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Development of Malware Detection Using Machine Learning

Development of Malware Detection Using Machine Learning

2024 International Conference on science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Adedayo-Ajayi, Victoria Oluwaseyi Oluwaseun Ogundokun, Roseline Akande, Hakeem Babatunde Nathanial Babatunde, Akinbowale Dubem, Dubem Tunbosun, Aderemi Emmanuel Kolawole Ojo, Adekunle Kaunas University of Technology Department of Multimedia Engineering Lithuania Landmark University Department of Computer Science Omu-Aran Nigeria Landmark University Department of Computer Science Omu Aran Nigeria University of Ilorin Department of Computer Science Ilorin Nigeria Kwara State University Department of Computer Science Malete Nigeria

ISBN: (纸本)9798350358155

With the continuous rise in computer tech, programmers and hacking occurrences are expanding and require additional security requests. Malware has been an extraordinary torment for computer clients around the world. Big organizations have gone into gigantic misfortunes due to an escape clause in security. In the current world of technology, Machine learning is considered a long-standing time and the foremost capable concept in innovation. The point is to utilize the machine learning concept and construct a show using gathering calculations that can be prepared effectively to identify malware in a framework. This extension is around the comparative consideration of standard machine learning and crossbreed calculations to determine how much effect a crossbreed machine learning calculation can have in distinguishing malware from traditional machine learning algorithms. In this study, we employ different machine-learning algorithms to detect malware. Our results show that Random Forest outperformed other machine learning algorithms with accuracy and average precision of 98% and 92%, respectively. These outcomes are crucial as malware becomes more prevalent and advanced. © 2024 IEEE.

关键词： Adversarial machine learning

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Skin Lesion Classification System Using Shearlets

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computer Systems science & engineering 2023年第1期44卷 833-844页

作者： S.Mohan Kumar T.Kumanan Department of Computer Science and Engineering Meenakshi Academy of Higher Education and ResearchChennai600078Tamil NaduIndia

The main cause of skin cancer is the ultraviolet radiation of the *** spreads quickly to other body ***,early diagnosis is required to decrease the mortality rate due to skin *** this study,an automatic system for Skin Lesion Classification(SLC)using Non-Subsampled Shearlet Transform(NSST)based energy features and Support Vector Machine(SVM)classifier is ***,the NSST is used for the decomposition of input skin lesion images with different directions like 2,4,8 and *** the NSST’s sub-bands,energy fea-tures are extracted and stored in the feature database for *** classifier is used for the classification of skin lesion *** dermoscopic skin images are obtained from PH^(2) database which comprises of 200 dermoscopic color images with melanocytic *** performances of the SLC system are evaluated using the confusion matrix and Receiver Operating Characteristic(ROC)*** SLC system achieves 96%classification accuracy using NSST’s energy fea-tures obtained from 3^(rd) level with 8-directions.

关键词： Skin lesion classification non-subsampled shearlet transform sub-band coefficients energy feature support vector machine

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Development of CNN-Based Feature Extraction and Multi-layer Perceptron for Eye Disease Detection 1

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3rd International Conference on Advanced Computing and Intelligent Technologies, ICACIT 2023

作者： Malakar, Antara Ganguly, Ankur Chakraborty, Swarnendu Kumar Department of Computer Science and Engineering The Assam Royal Global University Guwahati India Department of Computer Science and Engineering National Institute of Technology Arunachal Pradesh Jote India

ISBN: (数字)9789819719617

ISBN: (纸本)9789819719600

Identification of multiple eye disorders utilizing a multi-label categorization methodology is an effective way. The crucial merit of this technique is that it can identify the disorders in earlier times. Ocular disease affects millions of people worldwide, and early detection and treatment of these abnormalities are crucial in preventing avoidable blindness. Diagnosing eye illnesses accurately necessitates the analysis of a diverse array of visually discernible symptoms associated with these conditions. The wide range of symptoms exhibited by various eye illnesses emphasizes the importance of a comprehensive assessment for an accurate diagnosis. Because of the moderate progression, the disorder gives several indications in the initial times, so creating disorder detection is a complex work. To achieve an effective identification system, architecture for a machine learning-assisted method is suggested. Initially, the required eye images are gathered from the standard online data sources. It is then followed by Convolution Neural Network (CNN)-based feature extraction, where the features are extracted by Visual Geometry Group 16 (VGG16). Finally, the attained features are subjected to the Multi-Layer Perceptron (MLP) for detecting the different eye disorders. The performance analysis is conducted contrast with other conventional models to prove the developed model efficacy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Feature extraction

