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machine learning in Cybersecurity: Threat Detection and Response 1

Machine Learning in Cybersecurity: Threat Detection and Resp...

引用

1st international conference on AIML-Applications for Engineering and Technology, ICAET 2025

作者： Gadad, Tangevva Rudrappa Varshitha, R. Yoghana, K.B. Singh, Yumlembam Henba Dakshina Kannada India

ISBN: (纸本)9798350355611

ML has come up as a potent technology in the domain of cybersecurity, where it offers novelty solutions for the detection and responses to threats. With the integration of ML in cybersecurity frameworks, the automation anomaly will be possible to help identify nascent threats and enable such mechanisms to answer attacks with relevant efficiency in real-time. Traditional measures in cybersecurity mainly rely on predefined rule-based and signature-based techniques which turn out to be weak while combating advanced, fast-evolving cyber threats. This drawback in the current approach is taken care of by machine learning, as it analyzes huge amounts of data in the identification of patterns and behaviours that give indications of malicious activities. It gives an added advantage in the identification of zero-day exploits, APTs, and other classes of attacks that bypass conventional security mechanisms. An example is that it is theoretically possible to train the various ML algorithms to recognize normal behavior of the network;thus, they could identify any deviation that could result in an attack. [1] For example, in supervised learning models, learning may be supervised on a classified labeled dataset that classifies network traffic as malicious or benign, while unsupervised learning models identify the given anomalies that may exist in unknown threats. Reinforcement learning can also optimize a defensive strategy by learning from the environment and adjusting responses based on the effectiveness of previous actions. Incident response automation is one more substantial role of ML outside its threat detection capability. Understanding the nature of the attack, ML systems can suggest, and sometimes even apply, countermeasures that would reduce time to negate the impact of a breach. In other words, the automation is vital for environments in which the amount of alerts was too high to be managed by human analysts, therefore causing the response to be delayed and resulting in

关键词： Adversarial machine learning

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Application of machine learning algorithms in Artificial Intelligence Image Recognition Technology 1

Application of Machine Learning Algorithms in Artificial Int...

引用

1st international conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Ouyang, Qi Dai, Siyang Xie, Kexin Sun, Zeyu Wang, Yao Polytechnic of Milan Milan Italy Faculty of Engineering and Information Technology The University of Melbourne VIC Australia College of Business and Public Management Wenzhou-Kean University Wenzhou China School of Environment and Science Griffith University Brisbane Australia Nanjing Stomatological Hospital Affiliated Hospital of Medical School Institute of Stomatology Nanjing University Nanjing China

ISBN: (纸本)9798331529246

In smart city applications such as medical image analysis, autonomous driving, and security monitoring, image recognition faces challenges like complex backgrounds, low-quality images, and diverse targets, affecting accuracy and robustness. This study explores machine learning algorithms to enhance image recognition performance by addressing noise interference, intricate backgrounds, and feature extraction difficulties. It combines Convolutional Neural Networks (CNN) with transfer learning, starting with data preprocessing to reduce noise, using pre-trained CNN models to extract high-level features, and fine-tuning with a ResNet transfer learning strategy for specific tasks. Additionally, ensemble learning methods are employed to further improve model robustness and accuracy. Experimental results show that the ensemble model maintains around 85% accuracy even with high background complexity, and transfer learning achieves 90% accuracy when the sample size reaches 1000. These findings demonstrate that transfer and ensemble learning effectively enhance image recognition accuracy and resilience in complex environments. © 2025 IEEE.

关键词： Adversarial machine learning

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Context-Aware Selection of machine learning as a Service (MLaaS) in IoT Environments

Context-Aware Selection of Machine Learning as a Service ...

引用

PhD Symposium, Posters, Demos, and A Web for more inclusive, sustainable and prosperous societies, WEB-for-GOOD 2024 and 1st international Workshop on AI and Web Data Analytics, AIWDA 2024 form the 25th international conference on Web Information Systems Engineering, WISE 2024

作者： Patel, Keya Mistry, Sajib Kanneganti, Deepak Krishna, Aneesh School of Electrical Engineering Computing and Mathematical Sciences Curtin University Perth Australia

ISBN: (纸本)9789819614820

The integration of machine learning as a Service (MLaaS) into the Internet of Things (IoT) environments presents considerable opportunities for enhancing decision-making and automation. We propose a novel framework for context-aware selection of MLaaS in IoT settings, aimed at optimising the interaction between IoT users’ activities and machine learning services. Our framework considers various contextual dimensions, such as user preferences, locations, IoT device capabilities, and application requirements, to develop a dynamic selection process. By employing context-aware algorithms, our approach seeks to enhance the efficiency, accuracy, and responsiveness of IoT systems. We propose a context change analysis algorithm based on support vector machines (SVM). We develop a contextual bandits algorithm along with skyline queries to achieve optimal mapping between abstract MLaaS services and concrete MLaaS services for quality of service (QoS) attributes. Experiments conducted with real-world and simulated datasets demonstrate the effectiveness of our proposed methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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A machine learning Approach to Short-Term Price Forecasting of Agricultural Crops in India 1

A Machine Learning Approach to Short-Term Price Forecasting ...

