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An Enhanced Intrusion Detection System Model Using Machine learning Algorithm 4

An Enhanced Intrusion Detection System Model Using Machine L...

引用

4th international conference on Emerging Systems and Intelligent computing, ESIC 2024

作者： Chandra, Leena Singh Singh, Devendra Kumar Guru Ghasidas Vishwavidyalaya Department of Computer Science and Engineering Bilaspur India

ISBN: (纸本)9798350349856

Intrusion Detection Systems (IDS) play a pivotal role in safeguarding the integrity and performance of an organization. Throughout recent years, various approaches have been devised and put into practice to fortify the security, reliability, and availability of computer networks within enterprises. Our research specifically on IDSs constructed using Machine learning (ML) techniques. In our research, we have conducted an in-depth analysis of the CICIDS2017 intrusion detection dataset, which serves as the foundation for training and evaluating our machine learning models. We have implemented several machine learning approaches, including Decision Trees (DT), Extra Trees, Random Forest, and XGBoost, to study their effectiveness in detecting intrusions and ensuring the security of computer systems. In our experiments, we explored the use of Principal Component Analysis (PCA) for feature selection and HyperOpt for optimization. Our findings revealed that the XGBoost classifier achieved the highest accuracy among all three other methods (DT,ET,RF) the methods, with an impressive accuracy rate of 99.331%. This suggests that XGBoost is particularly effective in our context. © 2024 IEEE.

关键词： Decision trees

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Recognition of Secure Data Transmission in Cloud Platform using Deep learning 3

Recognition of Secure Data Transmission in Cloud Platform us...

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3rd international conference on Applied Artificial Intelligence and computing, ICAAIC 2024

作者： Bhavya, R. Kumar, M. Guru Vimal Ramya, U.M. Janagi, R. Ganesan, M. Deepa, S. New Horizon College of Engineering Department of Computer Science and Engineering Bangalore India Vel Tech Rangarajan Dr. SangunthalaR&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Sri Krishna College of Engineering and Technology Deparment of Information Technology Coimbatore India Bannari Amman Institute of Technology Department of Computer Science and Engineering Erode India Hindusthan College of Engineering and Technology Deparment of Information Technology Coimbatore India Kongu Engineering College Perundurai Department of Computer Technology-PG Erode638060 India

ISBN: (纸本)9798350375190

The recognition of secure data transmission in a cloud platform requires the integrity of sensitive information with higher confidentiality. This is obtained using the aid of deep learning techniques. The robustness of the system is improved using a random forest algorithm. In secure data transmission, the deep learning algorithms are trained to adopt various patterns of communication with the identification of certain anomalies. They result in security threats. They involve suspicious user behaviour with unexpected information exchange. The overall accuracy and interpretability of the system are achieved using a random forest algorithm which combines multiple decision trees. They are used for handling the high-dimensional data and obtaining the relationship between complex patterns. They helps in the identification of various security threats in the data communication through cloud platforms. This helps in the automatic decision-making process during malicious behaviour. The obtained dataset contains both the secure and insecure datasets. The extracted features acts as an input for the random forest classifier. These features are analyzed by the random forest and provides information that helps to influence the decision-making process. The integrated approach improves the accuracy of the security threats with enhancing transparency in the automatic decision-making techniques. They provide trust in the security measures in the cloud platforms. The collaboration of deep learning with a random forest algorithm helps in obtaining secure data transmission. © 2024 IEEE.

关键词： Behavioral research

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Predicting Lung Cancer using XGBoost and other Ensemble learning Models 14

Predicting Lung Cancer using XGBoost and other Ensemble Lear...

引用

14th international conference on computing Communication and Networking Technologies, ICCCNT 2023

作者： Singh, Divanshu Khandelwal, Avish Bhandari, Pawan Barve, Sunita Chikmurge, Diptee Mit Academy of Engineering Information Technology Alandi Pune India Mit Academy of Engineering School of Computer Engineering Alandi Pune India

ISBN: (纸本)9798350335095

Abnormal reproduction of cancerous cell growths in the lungs leads to highly fatal lung cancer. Early detection is essential for reducing death rates of lung cancer. New research has shown that machine learning algorithms can effectively diagnose lung cancer from medical datasets. This paper reviews the predictive effectiveness of various machine learning ensemble techniques used in lung cancer detection, like SVM, XGBoost, LightGBM, AdaBoost, CatBoost, and Random Forest with their performance analysis. Boosting techniques AdaBoost and XGBoost outperformed other algorithms with accuracy scores of 96.77% and 96.76% respectively. Therefore, we conclude that machine learning-based techniques hold great promise for improving lung cancer diagnosis and reducing mortality rates. © 2023 IEEE.

关键词： Adaptive boosting

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Unveilling the Effectiveness of LSTM with TF-IDF in Assamese Poem Classification:A Comparative Analysis with Conventional and Deep learning Approaches 15

Unveilling the Effectiveness of LSTM with TF-IDF in Assamese...