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BiGRU-ANN based hybrid architecture for intensified classification tasks with explainable AI

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International Journal of Information Technology (Singapore) 2023年第8期15卷 4211-4221页

作者： Chakraborty, Sovon Talukder, Muhammad Borhan Uddin Hasan, Mohammad Mehadi Noor, Jannatun Uddin, Jia Department of Computer Science and Engineering University of Liberal Arts Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh Department of Computer Science and Engineering European University of Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering BRAC University Dhaka Bangladesh AI and Big Data Department Woosong University Daejeon South Korea

Artificial Intelligence (AI) is increasingly being employed in critical decision-making processes such as medical diagnosis, credit approval, criminal justice, and many more. However, many AI models exploit complex algorithms that are difficult for humans to see through, which can lead to concerns about accountability, bias, and the ability to trust the outcomes. With the increasing demand for AI systems to be transparent, interpretable, and reliable, the field of Explainable AI (XAI) has gained attention of the researchers. This paper presents a robust hybrid architecture that combines Bidirectional Gated Recurrent Units (BiGRU) and Artificial Neural Networks (ANN) for the classification of texts and sentiment analysis. Interpretable Model Agnostic Explanation (LIME) has been employed with our proposed model to enhance confidence in the outcomes. The proposed architecture is found to be effective for sentiment analysis from texts, and classifying images containing handwrit- ten characters. It leverages the BiGRU to model the sequential dependencies in the data, while the ANN is used for the final classification. Evaluations on both Bengali and English datasets show that the proposed architecture outperforms state-of-the-art models in various performance metrics, providing meaningful and interpretable explanations for its predictions. The model can be used in systems that require the architectures to be computationally less demanding, yet a decent accuracy is secured. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： ANN BiGRU Explainable AI LIME Sentiment analysis

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A Model Driven Framework Enabling the Development of Standardized Text-Free User Interface for Smartphones 21

A Model Driven Framework Enabling the Development of Standar...

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21st International Bhurban Conference on Applied sciences and Technology, IBCAST 2024

作者： Rasheed, Uzair Khalid, Sadia Mehmood, Rabia Ahmad, Muhammad Muneer, Mishal College of Electrical and Mechanical Engineering NUST Department of Computer and Software Engineering Islamabad Pakistan Lahore College for Women University Department of Computer Science Lahore Pakistan International Univesity of Rostock Department of Computer science Germany

ISBN: (纸本)9798331516680

Smartphones offer a wide range of applications globally, and usability is a key factor in their quality. Human-computer Interaction (HCI) continuously evolves to enhance usability by improving user interface (UI) components. However, most UI trends are designed for literate users, neglecting a significant portion of the population who cannot read or write. These users often rely on others for assistance with smartphone applications, and text-based UIs hinder their learning and usage, reducing overall usability. This research addresses the limitations of text-based standards by introducing a standardized framework for developing text-free user interfaces for smartphones. The framework utilizes static and dynamic images instead of text to convey widget or button functions, aiming to enhance usability for illiterate users, including preschoolers. Developed with Obeo Designer and Sirius, the framework's Platform Independent Metamodel (PIM) was validated through case studies representing the target user groups. Future work involves Model to Text (M2T) transformation, and results show the framework's potential for automating applications with text-free UIs. © 2024 IEEE.

关键词： Smartphones

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Optimizing Service Placement in Edge-to-Cloud AR/VR Systems Using a Multi-Objective Genetic Algorithm 14

Optimizing Service Placement in Edge-to-Cloud AR/VR Systems ...

