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Covertvasion: Depicting threats through covert channels based novel evasive attacks in android

International Journal of Intelligent Networks 2023年第1期4卷 337-348页

作者： Gautam, Sunil Pattani, Ketaki Zuhair, Mohd Rashid, Mamoon Ahmad, Nazir Department of Computer Science and Engineering Institute of Technology Nirma University Ahmedabad Gujarat India Department of Computer Sciences and Engineering Institute of Advanced Research Gujarat Gandhinagar India School of Information Communication and Technology Bahrain Polytechnic Isa Town Bahrain Department of Information System College of Applied Sciences King Khalid University P.O. Box 61913 Muhayel63317 Saudi Arabia

Privacy and security issues concerning mobile devices have substantial consequences for individuals, groups, governments, and businesses. The Android operating system bolsters smartphone data protection by imposing restrictions on app behavior. Nevertheless, attackers conduct systematic resource analyses and divert privacy-sensitive information from plain view. They employ evasive mechanisms to evade system monitoring and create an illusion of benign and non-sensitive communication. Furthermore, covert channels amplify the impact of these malicious activities by facilitating information transfer through non-standard methods. The purpose of this research is to shed light on these novel threats targeting Android systems. The study delves into security and privacy attacks that compromise sensitive user information. The methodology leverages evasion concepts and employs sound-specific covert channel communication, particularly ultrasonic channels. This research work introduces novel evasive attacks, namely Prime-Composite Evasive Information Invasion (PCEII) and File-lock-based Evasive Information Invasion (FEII), both relying on covert channel communication. These unique variants of attacks successfully evade user data within a few milliseconds for both noisy as well as non-noisy environments and do not show any signs of detection by antivirus mechanisms like Anti-Virus Guard (AVG), 360 security, etc. and state-of-the-art tools such as TaintDroid, MockDroid and others. The paper not only assesses their impact on the privacy and security of information but also introduces avenues for their detection and mitigation. © 2023 The Authors

关键词： Android (operating system)

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Recurrent Neural for Time Series Prediction in Finance 2

Recurrent Neural for Time Series Prediction in Finance

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2nd International Conference on IT Innovations and Knowledge Discovery, ITIKD 2024

作者： Nair, Rekha R Babu, Tina Kishore, S. Alliance School of Advanced Computing Department of Computer Science and Engineering Alliance University Bengaluru India Nitte Institute of Professional Education Department of Computer Applications Name of Organization Nitte (Deemed to Be University) Mangalore India

ISBN: (纸本)9798350355468

Overcoming risk is crucial when predicting financial time series, such as stock prices, due to market volatility influenced by factors like economic reports, political events, and investor sentiment. Linear models like ARIMA handle linear dependencies well but fail to capture nonlinear relationships, which are key in financial forecasting. While Long Short-Term Memory (LSTM) models excel at capturing temporal dependencies, they struggle with identifying significant market events, potentially contaminating predictions. This paper introduces an Attention RNN with Sentiment Integration, which combines Recurrent Neural Networks (RNN) with an attention mechanism for sentiment analysis of financial news and social media. The model considers relevant time steps and market sentiment, improving context-awareness. Results demonstrate that this hybrid approach outperforms traditional methods by providing a more accurate and flexible forecasting model, addressing the complexities of financial markets. Additionally, sentiment data helps the model capture both stock-specific and overall market sentiment, particularly during periods of heightened uncertainty or volatility, offering a more comprehensive approach to stock price prediction. © 2025 IEEE.

关键词： Attention Mechanism Financial Time Series Prediction LSTM Machine Learning Recurrent Neural Networks Sentiment Analysis

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Artificial Intelligence Technique for Prediction of Carbon Stocks and Uncertainty Estimates in Tropical Forests

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SN computer science 2025年第4期6卷 1-21页

作者： Akinbiola, Sehinde Akinsola, Jide Ebenezer Taiwo Ajagbe, Sunday Adeola Salami, Ayobami Olubaju, Ayomide Awotoye, Olusegun Awoleye, Olusesan Micheal Department of Surveying and Geoinformatics First Technical University Ibadan Ibadan 200255 Nigeria Department of Computer Science First Technical University Ibadan Ibadan 200255 Nigeria Department of Computer Engineering First Technical University Ibadan Ibadan 200255 Nigeria Institute of Ecology and Environmental Studies Obafemi Awolowo University Osun State Ile Ife Nigeria African Institute for Science Policy and Innovation Obafemi Awolowo University Ile Ife Nigeria

