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Fusion of Feature Ranking Methods for an Effective Intrusion Detection System

computers, Materials & Continua 2023年第8期76卷 1721-1744页

作者： Seshu Bhavani Mallampati Seetha Hari School of Computer Science and Engineering VIT-AP UniversityAndhra PradeshIndia Center of Excellence AIand RoboticsVIT-AP UniversityAndhra PradeshIndia

Expanding internet-connected services has increased cyberattacks,many of which have grave and disastrous *** Intrusion Detection System(IDS)plays an essential role in network security since it helps to protect the network from vulnerabilities and *** extensive research was reported in IDS,detecting novel intrusions with optimal features and reducing false alarm rates are still ***,we developed a novel fusion-based feature importance method to reduce the high dimensional feature space,which helps to identify attacks accurately with less false alarm ***,to improve training data quality,various preprocessing techniques are *** Adaptive Synthetic oversampling technique generates synthetic samples for minority *** the proposed fusion-based feature importance,we use different approaches from the filter,wrapper,and embedded methods like mutual information,random forest importance,permutation importance,Shapley Additive exPlanations(SHap)-based feature importance,and statistical feature importance methods like the difference of mean and median and standard deviation to rank each feature according to its *** by simple plurality voting,the most optimal features are *** the optimal features are fed to various models like Extra Tree(ET),Logistic Regression(LR),Support vector Machine(SVM),Decision Tree(DT),and Extreme Gradient Boosting Machine(XGBM).Then the hyperparameters of classification models are tuned with Halving Random Search cross-validation to enhance the *** experiments were carried out on the original imbalanced data and balanced *** outcomes demonstrate that the balanced data scenario knocked out the imbalanced ***,the experimental analysis proved that our proposed fusionbased feature importance performed well with XGBM giving an accuracy of 99.86%,99.68%,and 92.4%,with 9,7 and 8 features by training time of 1.5,4.5 and 5.5 s on Network Security Laborat

关键词： Cyber security feature ranking imbalance preprocessing IDS SHap

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Improved Monarchy Butterfly Optimization Algorithm (IMBO): Intrusion Detection Using Mapreduce Framework Based Optimized ANU-Net

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computers, Materials & Continua 2023年第6期75卷 5887-5909页

作者： Kunda Suresh Babu Yamarthi Narasimha Rao School of Computer Science and Engineering VIT-AP UniversityAmaravathi522237Andhra PradeshIndia

The demand for cybersecurity is rising recently due to the rapid improvement of network *** a primary defense mechanism,an intrusion detection system(IDS)was anticipated to adapt and secure com-puting infrastructures from the constantly evolving,sophisticated threat ***,various deep learning methods have been put forth;however,these methods struggle to recognize all forms of assaults,especially infrequent attacks,because of network traffic imbalances and a shortage of aberrant traffic samples for model *** work introduces deep learning(DL)based Attention based Nested U-Net(ANU-Net)for intrusion detection to address these issues and enhance detection *** this IDS model,the first data preprocessing is carried out in three stages:duplication elimi-nation,label transformation,and data *** the features are extracted and selected based on the Improved Flower Pollination Algorithm(IFPA).The Improved Monarchy Butterfly Optimization Algorithm(IMBO),a new metaheuristic,is used to modify the hyper-parameters in ANU-Net,effectively increasing the learning rate for spatial-temporal information and resolving the imbalance *** the use of parallel programming,the MapReduce architecture reduces computation complexity while signifi-cantly accelerating *** publicly available data sets were used to evaluate and test the *** investigational outcomes suggest that the proposed technique can more efficiently boost the performances of IDS under the scenario of unbalanced *** proposed method achieves above 98%accuracy and classifies various attacks significantly well compared to other classifiers.