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14th International Conference on Cloud Computing and Services science, CLOSER 2024

作者： Herabad, Mohammadsadeq G. Taheri, Javid Ahmed, Bestoun S. Curescu, Calin Department of Mathematics and Computer Science Karlstad University Karlstad Sweden School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast Belfast United Kingdom Department of Computer Science Faculty of Electrical Engineering Czech Technical University Prague Czech Republic Ericsson AB Stockholm Sweden

ISBN: (纸本)9789897587016

Augmented Reality (AR) and Virtual Reality (VR) systems involve computationally intensive image processing algorithms that can burden end-devices with limited resources, leading to poor performance in providing low latency services. Edge-to-cloud computing overcomes the limitations of end-devices by offloading their computations to nearby edge devices or remote cloud servers. Although this proves to be sufficient for many applications, optimal placement of latency sensitive AR/VR services in edge-to-cloud infrastructures (to provide desirable service response times and reliability) remain a formidable challenging. To address this challenge, this paper develops a Multi-Objective Genetic Algorithm (MOGA) to optimize the placement of AR/VR-based services in multi-tier edge-to-cloud environments. The primary objective of the proposed MOGA is to minimize the response time of all running services, while maximizing the reliability of the underlying system from both software and hardware perspectives. To evaluate its performance, we mathematically modeled all components and developed a tailor-made simulator to assess its effectiveness on various scales. MOGA was compared with several heuristics to prove that intuitive solutions, which are usually assumed sufficient, are not efficient enough for the stated problem. The experimental results indicated that MOGA can significantly reduce the response time of deployed services by an average of 67% on different scales, compared to other heuristic methods. MOGA also ensures reliability of the 97% infrastructure (hardware) and 95% services (software). © 2024 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Augmented reality

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Fractal Measurement of Images Human Brain from MRI

Fractal Measurement of Images Human Brain from MRI

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2024 Cognitive Models and Artificial Intelligence Conference, AICCONF 2024

作者： Anarova, Shahzoda Jabbarov, Jamoliddin Uralova, Iroda Tashkent University of Information Technologies Department of Convergence of digital technologies Computer Engineering Faculty Tashkent Uzbekistan Department of Computer science and technology Intelligent systems and computer technologies Faculty Samarkand State University Samarkand Uzbekistan

ISBN: (纸本)9798400716928

This article is devoted to the determination of the fractal size of the damaged part of the human brain on the basis of images obtained from MRI (magnetic resonance imaging). There are various mathematical methods for determining the size of fractal structures, this article uses two variants of the cell counting method and describes the analysis of errors in determining the fractal size of the damaged part of the human brain. The article shows that the damaged part of the human brain has a fractal structure and that it has a fractal dimension. Fractal measurement has also been used to determine the size of the damaged part of the human brain, unlike common methods. In determining the fractal scale, cases of changes in human body organs were not taken into account. In the article, the determination of the size of the damaged part of the human brain is studied on the basis of mathematical formulas. Two different variants of the special cell counting method were used to calculate the fractal measurements, as well as the corresponding number of experimental results. In addition, these measurements were used to assess the extent to which patients suffered brain damage and to provide information on the practical application of these fractal measurements. © 2024 ACM.

关键词： Magnetic resonance imaging

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Parkinson's Disease Detection and Analysis Using Machine Learning 2

Parkinson's Disease Detection and Analysis Using Machine Lea...

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Aniruddha Prabhu, B.P. Bhatt, Chandradeep Mohd, Noor Sharma, Tushar Singh, Malkeet Department of Computer Science and Engineering Alliance University Bangalore India Department of Computer Science and Engineering Graphic Era Hill University Dehradun India University Dehradun India