引用

1st international conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Ahmed, Abu lmran Walmart Global Tech SunnyvaleCA94086 United States

ISBN: (纸本)9798331518578

This research presents a comprehensive methodology for short-term price forecasting of orange and cotton crops in the Indian agricultural market, utilizing a robust dataset from the Indian Government. The study employs a series of machine learning algorithms, including Artificial Neural Networks (ANN), Support Vector Regression (SVR), Linear Regression (LR), and Auto-Regressive Integrated Moving Average (ARIMA), which serves as a benchmark for comparative analysis. Data preprocessing, exploratory data analysis (EDA), and feature engineering were conducted to address challenges posed by missing values and to extract significant patterns from the dataset. The results demonstrate the model's effectiveness in accurately predicting crop prices across various geographic and seasonal contexts. The study highlights the implications of identified trends for decision making within the agricultural supply chain, emphasizing the potential for enhanced resilience and sustain ability in India's agricultural sector. Future work will focus on refining the model for broader applications, addressing evolving market dynamics, and integrating additional external factors influencing crop prices. © 2025 IEEE.

关键词： Support vector regression

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1st international conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

1st International Conference on Advances in Computer Science...

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1st international conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

ISBN: (纸本)9798331518578

The proceedings contain 260 papers. The topics discussed include: application of wide bandgap in power electronics: next-gen smart grids;flexible hybrid electronics for printing of nano and chip scale structures;reliability of electronic connections in fast chargers of electric vehicle;enhancing substation earthing system: a finite element method for minimizing ground resistance and analyzing electric potential and temperature distribution;design and analysis of thermal management systems for an electric vehicle battery;analysis of smart city solutions for sustainable urban growth in India;an approach for the reliability realization of dynamic wireless charging of electric vehicle system;analyzing the impact of blockchain technology on financial transactions and accounting practices in global trade;optimization of electric discharge machining process parameters using simulated annealing optimization algorithm;and machine learning for wireless network optimization: algorithms and applications for adaptive communication.

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Leveraging machine learning algorithms for Early Detection of Major Depressive Disorder: A Deep learning Approach with Twitter Data 1

Leveraging Machine Learning Algorithms for Early Detection o...

引用

1st international conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Markose, Ginoop Chennekkattu Leading Health Insurance Company RichmondVA United States

ISBN: (纸本)9798331518578

Social media plays a significant role in daily lives of people where they share the topics and contents to express their feelings, events experiences and thoughts. Due to extensive use of social media, huge volume of data is generated. The data comprised of textual content allows to collect large volume of relevant information using deep learning techniques. The primary aim of the study is early detection of major depressive disorder through extracted data from Twitter platform. The study uses binary classification models for treating the mental disorder and achieved satisfactory results. The proposed methodology includes deep learning technique designed with effective multi class deep learning model to detect major depressive disorder. The well-defined methodology includes tweet analysis by testing wide range of deep learning models such as simple variant such as GRU and LstM and hybrid variant like CNN-BiGRU. The present work is validated by comparing it with different data sets and existing binary classification models. The Binary class models effectively assume the collected data and utilisation of multi class model. The findings of the study shows that the performance of the proposed model is comparatively high when with other existing models. © 2025 IEEE.

关键词： Tweets

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machine learning Approaches for Musical Instrument Recognition using Distinctive Audio Features 1

Machine Learning Approaches for Musical Instrument Recogniti...

引用

1st international conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Bhagyalakshmi, R. Anandaraju, M.B. B G S Institute of Technology Adichunchanagiri University Department of Ece Mandya India Governament Engineering College Hassan India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529246

Automatic musical instrument recognition, being part of the machine listening, is an important task. In this work, we are concerned with instrument recognition for multi-instrument. In this work, the goal will be to consider different approaches to classification of signal containing audio data in music. Main objectives of this work included the understanding of how various features make two or more instrumental sounds unique, the identification of how these features can be extracted in the most effective matter and the final one was to study and to identify the type of the instrument mostly playing in a specific musical file with the help of suitable machine learning algorithms. This study proved that the proposal to identify the pre-dominant instrument of a song is plausible with the help of machine learning techniques. This work contributes to the field of automatic musical instrument recognition by focusing on the classification of multi-instrument audio signals. It offers a practical framework to identify and analyze features that can distinguish instrumental sounds and how to extract and use these features together with appropriate machine learning algorithms to detect the main instrument in audio files. Based on the study, it is found that it is possible to selectively identify the principal instrument in a song, which requires only a big amount of time and the efficient machine learning approaches used in the research offer a high level of accuracy. Considering the presented results, it becomes clear that spectral features concern instrument timbre discriminability and are important to the classification process. In the context of the proposed method, it was possible to achieve nearly 91% accuracy in identifying whole families of instruments and particular instruments belonging to these families, which proves the effectiveness of the stated approach. © 2025 IEEE.