The lack of a robust national forest inventory in tropical regions remains a major challenge to the assessment of forest productivity and the evaluation of the climate regulation potential of tropical forests. Therefore, many sampling procedures and models exist for estimating carbon stocks;albeit with varying levels of limitations because of their associated errors. Given the development in computing and modeling in environmental science, this study focuses on providing an Artificial Intelligence (AI)-based approach for predicting carbon stocks and uncertainty estimates in tropical forests. In this study, an artificial intelligence (AI) technique was used to model carbon stocks using machine learning (ML) paradigms. Four ML algorithms, namely, Multi-Layer Perceptron (MLP), Support Vector Machines (SVM), Random Forest (RF), and K-Nearest Neighbors (KNN), were considered based on biophysical variables;including tree height and diameter. The model was implemented in the Waikato Environment for Knowledge Analysis and Jupyter (6.1.4) using the Anaconda distribution of Python (3.00). Uncertainty estimates were evaluated using cost performance measures, including Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), to validate the reliability of the predicted carbon stock values. In addition, a comparative analysis was performed using 8 different performance measures for classification, while error estimation was performed for regression analysis. One of the models produced included the random forest machine learning algorithm with an accuracy of 98.3%, root mean square error of 0.02, and model building time of 0.13 s, which was chosen as the best algorithm for modeling carbon stocks. This study concluded that there is variation in carbon distribution among species which is not proportional to the volume of carbon stocks in the genus. The study recommends for further studies, the application of a multi-criteria decision-making approach in the selection of an opti

关键词： Artificial intelligence Carbon stock Global climate regulation Informatics Machine learning Tropical-forest productivity

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Strain-enhanced dynamic regulation of chiroptical responses in 3D suspended nested metamaterials

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Optics Express 2025年第11期33卷 23440-23451页

作者： Li, Bo Duan, Shilong Zou, Qiushun Guo, Ruansheng Chen, Yimin Gu, Chenjie Zhang, Peiqing Shen, Xiang Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo315211 China Laboratory of Infrared Material and Devices Zhejiang Key Laboratory of Advanced Optical Functional Materials and Devices Advanced Technology Research Institute Ningbo University Zhejiang 315211 China CAS Key Laboratory of Mechanical Behavior and Design of Materials Department of Modern Mechanics University of Science and Technology of China Anhui Hefei230027 China

Three-dimensional (3D) chiral metamaterials, which exhibit strong chiroptical responses, have attracted tremendous attention in the broad fields of next-generation photonic devices and chiroptical spectroscopy techniques. There is an urgent need for a novel fabrication approach that enables the rapid and straightforward preparation of 3D chiral nanostructures, along with a robust strategy for achieving dynamic regulation. Here, we proposed a strain-enhanced strategy for dynamically regulating the chiroptical responses of 3D suspended nested metamaterials from the transformation of a 2D precursor. The local strain enhancement could effectively resolve the bottleneck of insufficient strain and low precision for the buckling strain self-assembly. The changing morphology and circular dichroism (CD) of 3D suspended chiral metamaterials were analyzed under the impact of a strain-enhanced effect. Then, the uplifted heights of these 3D nanostructures increased with the substrate’s pre-strain (Εpre). Its CD reached up to 0.79, mainly resulting from ring electric dipoles. Interestingly, the chiroptical responses of 3D nanopyramids were regulated dynamically by controlling the substrate’s pre-strain. Such a strategy provides a new pathway for achieving high-performance chiral metamaterials and offers promising applications in circularly polarized light detection, super-chiral light sources, optical communication, and advanced nanophotonic systems. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Metamaterials

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Recent Advancements in Hyperspectral Face Recognition: A Review 13

Recent Advancements in Hyperspectral Face Recognition: A Rev...

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13th International Conference on Computing Communication and Networking Technologies, ICCCNT 2022

作者： Das, Samiran Chatterjee, Sujoy JIS University Centre for Data Science JIS Institute for Advanced Studies and Research Kolkata India Amity University Department of Computer Science and Engineering Kolkata India

ISBN: (数字)9781665452625

ISBN: (纸本)9781665452625

The increasing demand for high-accuracy face recognition methods and the rapid advancement in instrumentation has made hyperspectral face recognition a viable alternative to traditional face recognition. The high-security biometric application scenarios, which demand high accuracy of recognition are switching to hyperspectral imaging due to its robustness against spoofing, illumination, pose variation, etc. The hundreds of visible and near infrared (NIR) images contained in a hyperspectral face cube provide sufficient information content, leading to more accurate recognition. However, the gigantic volume of information and the computational challenges call for efficient learning strategies for the recognition task. Researchers have introduced several feature extraction, supervised learning, deep learning, and pattern recognition approaches to carry out face recognition from hyperspectral face cubes. This work summarized the state-of-the-art approaches for hyperspectral face recognition in detail and highlight their pros and cons. The work also discusses future directions and challenges in this domain. © 2022 IEEE.