ISBN: (纸本)9798350383867

Neurodegenerative disease Parkinson's, which have both motor and non-motor type of symptoms, is one of the biggest problems, which gets consideration in the public health department. To assurance of patients and beneficial therapeutics process diagnosis should occur in the critical stages and should also be precise. The aim of this study is developing of the machine learning methods for the diagnosis and appraisal of Parkinson's disease. Dataset which contains patient data is used where each record has important attributes such as age, height, weight, gender, and so forth is obtained from the University of California Irvine (UCI) Machine Learning Repository. The research process commences with a detailed data-gathering procedure which is followed by an investigative exploratory data analysis to discover an inherent pattern shared by all Parkinsonian traits. The data visualization tools can help us more deep information about Parkinson's disease symptoms. The task of making sure that the Minority Random Oversampling method is properly implemented in data preparation is quite significant for us as we work on the project because it is one of the important aspects. On the other hand, the flare Light Gradient Boosted Machine (LightGBM) model is the one we use for this particular purpose. When run on the test dataset, this state of the art algorithm hits the almost perfect result of 99.24% accuracy. As well as adding up new facts to the science of the Parkinson's disease research, this work shows how powerful computer algorithms and perfect datasets are in the spreading accurate diagnoses of Parkinson's disease. © 2024 IEEE.

关键词： Neurodegenerative diseases

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Extracting Facial Features to Detect Deepfake Videos Using Machine Learning

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International Journal of Advanced computer science and Applications 2025年第4期16卷 834-842页

作者： Aslam, Ayesha Mir, Jamaluddin Zaman, Gohar Rahman, Atta Salam, Asiya Abdus Ali, Farhan Alhiyafi, Jamal Bakry, Aghiad Gul, Mustafa Jamal Gollapalli, Mohammed Mahmud, Maqsood Department of Computer Science Abbottabad University of Science and Technology Havelian Pakistan Department of Computer Science College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University P.O. Box 1982 Dammam31441 Saudi Arabia Department of Computer Information Systems College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University P.O. Box 1982 Dammam31441 Saudi Arabia College of Electronics and Information Engineering Shenzhen University Shenzhen China Department of Computer Science Kettering University FlintMI United States Department of Business Administration University of York York YO10 5DD Heslington United Kingdom Department of Information Technology & Engineering Sydney Met SydneyNSW2000 Australia School of Computing Ulster University Northern Ireland Belfast United Kingdom

Generative adversarial networks (GANs) have gained popularity for their ability to synthesize images from random inputs in deep learning models. One of the notable applications of this technology is the creation of realistic videos known as deepfakes, which have been misused on social media platforms. The difficulty lies in distinguishing these fake videos from real ones with the naked eye, leading to significant concerns. This study proposes a supervised machine learning approach to effectively differentiate between real and counterfeit videos by detecting visual artifacts. To achieve this, two facial features are extracted: eye blinking and nose position, utilizing landmark detection techniques. Both features were trained on supervised machine learning classifiers and evaluated using the publicly available UADFV and Celeb-DF deepfake datasets. The experiments successfully demonstrate that the proposed method achieves a promising and superior performance, with an area under the curve (AUC) of 97% for deepfake detection in contrast to state-of-the-art methods investigating the same datasets. © (2025), (science and Information Organization). All rights reserved.

关键词： Support vector machines

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Advances in Multimodal Fusion of EHR and Medical Imaging Data Using deep learning techniques for advanced treatment of brain cancer 24

Advances in Multimodal Fusion of EHR and Medical Imaging Dat...

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8th International Conference on Algorithms, Computing and Systems, ICACS 2024

作者： Siva Raja, P.M. Vidhya, S. Sumithra, R.P. Anjana, S. Rejini, K. Ramanan, K. Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Nagercoil Tamil Nadu Nagercoil India Department of Computer Science and Engineering Chennai Institute of Technology Tamil Nadu Chennai India

ISBN: (纸本)9798400718304

In this research, the performance of different deep learning models, including CNN + RNN, VGG16 + RNN, VGG19 + RNN, ResNet50 + RNN, and InceptionV3 + RNN in predicting brain tumour grades by employing Electronic Health Record (EHR) and imaging data are evaluated. Confusion matrices show the models' abilities to predict, showing the results of the prediction capability of each model over tumour grades. From the result and analysis, InceptionV3+RNN is performing better than all other ML models. With the multimodal fusion techniques, an informed decision can be made about the grades of the cancer cells, and the clinicians can give the proper treatment to the patients. The proposed approach can be implemented in real-time to predict the cancer grades with the highest level of accuracy for better treatment. © 2024 Copyright held by the owner/author(s).

关键词： Electronic health record

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