关键词： Adversarial machine learning

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Seismopredict: Advanced machine learning for Earthquake Damage Assessment 1

Seismopredict: Advanced Machine Learning for Earthquake Dama...

引用

1st international conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Amerthen, S. Aron Nambikai Fernando, J. Praba, M. St.Joseph's Institute Of Technology Chennai India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529246

The 2015 Gorkha earthquake highlighted the urgent need for predictive models to assess structural damage levels effectively. This study proposes an advanced machine learning framework employing gradient boosting algorithms - XGBoost, CatBoost, and LightGBM - to predict building damage levels. The models utilize construction attributes and geographic features. Key innovations include tailored feature engineering, hyperparameter tuning using GridSearchCV, and a detailed comparative evaluation against traditional methods like Random Forest and Logistic Regression. LightGBM emerged as the best-performing model, achieving 0.85 accuracy and exceptional computational efficiency due to its leaf-wise growth strategy. These findings can significantly aid disaster management by enabling precise risk assessments and efficient resource allocation. The paper provides mathematical foundations, implementation details, and interprets results to validate the approach's practicality. © 2025 IEEE.

关键词： Risk assessment

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Leveraging AI and machine learning for Enhanced Fraud Detection in Digital Banking System: A Comparative study 1

Leveraging AI and Machine Learning for Enhanced Fraud Detect...

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1st international conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Nair, Sreejith Sreekandan Lakshmikanthan, Govindarajan Belagalla, Ningaraj Belagalla, Santosh Ahmad, Samaila Kasimu Farooqi, Shaista Ashraff Anna University Department of Electronics and Communication Chennai India School of Agriculture SR University Department of Entomology Ananthasaga Telangana India A R Jahagirdar BCA College Bagalkot University Department of Computer Science and Application Ilkal Karnataka Jamkhandi India Sharda University Uttar Pradesh Greater Noida India Department of Computer Science Selangor Malaysia

ISBN: (纸本)9798331518578

The merger of artificial intelligence (AI) and machine learning (ML) technologies has drastically changed the field of fraud detection in digital banking in recent years. The increasing frequency of digital transactions in contemporary financial systems has increased the risk of credit card fraud (CCF), necessitating the use of effective detection techniques. Despite the potential of machine learning (ML) technology, dataset imbalance limits the accuracy of fraud detection. This study examines how well data balancing strategies and ensemble learning approaches can identify CCF in unbalanced datasets. This study examines how well data balancing strategies and ensemble learning approaches can identify CCF in unbalanced datasets. In addition to evaluating data balancing strategies like SMOTE, ADASYN, and Random Oversampling, we examine four machine learning algorithms: KNN, Decision Tree, SVM, and LstM. Significant gains were made by using balancing techniques: The greatest results are obtained with ROS on LstM, with Recall 89.6%, F1-Score 91.5% and Precision 98.4%, With Precision 97.6%, Recall 91.5%, and F1-Score 93.9%, Random Oversampling ranks second on SVM, whereas SWOT on SVM is third with Precision 91.7%, Recall 86.3%, and F1-Score 89.9 %. The study's findings demonstrate that using SVM and LstM classifiers in conjunction with oversampling strategies like Random Oversampling yields precise and unaffected results for identifying CCF in unbalanced datasets, assisting in the development of trustworthy fraud detection systems for practical uses. © 2025 IEEE.

关键词： Adversarial machine learning

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Estimation of resonant frequency of Split Ring Resonator (SRR) using machine learning 1st

Estimation of resonant frequency of Split Ring Resonator (SR...

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1st international conference on Applications of AI in 5G and IOT, ICAAI5GI 2024

作者： Audu, Eliazar Elisha Eteng, Akaa Agbaeze Iyameh, Uchendu Omijeh, B.O. Technology and Innovation NNPC Limited FCT Abuja Nigeria Department of Electrical/Electronic Engineering University of PortHarcourt Nigeria Centre for Information and Telecommunication Engineering University of PortHarcourt Nigeria

ISBN: (纸本)9781032874036

Metasurface, a composite array of subwavelength particles, has emerged as a cost effective, efficient and elegant prescription for manipulating the phase, amplitude and direction of electromagnetic waves in 5G radio environment and millimeter waves communications. The operating range and bandwidth of metasurfaces depends on the resonant frequency of the sub-wavelength particles called resonators or meta-atoms. However, modeling metasurfaces using full-wave 3D computation EM solvers provides good approximation of resonant frequency via scattering parameters at the expense of significant computational storage and execution time. While analytical models provide a good cost to design efforts, they require parameter optimization to produce comparable accuracy to numerical and experimental values. In this paper, Artificial Neural Network (ANN) is presented as an alternative to numerical and analytical. Using analytical and numerical data from studies of split ring resonators (SRR), machine learning model for predicting resonant frequency at arbitrary geometrical parameters of SRR is developed and trained by supervised machine learning. For all the test data inputs, the mean accuracy of ANN model was 98.73% which is very to the target resonance frequency. The minimum and maximum frequency deviation in the test cases were 0.29% and 2.64% respectively. © 2025 the Author(s).

关键词： Frequency estimation

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