关键词： Intrusion detection system(IDS) attention based nested U-Net

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Optimized BERT: an effective attention layer based deep learning technique utilizing for multiword term extraction

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International Journal of Information Technology (Singapore) 2024年 1-13页

作者： Mandava, Mamatha Vinta, Surendra Reddy School of Computer Science and Engineering VIT-AP University Amaravathi India

Multiword term extraction refers to identifying and extracting meaningful phrases or expressions of multiple words from a given text or a corpus. These multiword terms often represent specific concepts, entities, or domain-specific terminology. Multiword terms often include compound words, combining multiple words to form a single term. Recognizing and extracting compound terms accurately can be challenging, especially when dealing with languages that have flexible word composition rules. To overcome the problems in multiword term extraction, propose a novel deep learning and attention-based technique. There are four steps to evaluate the process. First, collect the data from the datasets, clean the data, and then perform lowercasing, tokenization, stop word removal, stemming, and lemmatization. After that, perform n-grams extraction and part-of-speech tags in the feature extraction step. Then, extract the multiword terms using the attention layer of the pre-trained BERT method. Finally, optimize the hyper-parameters in the pre-trained BERT method using the Enhanced Northern Goshawk Optimization (ENGO) algorithm. The term extraction frameworks used in this study can be presented via the attention-based models. Results indicate that compared to the baseline pre-trained BERT word extraction model, the pre-trained BERT with attention layer can reach a high term extraction accuracy of 98.57%. This study also shows that attention-based models can find pertinent terms with strong associations to the categories found in clinical progress notes. © Bharati Vidyapeeth's Institute of computer applications and Management 2024.

关键词： Feature extraction Natural language processing Optimization Pre-processing Pre-trained BERT Term extraction

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FANCFIS: ensemble deep learning based features learning with a novel fuzzy approach for sentiment analysis

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International Journal of Information Technology (Singapore) 2024年 1-15页

作者： Bharath, Potu Venkatalakshmi, Dasari School of Computer Science and Engineering VIT-AP University Amaravati India

In the past decade, sentiment analysis has been a popular study area in data mining and natural language processing. In sentiment analysis, deep neural network (DNN) approaches have recently been used in tasks with encouraging results. The usage of deep neural networks for this purpose has grown significantly over the past few years, and the results have been considerable. DNN is showing promise as an approach. Because DNN can independently extract features from data, intermediate representations produced by these networks can also be employed as appropriate features. Therefore, we suggested an ensemble of ConvNeXt and PCFAN in this research to extract both low-level and high-level features. While many deep neural networks can extract different kinds of information because of their unique topologies, we merge features obtained from hybrid neural networks. To improve the entire effectiveness of sentiment analysis by considering their association. A multi-scale classifier called the Hierarchical Multi-scale LSTM (HMLSTM) receives intermediate representations from both introduced deep neural networks. Using the Sentiment140 and SST2 datasets, we simulate and assess the suggested approach. Our suggested model can outperform state-of-the-art approaches, as demonstrated by experimental findings on numerous public benchmark datasets. © Bharati Vidyapeeth's Institute of computer applications and Management 2024.

关键词： ConvNeXt Deep neural network (DNN) Hierarchical multi-scale LSTM (HMLSTM) Pyramid channel-based feature attention network (PCFAN) Sentiment analysis

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Comparative Analysis of Machine Learning Algorithm;for Conversion Predictors of Clinically Isolated Syndrome (CIS) to Multiple Sclerosis (MS) 4

Comparative Analysis of Machine Learning Algorithm;for Conve...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Mendanha, Serena Gudibandi, Keerthi Reddy Hajarathaiah, Koduru Gudibandi, Bharat Reddy Enduri, Murali Krishna School of Computer Science and Engineering VIT-AP University Amaravati India School of Electronics Engineering VIT-AP University Amaravati India Srm University Department of Computer Science and Engineering AP India