引用

15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Dutta, Bikokhita Neog, Mandira Baruah, Nomi Dibrugarh University Computer Science & Engineering Dept Dibrugarh India

ISBN: (纸本)9798350370249

The classification of Indian poetry extends to various genres reflecting the rich cultural heritage and linguistic diversity. In this paper we are classifying the Assamese poems according to their sentiments and implementing it by using one of the deep learning approach i.e. Long short-term memory (LSTM) with TFIDF (Term Frequency-Inverse Document Frequency) vectorization. For our study we have collected a dataset of 341 Assamese poems, categorized into four themes such as 'biroh' (sadness),'prem' (love),'prokriti' (nature) and 'swadekh prem' (patriotic).We are also comparing the experimental outcome given by our proposed model with some other machine learning and deep learning approaches like Naive Bayes (NB), Support Vector Machine (SVM) and Convolution Neural Network (CNN). Among these our proposed model gives a higher accuracy of 94%, surpassing the performance of other models. © 2024 IEEE.

关键词： Assamese Convolution Neural Network(CNN) Long Short Term Memory(LSTM) NB(Naive Bayes) poem Support Vector Machine(SVM) TF-IDF

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Deep Reinforcement learning for Dynamic VNF Profiling and Resource Optimization 3

Deep Reinforcement Learning for Dynamic VNF Profiling and Re...

引用

3rd international conference on Intelligent computing and Next Generation Networks, ICNGN 2024

作者： Jaisudthi, Pratchaya Phungkoed, Natthakran Srisuk, Kanyaphak Phueaknumpol, Vasupon Sridee, Pachara Threerapat Computer Engineering Rambhai Barni Rajabhat University Chanthaburi Thailand Computer Network Technology Rambhai Barni Rajabhat University Chanthaburi Thailand

ISBN: (纸本)9798331529222

This paper presents an AI-driven framework for multi-objective Virtual Network Function (VNF) profiling, utilizing deep reinforcement learning (DRL) to optimize key resources such as CPU, memory (MEM), and link capacity (LC) in dynamic network environments for next-generation networks. The proposed DRL model was tested in two scenarios: vFW VNF and Snort Inline Mode VNF, to evaluate its effectiveness in resource optimization. Compared to traditional reinforcement learning (RL), DRL demonstrated approximately a 10% reduction in CPU usage across both scenarios. Additionally, DRL achieved an 8% reduction in memory usage for the vFW VNF scenario and optimized LC allocation by approximately 23% for Snort Inline Mode VNF and 8% for vFW VNF. These results highlight DRL’s superior adaptability and efficiency in complex, fluctuating environments, making it a promising approach for real-time VNF resource management in next-generation networks. ©2024 IEEE.

关键词： Deep reinforcement learning

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Development of a Machine learning-Based System for Optimizing Crop Recommendations 1

Development of a Machine Learning-Based System for Optimizin...

引用

1st IEEE international conference on Advances in computing, Communication and Networking, ICAC2N 2024

作者： Tomar, Vinay Sharma, Gaurav Rajkumar, Rahul Singh, Akanksha Dhakare, Sarvendra Singh Kumar, Kamlesh Jiwaji University Dept of Chemistry Gwalior India Technical Architect Espire Infolabs Gurgaon India Dhanalakshmi Srinivasan College of Engineering Dept. of Food Technology Coimbatore India Mangalayatan University Dept. of Agriculture Aligarh India J.S University Dept. of Agriculture Shikohabad India

ISBN: (纸本)9798350356816

In precision agriculture, crop recommendation systems play a crucial role in enhancing crop productivity. This research paper proposes a machine learning-based crop recommendation system that leverages climatic variables - such as temperature, humidity, and rainfall - as well as soil characteristics, including nitrogen, potassium, and pH levels. Utilizing a dataset that integrates soil parameters, climate data, and corresponding crop yield information, we aim to train and evaluate several machine learning algorithms to determine their efficacy in providing crop recommendations. By comparing the performance of these algorithms, our proposed system is designed to assist farmers and agricultural experts in selecting and managing crops more effectively, thereby improving overall crop yields and productivity. © 2024 IEEE.

关键词： Adversarial machine learning

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Conformal prediction based on principal component analysis for high-dimensional outlier detection

Conformal prediction based on principal component analysis f...

