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Malware Detection and Analysis using machine learning

Malware Detection and Analysis using Machine Learning

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international conference on computing Methodologies and Communication (ICCMC)

作者： Manoj Sirigiri Divya Sirigiri R. Aishwarya R. Yogitha Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Data Scientist Brillio Technologies Bangalore India

Online privacy for people is getting worse every day. Computer malware is tainting the data records of some well-known companies. Hackers can gain access to a network and change data, once inside. this work discusses several types of malware and communication strategies, such as Trojans, keyloggers, port forwarding, source code obfuscation, application format converters, and social engineering techniques. three different machine learning algorithms are applied in this work to derive meaningful insights from the data. As a result of the work proposed, given malware can be categorized as a malicious or a non-malicious application. this is executed by analyzing the classification report, accuracy and f1 score metrics. the Random Forest is selected as a champion model based on the highest f'1-score for the validation dataset.

关键词： Measurement Privacy machine learning algorithms Computer hacking Source coding Companies Trojan horses

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A Review of machine learning Algorithms and Feature Selection Techniques for Cardiovascular Disease Prediction: Insights and Implications

A Review of Machine Learning Algorithms and Feature Selectio...

引用

international conference on computing Communication Control and Automation (ICCUBEA)

作者： Abhigya Mahajan Baijnath Kaushik School of CSE Shri Mata Vaishno Devi University Katra India

Cardiovascular disease (CVD) is a formidable public health challenge across the globe and is the most prevalent cause of mortality. Early detection and accurate prediction of CVD can help prevent disease progression and reduce the risk of complications. machine learning (ML) techniques show promising results in improving the accuracy and efficiency of CVD prediction to precision. However, the effectiveness of machine learning algorithms in CVD prediction largely depends on the selection of relevant features from complex datasets. the performance and interpretability of ML models are improved by feature selection strategies, which attempt to identify significant attributes while eliminating duplicate or irrelevant features. the feature selection and ML algorithms for CVD are thoroughly reviewed in this publication. the review provides insight into the selection of appropriate feature selection techniques and machine learning algorithms for accurate CVD prediction and evaluates the effectiveness and performance of these methods on cardiovascular datasets. Insights from the findings of this study can be used for interpreting the selection of optimal feature selection methods and ML algorithms for the precise prediction of cardiovascular disease, thereby improving patient outcomes and reducing healthcare costs.

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Evolution, Trends, and Future Developments of Business Intelligence 1

引用

7th international conference on Smart Trends in computing and Communications, SmartCom 2023

作者： Vinay, Vaishnavi Mathew, Allen Siby, Amala Bangalore India

ISBN: (数字)9789819907694

ISBN: (纸本)9789819907687

A decision-making process backed by the integration and evaluation of an organization's data resources is referred to as business intelligence. Since information has been recognized as a business's most valuable asset, it is a crucial resource for its growth and plays an increasingly important role in a variety of organization kinds. this research article examines the history of business intelligence technologies, their relevance in current times, and all the future developments that seem possible. Organizations are transforming into various approaches based on the information and networking in the twenty-first century in response to a chaotic and ambiguous environment marked by hazy organizational boundaries and rapid change. Knowledge-based assets become apparent to be the core of long-term strategic edge and the cornerstone of success in the twenty-first century in such situations. the primary characteristics of business intelligence are determined by data analysis, processing, and visualization. Relational tables are used by business intelligence technologies to store and display a lot of organized and unstructured data. they utilize specialized tools and mathematics to produce intricate visual reports. this research has been aggravated to focus on the upcoming strategic revolution in the market with numerous cutting-edge business intelligence technologies. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： machine learning

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Robust Self-training with Label Refinement for Noisy Domain Adaptation 7th

Robust Self-training with Label Refinement for Noisy Domain ...

引用

7th international conference on computing, Control and Industrial Engineering, CCIE 2023

作者： Cao, Zhengtao Li, Shaoyuan College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing211106 China

ISBN: (纸本)9789819927296

Unsupervised Domain Adaptation (UDA) intends to achieve excellent results by transferring knowledge from labeled source domains to unlabeled target domains in which the data or label distribution changes. Previous UDA methods have acquired great success when labels in source domain are pure. However, even the acquisition of a large scare clean labels in source domain needs plenty of cost as well. In the presence of label noise in source domain, the traditional UDA methods will be seriously degraded as they do not take appropriate measures against the label noise. In this paper, we propose an approach named Robust Self-training with Label Refinement (RSLR) to address the above issue. RSLR adopts the self-training framework by maintaining two Labeling Networks (LNets) on the source domain, which are used to provide confident pseudo-labeled to target samples, and a Target-specific Network (TNet) trained by the pseudo-labeled samples. To combat the effect of label noise, LNets progressively distinguish and refine the mislabeled source samples. RSLR leads to significant improvements over the state-of-the-art methods on extensive benchmark datasets. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： machine learning

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Fake News Detection Using machine learning 7th

Fake News Detection Using Machine Learning

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7th international conference on Smart Trends in computing and Communications, SmartCom 2023

作者： Lohitha, S. Reddy, S. Dwijesh Krishna, B. Revanth Krishna, N. Satya V R Siddhartha Engineering College Vijayawada India SRM University Neerukonda Guntur India

ISBN: (纸本)9789819907687

the news is the most crucial resource for the general population to learn about what is occurring across the world. Even if newspapers remain a reliable source of news, social media is currently the next frontier in news. Regular individuals may simply alter the news to produce fake news since these social networks are so accessible. these fictitious news stories may be utilized for both political and commercial gains. It may be used as a vehicle to stir up neighborhood animosity, which is detrimental to society. In order to mitigate its impacts, it is crucial to recognize fake news. A platform that can validate and classify news is currently unavailable. In this essay, a technique is presented for figuring out whether or not news is reliable in the present. To train the features that were retrieved from the data using natural language processing techniques, this system makes use of ML classifiers including Decision Tree, Random Forest (RF), and Logistic Regression (LR). We evaluate each classifier’s performance using a variety of parameters. the best classifier will provide the outcome for real-time news prediction. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Logistic regression

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Enhancing Cardiovascular Disease Prediction Using Ensemble learning

Enhancing Cardiovascular Disease Prediction Using Ensemble L...

