayam India

Social media is a digital environment where users openly share their opinions and engage in debates and discussions on various topics. Social media has amassed an enormous quantity of accessible data due to its constantly expanding and highly active user base. This data is a valuable resource for researching a variety of topics. An example of such a research problem is identifying user similarities by analyzing their data. This article explores combining comments, textual posts, and likes for comments to create keywords or tags and group the users. These tags are extracted from user data and utilized in constructing a tag network. The tag network facilitates the formation of communities consisting of users with similar interests. User grouping is achieved based on the tags extracted from the posts. The proposed methods employ TF-IDF (term frequency-inverse document frequency) and TextRank algorithms to extract the tags. Kernel diffusion determines the similarities between tags in the tag network. Finally, an aggregation-hierarchical clustering algorithm is employed to group social media users based on these tags. © 2024 The Authors. Published by Elsevier B.V.

关键词： Complex networks

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Efficient Radon Levels Detection in Residential Areas using Cloud-Enabled Decision Trees for Real-Time Analysis 8

Efficient Radon Levels Detection in Residential Areas using ...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Lenin, J. Rohit, V.Robin Nisha, F. Vasudevan, B. Sivakumar, S. Murugan, S. Alliance College of Engineering and Design Alliance University Department of Computer Science & Engineering Bangalore India Presidency University Computer Science & Engineering School of Computer Science & Engineering and Information Science Bengaluru India Cmr University School of Engineering and Technology Computer Science & Engineering Bangalore India Srm Institute of Science and Technology Department of Electronics and Communication Engineering Kattankulathur SRM Nagar Tamil Nadu Chennai India Department of Computer Science Tamil Nadu Perambalur India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350376425

This research introduces a new approach to radon detection in homes utilizing Decision Trees (DTs) enabled by the cloud in real-time. High radon levels, a natural radioactive gas, are dangerous to human health. Quick evaluation and mitigation efforts are hindered by the long processes required by traditional detection systems. Using DTs, a machine learning methodology, the proposed method sorts of radon levels according to variables, including building materials, ventilation systems, and geographic location. These trees may handle both numerical and categorical data and provide interpretability. Fast processing of massive data sets and real-time analysis across varied residential areas are made possible by integrating cloud computing, increasing efficiency and scalability. Stakeholders may remotely access crucial information due to the solution's accessibility and flexibility. Results from controlled experiments demonstrate that this method can accurately identify radon levels in the air in real time, allowing for preventative actions to lessen potential health hazards. People living in areas prone to radon may benefit from this practice in terms of their health. © 2024 IEEE.

关键词： Indoor air pollution

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Python-Based Free and Open-Source Web Framework Implements Data Privacy in Cloud Computing 4th

Python-Based Free and Open-Source Web Framework Implements D...

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4th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2023

作者： Veeresh, V. Parvathy, L. Rama Department of Computer Science and Engineering Saveetha School of Engineering Tamil Nadu Chennai India

ISBN: (纸本)9789819965854

Data sharing turn into a remarkably striking service delivered by cloud computing architecture due to its suitability and reduced cost. As a probable practice for understanding finely secured distribution of data, using a technique called Attribute-Based Encryption (ABE) with a different variety of users. In real time, ABE technique has its own drawbacks like high computation overhead and less security in cloud data storage with fine-grained data files. Processing tasks could be transferred to remote computing resources utilizing cloud computing. Cyber insurance is a realistic way of passing on cyber security risks, but data security can or cannot be improved depending on the environment's key characteristics. This paper focused on one income insurer, client participants, and insured volunteers, focusing on two different cybersecurity aspects and their impact on the standard form of contracts. Since cyber security is linked, an entity's degree of security is determined not just by its own effort and effort but also by the efforts of others operating within the same system. As such, effective resource utilization could be performed, reducing manufacturer costs, but the only drawback of cloud services is that data will be secured and reasonably priced in the cloud. Hence, before going to the cloud, all content must be encrypted. Consumers of a collision protection information-sharing system are given secure private keys so they may add or remove consumers. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Digital storage

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