关键词： Biometrics

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Sentiment Analysis based Emotion Extraction for COVID-19 Using Crawled Tweets and Global Statistics for Mental Health

Sentiment Analysis based Emotion Extraction for COVID-19 Usi...

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2022 International Conference on Machine Learning and Data Engineering, ICMLDE 2022

作者： Nandal, Neha Tanwar, Rohit Pathan, Al-Sakib Khan Department of Computer Science and Engineering Gokaraju Rangaraju Institute of Engineering and Technology India School of Computer Science University of Petroleum and Energy Studies India Department of Computer Science and Engineering United International University Bangladesh

The unpredictable and crucial challenges that occurred because of the COVID-19 pandemic disease have taken a gradual upsurge impacting over 213 countries across the globe. Different countries have taken several measures to get control over it like Lockdown, Curfews, Travel ban, etc. but still the cases were increasing and the situation was getting worse globally during some period of time. The impacts on the financial, social, and physical aspects of several citizens resulted in their psychological and mental health issues. In this work, we have quantitatively analyzed the depression, stress, and suicide cases during the period of COVID-19 globally and especially, in India. The global data including tweets (collected using a Scraper) is used for analysis. The data have been analyzed on Tableau and;sentiment analysis for extracting emotions in tweets has been performed using Python. Tweets are analyzed to extract the emotion of people in terms of Fear, Sadness, Anger, and Happiness. With total collected Tweets of 819678 from Jan 2020 to March 2022, it is found that people are more into Fear and Sadness with 59.3% and 28.9% scores respectively. © 2023 The Authors. Published by Elsevier B.V.

关键词： COVID-19

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Efficient Lung Cancer Detection Based on Support Scalar Vector Feature Selection with Fuzzy Optimized-Multi Perceptron Neural Network Using Natural Language Processing

Efficient Lung Cancer Detection Based on Support Scalar Vect...

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Sustainable Computing and Smart Systems (ICSCSS), International Conference on

作者： Jabir. K S. Kamalakkannan J. Anita Smiles Department of Computer Science School of Computing Sciences Vels Institute of Science Technology & Advanced Studies(VISTAS) Chennai India Department of Information Technology School of Computing Sciences Vels Institute of Science Technology & Advanced Studies(VISTAS) Chennai India Department of Computer Applications SRMIST Chennai India

ISBN: (数字)9798350379990

ISBN: (纸本)9798350391558

Lung cancer is one of the most frequent types of cancer worldwide, and it has a high fatality rate. To improve survival chances and ensure a successful treatment, early identification of lung cancer is essential. The rapidly developing artificial intelligence discipline of Natural Language Processing (NLP) has great promise for transforming the understanding, identification, and management of lung cancer. Previously, NLP-based lung cancer prediction models may not always produce accurate results, as they rely on complex algorithms to analyze text data. Errors in language processing or interpretation can lead to incorrect predictions and potentially harmful decisions. This study suggests employing Fuzzy Optimized -Multi Perceptron Neural Network (FOMPNN) for lung cancer detection based on NLP and the Relative Support Scalar Vector Feature Selection (RSSCVFS) method for optimal attribute selection. To begin with, preprocessing involves finding null and missing values, and textual summaries based on stemming and tokenization. Then, the Semantic Lexicon-Based Approach (SLBA) is used to find the sentiment score. Furthermore, the sentiment score of each word in the medical summary can be added up to get the overall sentiment of the text. Based on dictionary terms, feature outcomes are added to patient records. Similar generalization patterns based on Linguistic Subset Pattern Mining (LSPM) are used to produce corresponding feature patterns. The RSSCVFS method is used to pick features based on their significance to minimize the feature dimension of the illness effect rate. To classify the risk of lung cancer, the FOMPNN is finally utilized to test the feature's weight. The proposed method proficiently produces better performance than other methods.