ISBN: (纸本)9798331523923

The central nervous system is impacted by multiple sclerosis (MS), a chronic neurological condition that causes significant cognitive and physical deficits. Better disease management and prompt intervention depend on early and precise detection. Effective diagnosis of MS is hampered by a number of factors, such as small datasets, significant clinical presentation variability, difficult feature selection, model generalization problems, and integration of multimodal data such as magnetic resonance imaging and genetic markers. Several machine learning (ML) models are assessed in this study in order to predict the development of clinically isolated syndrome (CIS) to multiple sclerosis. Using clinical and magnetic resonance data from patients with CIS at risk of developing MS, we evaluate the effectiveness of support vector machines (SVM), K nearest neighbor (KNN), decision trees (DT), random forests (RF), logistic regression (LR), Gaussian naive Bayes (Gaussian NB) and XGBoost (XG). Evaluation is performed using performance criteria including F1 score, recall, precision, and precision. According to our research, Random Forest has the best prediction accuracy, which makes it a potentially useful tool for helping doctors diagnose and treat MS patients early. Notwithstanding these developments, issues like model interpretability and data scarcity still exist. In order to improve diagnosis precision, future research will concentrate on enhancing these models by integrating deep learning methods, genetic markers,, and more advanced imaging modalities. © 2025 IEEE.

关键词： Magnetic resonance imaging

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Classification Algorithms for Liver Epidemic Identification

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EAI Endorsed Transactions on Pervasive Health and Technology 2023年 9卷 1-13页

作者： Makkena, Koteswara Rao Natarajan, Karthika School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India

Situated in the upper right region of the abdomen, beneath the diaphragm and above the stomach, lies the liver is a crucial organ essential for the proper functioning of the body. It performs many important functions in the body. The principal tasks are to eliminate generated waste produced by our organs, and digestive food and preserve vitamins and energy materials. It performs many important functions in the body, It Regulating the balance of hormones in the body Filtering and removing bacteria, viruses, and other harmful substances from the blood. In certain dire circumstances, the outcome can unfortunately result in fatality. There exist numerous classifications of liver diseases, based on their causes or distinguishing characteristics. Some common categories of liver disease include Viral hepatitis, Autoimmune liver disease, Metabolic liver disease, Alcohol-related liver disease, Non-alcoholic fatty liver disease, Genetic liver disease, Drug-induced liver injury, Biliary tract disorders. Machine learning algorithms can help identify patterns and risk factors that may be difficult for humans to detect. With This clinician can enable early diagnosis of diseases, leading to better treatment outcomes and improved patient care. In this research work, different types of machine learning methods are implemented and compared in terms of performance metrics to identify whether a person effected or not. The algorithms used here for predicting liver patients are Random Forest classifier, K-nearest neighbor, XGBoost, Decision tree, Logistic Regression, support vector machine, Extra Trees Classifier. The experimental results showed that the accuracy of various machine learning models-Random Forest classifier-67.4%, K-nearest neighbor-54.8%, XGBoost-72%, Decision tree-65.1%, Logistic Regression-68.0%, support vector machine-65.1%, Extra Trees Classifier-70.2% after applying Synthetic Minority Over-sampling technique. © 2023 K. R. Makkena et al., licensed to EAI.

关键词： Decision trees

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Telugu News Classification Using N-gram Model 3

Telugu News Classification Using N-gram Model

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Chetan, Gudditi Lavanya, Kotha Kumar, Manjunaatt Girish Naseeba, Beebi School of Computer Science and Engineering VIT-AP University Vijayawada India School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India

ISBN: (纸本)9798331518592

Putting text documents into predetermined genres or categories is a critical operation in natural language processing (NLP) known as genre categorization. While a significant amount of research has been done on genre classification for languages that are widely spoken, like English, studies on less frequently spoken languages, like Telugu, are noticeably lacking. This research uses a dataset of 10,000 articles across five categories: business, sports, nation, entertainment, and editorial. The classification was performed using an N-gram model combined with the Multinomial Naive Bayes algorithm, implemented with Python libraries like NLTK and scikit-learn. The model achieved an overall accuracy of 93%, with high precision, recall, and F1-scores for all categories, demonstrating effective classification performance. © 2024 IEEE.