引用

international conference on computing Communication Control and Automation (ICCUBEA)

作者： Sharwari Ambade Diptee Chikmurge School of Computer Engineering MIT Academy of Engineering Alandi India

the proposed ensemble learning framework integrates diverse machine learning algorithms. Each base model is trained on a diverse set of features derived from comprehensive patient data. To evaluate the ensemble model's performance, a large-scale dataset comprising anonymized electronic health records from a diverse patient population is employed. the dataset includes longitudinal data, allowing for the incorporation of temporal information. Preliminary results demonstrate that the ensemble learning approach outperforms individual models in terms of predictive accuracy and stability. Moreover, the model exhibits robustness across different subpopulations, indicating its potential for generalizability and applicability in diverse healthcare settings. the accuracy metrics is being utilized as a foundation for assessing the effectiveness of all classifications. With an accuracy of 93.01% the stacking classifier has proven to be the finest model.

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A Survey on Drug Suggestion Mechanisms using machine learning Algorithm

A Survey on Drug Suggestion Mechanisms using Machine Learnin...

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international conference on ent computing and Control Systems (ICICCS)

作者： S Aravind V. Sivakumar School of Computer Science and Engineering (SCOPE) Vellore Institute of Technology Vellore India School of Computer Science and Engineering (SCOPE) Vellore Institute of Technology (VIT) Vellore India

this research study intends to bring convenience to any user to find the appropriate medicine that suits them. the application finds the brand and name of medicine that has the least side-effects and lack harmful chemicals. the primary objective of this research study is to design a prototype for a computer software that would make it simpler and more straightforward for users to evaluate the potential utility of a new pharmaceutical product in an unbiased manner.

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Approach to Predict Early Stage of Breast Cancer using machine learning

Approach to Predict Early Stage of Breast Cancer using Machi...

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international conference on ent computing and Control Systems (ICICCS)

作者： R.Raja Sekar Midigesi Rakesh Kumar Mandala Vishnu Vardhan Kuruba Venu Gopal C Jayakkam Allan Tilak Department of Computer Science and Engineering Kalasalingam academy of Research and Education Srivilliputhur Tamilnadu

In the world, breast cancer is regarded as one of the main factors that cause death for females between the ages of 20 and 59. Early detection and treatment can enable patients to receive appropriate care, hence lowering the rate of breast cancer morbidity. In line with research, most experienced doctors can correctly identify cancer with 79% accuracy, whereas deep learning algorithms can do so with 91% accuracy. this research study analyzes the most recent deep learning-based breast cancer models detection and classification and present them through a comparative study. Additionally, to make it easier for any future experiments and comparisons, the datasets that are used and available to usage are listed in this research work. Models based on decision trees, random forests, logistic regression, and support vector machines are the most up-to-date, most accurate models used in machine learning techniques

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Easy Job Eligibility Checking System using machine learning

Easy Job Eligibility Checking System using Machine Learning

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international conference on ent computing and Control Systems (ICICCS)

作者： Mangesh Balpande Sakshi Dagadu Mali Durgesh Sunil Chaudhari Nivedita Bapu Mali Ketaki Pradip Chaudhari Department of Information Technology SVKM’s Institute of Technology Dhule

this study presents a novel approach to automate the process of job eligibility verification. the suggested system examines the candidate’s qualifications and the job criteria using machine learning and natural language processing methods. the algorithm generates an eligibility score by comparing a candidate’s qualifications to the job’s criteria. Both recruiters and job seekers will have a smooth experience because to the suggested system’s powerful, effective, and user-friendly architecture. the system also gives suggestion of skills and courses required for the eligibility job. the system’s effectiveness was evaluated through extensive experimentation on a large dataset of job requirements and candidate qualifications, and the results demonstrated its high accuracy and effectiveness. the proposed system has the potential to revolutionize the job application process, making it easier and more efficient for both recruiters and job applicants.

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A Hybrid Gradient Boost Model for Intrusion Detection

A Hybrid Gradient Boost Model for Intrusion Detection

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international conference on computing Methodologies and Communication (ICCMC)

作者： R. Vaishali Dept. of Physical science University of Vavuniya Vavuniya Sri Lanka

Due to the advancement of network threats at present, it is crucial to conduct research on identifying and preventing network anomalies. machine learning (ML) is one strategy for Intrusion Detection System (IDS). Finding a reliable system to act as a networking shield is still difficult despite the fact that various IDS are suggested utilizing ML. this paper suggests a hybrid model combined with the gradient boost methods XGBoost and Lightgbm to forecast the various attacks that are urging in the network. To obtain the higher precision, hyperparameters of the algorithms are tuned. the proposed system is trained using the UNSW-NB15 dataset, which contains attacks for Generic, Exploits, Denial of Service (DoS), Shellcode, Fuzzer, and Reconnaissance. the system has an average accuracy of 99.89%. Because of the recent dataset training, the proposed system is relevant to modern Intrusion Detection Systems used in current network systems.

关键词： Training machine learning algorithms Computational modeling Intrusion detection Reconnaissance Predictive models Reliability

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