关键词： Fuzzy logic Accuracy Uncertainty Neural networks Lung cancer Predictive models Feature extraction

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NeuroSpectrum Deep Learning Architecture for Automated Spectrum Analysis Resource Optimization 3

NeuroSpectrum Deep Learning Architecture for Automated Spect...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Tamilselvan, K. Senthil, T. Buvaneswari, B. Santhiya, S. Soni, Mukesh Avs Engineering College Department of Information Technology Salem India Sreenivasa Institute of Technology and Management Studies Department of Computer Science and Engineering chittoor India Panimalar Engineering College Department of Information Technology Chennai India Excel Engineering College Department of Computer Science and Engineering Namakkal India Lovely Professional University Department of Division of Research and Development Phagwara India

ISBN: (纸本)9798331508456

Reducing the availability of spectrum, as well as the nature of allocating channels in the present wireless telecommunication networks, is one of the critical issues facing modern wireless technology. Such spectrum sensing methods may not be highly flexible or efficient in environments where the radio spectrum undergoes frequent changes. This work proposes an improved deep learning spectrum sensing model based on CNN with reinforcement learning. The proposed system design calls for a pioneering dual mechanism, incorporating both spectrum analysis and a resource allocation learning model. The framework achieved remarkable performance metrics: 96 The spectrum sensing method achieved a 3% occurrence rate, a 94.8% precision rate for resource allocation, and a 92.7% efficiency in dynamic spectrum use. In the present investigation, we have employed it for different topologies and got 45% improvement in spectrum efficiency and 87% less interference than the conventional techniques. The work challenges deep learning-based spectrum sensing as well as management and indicates that they are prepared for the following generation of the wireless communication network. © 2025 IEEE.

关键词： Cognitive radio

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Driving into the Future: Artificial Intelligence based Traffic Sign Recognition using Learning Assisted OCR Principle

Driving into the Future: Artificial Intelligence based Traff...

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2024 IEEE International Conference on Automation and Computation, AUTOCOM 2024

作者： Kirubakaran, D. Lakshmisridevi, S. Ramal, P. Janaki Rajeswari, M. Rani, A. Jaya Mabel Pandi, V.Samuthira R.M.K Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Bs Abdur Rahman Crescent Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Tamil Nadu Chennai India

ISBN: (纸本)9798350382723

Managing the inventory of traffic signs relies heavily on automatic detection and recognition. It offers a precise and quick method of managing traffic-sign inventory with little human intervention. The detection and identification of traffic signs is an issue that has received a lot of attention from the computer vision field. The traffic signs required for sophisticated driver-assistance and self-driving vehicles are well-served by most current methods. But it's just a fraction of all roadway markings (approximate 40-50 kinds among hundreds), and how well it will work with the rest of the traffic signs - the ones that need to be automated so that traffic-sign inventory management doesn't require any human intervention - is still up in the air. The goal of this work is to automate the process of managing traffic signs' inventory by addressing the challenge of detecting and recognizing a large number of sign classifications. This study introduces a new approach called Learning Assisted OCR Principle (LAOCRP). To assess its performance, it is cross-validated with the traditional model called Generic OCR (GOCR). The outcomes are shared for extremely difficult traffic-sign classes that have not been addressed in prior research. Our thorough evaluation of the deep learning method for traffic sign detection with high intra-category appearance variation yields lower error rates in comparison to the state-of-the-art methods, making them suitable for use in real-world traffic sign inventory management applications. © 2024 IEEE.

关键词： Traffic signs

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Multimodal Recipe Recommendation System Using Deep Learning and Rule-Based Approach

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SN computer science 2023年第4期4卷 421页

作者： Khilji, Abdullah Faiz Ur Rahman Sinha, Utkarsh Singh, Pintu Ali, Adnan Dadure, Pankaj Manna, Riyanka Pakray, Partha Department of Computer Science and Engineering National Institute of Technology Silchar Assam India University of Petroleum and Energy Studies Dehradun India Jadavpur University Kolkata India

In today’s age of the internet, there is tremendous growth in information. Moreover, this information can be easily accessed through different combinations of large-scale interconnected networks. But the information available is highly unstructured and requires a certain degree of intelligence to garner the requisite information required by the user. Hence, present age recommendation engines provide the most suitable answer considering different modalities of data, the most common form of which are text and images. Due to the wide range of possibilities and increased awareness among users, the service should be highly satisfactory. This paper has contributed a recipe recommendation system that relies on deep learning and a rule-based approach. It aims to recommend recipes based on input ingredients or food images. To test the proficiency of the designed recommendation system, we have created a dataset of recipes that mainly includes the recipe of food and their images. Finally, the designed recommendation system allows users to look for the desired information through text and image searches. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： BERT Cooking question classification FastText Image captioning Recipe embeddings Recipe recommendation

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