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2024 international conference on Cloud computing, Performance computing, and Deep learning, CCPCDL 2024

作者： Qian, Xiaoyu Wu, Jinru Lin, Youwu School of Mathematics and Computing Science Guangxi Colleges Universities Key Laboratory of Data Analysis and Computation Guilin University of Electronic Technology Guilin541002 China Guilin541002 China

ISBN: (纸本)9781510683044

Current outlier detection methods exhibit many limitations in high-dimensional settings. Traditional statistical approaches rely on strong assumptions and lack practicality and generality. Meanwhile, despite the better performance, machine learning methods suffer from low interpretability and reliability due to their complex mechanisms and the absence of confidence estimation. Although outlier detection methods based on principal component analysis (PCA) have shown some advantages by extracting important features from high-dimensional data, they have not entirely solved these problems. Conformal prediction, a finite-sample distribution-free uncertainty quantification method recently applied to outlier detection, produces a set-valued prediction with a Type-I error guarantee and false discovery rate control. This paper proposes a new distribution-free method for high-dimensional outlier detection, PCA-CP, combining principal component analysis and conformal prediction. PCA-CP overcomes the shortcomings of previous methods, demonstrating high generality and reliability while achieving high performance. Experiments on simulation and real data show that PCA-CP outperforms previous methods, achieving higher power and lower False Discovery Rate, thus proving its significant advantages. © 2024 SPIE.

关键词： Prediction models

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Crane hydraulic system experimental teaching reform mode research based on AMESim 24

Crane hydraulic system experimental teaching reform mode res...

引用

2024 international conference on Artificial Intelligence and Future Education, AIFE 2024

作者： Han, Guangdong Zhang, Jinhui Zhang, Meng Wang, Bingyu Chen, Haiquan Wang, Shenghai Marine Engineering College Dalian Maritime University Liaoning Dalian China

ISBN: (纸本)9798400710650

The research on the reform paradigm of crane hydraulic system experimental instruction based on AMESim seeks to investigate how to optimize the conventional crane hydraulic experimental teaching through simulation technology and enhance the instructional quality as well as students' hands-on capabilities. The study integrates AMESim software as an instructional tool, and by constructing a virtual model of the crane hydraulic system, students can conduct experiments in a safe and flexible environment, overcoming the bottleneck of high equipment costs and operational constraints in traditional teaching. The reform paradigm merges simulation experiments with real-world operations, enabling students to gain a deeper comprehension of the working principles and dynamic characteristics of the crane hydraulic system while fostering problem-solving skills through the simulation of failures and variations in working conditions. The findings demonstrate that the AMESim-based instructional model not only boosts students' engagement and proactiveness in learning but also enhances the efficiency and effectiveness of experimental teaching, advancing the modernization and digital transformation of crane hydraulic system experimental instruction. © 2024 Copyright held by the owner/author(s).

关键词： Students

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Development and Evaluation of a Google Chrome Extension for Real-Time Emotion Detection in Web Text Using Machine learning and Natural Language Processing 1

Development and Evaluation of a Google Chrome Extension for ...

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1st IEEE international conference on Advances in computing, Communication and Networking, ICAC2N 2024

作者： Sharma, Vinod Prajapat, Jitendra Dwivedi, Abhinay Kumar Sharma, Swati Prajapati, Lakshya Jadon, Kaushiki SLP College Dept of CSE Gwalior India Madhav University Dept. of Pharmacy Pindwara India Lingayas Vidyapeeth Dept. of English & Psychology Faridabad India Chandigarh University Dept. of Com. Science Engineering Chandigarh India J.S. University Dept. of Computer Science Shikohabad India

ISBN: (纸本)9798350356816

This research paper introduces a novel approach that leverages machine learning and natural language processing (NLP) techniques to detect emotions in text. The proposed system includes a Google Chrome extension that allows users to swiftly and effortlessly evaluate the emotional content of the websites they visit. It employs various NLP methods, such as keyword extraction and tokenization, to extract relevant features from the text. These features are then used to train a machine learning model capable of accurately predicting the emotional content of new text. The system demonstrates high accuracy on multiple benchmark datasets, showcasing its effectiveness in real-world applications. © 2024 IEEE.

关键词： Adversarial machine learning

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Sarcasm Detection in Newspaper Headlines 4

Sarcasm Detection in Newspaper Headlines

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4th international conference on Intelligent Technologies, CONIT 2024

作者： Sreenivas Prasad, B. Akash Babu, N. Harithikeswar Reddy, T. Vishnu Vardhan Reddy, A. Vekkot, Susmitha Nair, Priyanka C. Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Bengaluru India Amrita School of Engineering Department of Electronics & Communication Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350349900

In the current age of information saturation, where headlines serve as primary points of engagement, the pervasive use of sarcasm in news headlines poses a challenge for readers' comprehension, failing to discern sarcasm may lead to misunderstandings, misinformation dissemination, and harm journalistic integrity. This study addresses this critical issue and enhances the credibility of news sources by employing various ensemble learning algorithms and deep learning models with different types of word embedding, the evaluation includes various possible performance metrics and a complete correlation of the outcomes. XGBoost consistently outperforms other models with higher accuracy, precision, recall, and F 1-score in sarcasm detection. Among deep learning models, BiLSTM emerges as the top performer, achieving 0.86 accuracy, capturing nuanced language cues effectively and appears as the optimal model for sarcasm detection. BERT stands out with superior F1 scores, showcasing its exceptional ability in deciphering sarcastic language nuances. © 2024 IEEE.

关键词： Contrastive learning

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