关键词： Adversarial machine learning

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Ensemble Machine Learning approach for Biological Bird Species Detection and Classification 3

Ensemble Machine Learning Approach for Biological Bird Speci...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Lavanya, Kotha Chetan, Gudditi Kumar, Manjunaatt Girish Challa, Nagendra Panini School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India School of Computer Science and Engineering VIT-AP University Vijayawada India

ISBN: (纸本)9798331518592

Due to different ecological balances, there is need to identify the bird species which are varied from multiple locations. This research utilizes a novel ensemble platform, which combines Convolutional Neural Network (CNN), Support Vector Machine (SVM), and Random Forest models to categorize various bird species from various images. The literature survey focusses on various techniques to overcome the difficulties of bird species identification based on traditional recognition models. By integrating CNN, SVM, and Random Forest models - a hybrid model - achieved a 97% accuracy. © 2024 IEEE.

关键词： Forest ecology

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DeepFake Videos Detection Using Crowd Computing

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International Journal of Information Technology (Singapore) 2024年第7期16卷 4547-4564页

作者： Salini, Yalamanchili HariKiran, J. School of Computer Science and Engineering VIT-AP University Amaravati 522237 India

In recent times, the widespread dissemination of misinformation through realistic artificially generated images and deep faked videos has become a common issue, particularly prevalent on social media platforms targeting popular personalities. Detecting such deep fake content has become increasingly challenging as the technology behind it continues to advance, making it difficult for existing algorithms to avoid false positives. This paper focuses on deepfake detection and makes two key contributions: We propose automated deep fake detection as a supplement to human cognitive abilities, addressing the lack of studies quantifying human capabilities in identifying faked videos from multi-dimensional perspectives. To fill this gap, we conducted experiments with individuals from diverse age groups and professions, comparing their evaluations against automated approaches using state-of-the-art deep fakes. Employing various machine learning algorithms, we predict an individual's ability to correctly classify a video as genuine or fake. By combining human cognitive abilities with video statistical metadata, we achieved an impressive accuracy of 98.3% in predicting participant decisions on video authenticity. To our knowledge, this marks the first attempt to blend these two factors in the context of deepfake detection. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer applications and Management.

关键词： Deep fake detection Deep fake generation Deep fakes GANs

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Classification of Water Quality and Impurity Detection Using Machine Learning 5

Classification of Water Quality and Impurity Detection Using...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Selva Kumar, S. Kaur, Prableen Siddique Ibrahim, S.P. Gokulnath, B.V. School of Computer Science and Engineering VIT-AP University India

ISBN: (纸本)9798331518097

Water quality assessment is considered one of the most necessary measures taken to safeguard public health and maintain ecological balance. This work aims to design a machine learning-based framework for determining the classification of water quality and impurity level based on key physical, chemical, and biological parameters. The dataset was collected from diversified water bodies across multiple regions and included measurements such as pH, Biological Oxygen Demand (BOD), conductivity, temperature, nitrate and nitrite concentration, and coliform bacteria levels. After scrubbing of data for sanitizing it, we had applied the machine learning model like Gradient Boosting which calculates an impurity measure about every place. The important sources of water pollution for a specified region are determined by making use of feature significance analysis. Power BI was used to further visualize the quality of water and prime factors driving the water quality in different regions. It generated an interactive map with dynamic visualisation of impurity hotspots across several places. Users can hover over specific places to get massive information about the key pollutants responsible for the deterioration of water quality. The results of the study point out certain areas that are contaminated with certain types of pollutants, including industrial areas that have higher BOD or nitrates that exceed the permissible limits in agricultural areas. © 2024 IEEE.

关键词： Nitrates

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