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An effective intrusion detection system based on feature selection and regularized long short-term memory classifier

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Multimedia Tools and Applications 2025年 1-24页

作者： Chikkalwar, Sudha Rani Garapati, Yugandhar Department of Computer science and Engineering GITAM Deemed to be University Rudraram Hyderabad India Department of Computer science and Informatics Mahatma Gandhi University Telangana Nalgonda India

In recent days, the expansion of Internet of Things (IoT) and the quick advancement of computer system applications contribute to the current phenomenon of data growth. The field of intrusion detection has expanded considerably as a result of the need to protect networks from various attacks. Previously, security has been provided by combining conventional intrusion monitoring systems with a high number of security measures. However, the ability of combined systems to analyze enormous amounts of data has been restricted. Therefore, in this research, open-source and freely available datasets known as NSL-KDD and CSE-CIC-IDS2018 datasets are analyzed. Those datasets contain a class column that splits the attack detection into two classes (normal and anomalous) and four categories (DoS, probe, R2L, and U2R). Then, feature encoding and scaling are used for pre-processing. After that, a SelectPercentile-based feature selection algorithm is suggested to identify the numerous various intrusion attacks and choose the best features. Later, the Regularized Long Short-Term Memory (LSTM) is employed to classify the data. The result analysis verified that Regularized LSTM surpasses the existing models in the NSL-KDD based on accuracy (99.95%), false alarm rate (0.05%), attack detection rate (99.82%), and f1-measure (99.94%). While in the CSE-CIC-IDS2018 dataset, the Regularized LSTM method achieves better accuracy (99.33%), false alarm rate (0.07%), attack detection rate (99.96), and f1-measure (99.95%). © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Network intrusion

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Analyze the impact of feature selection techniques in the early prediction of CKD

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International Journal of Cognitive Computing in engineering 2024年第1期5卷 66-77页

作者： Hema, K. Meena, K. Pandian, Ramaraj Department of Computer Science and Engineering GITAM School of Technology GITAM University Bangalore India Netapp San JoseCA United States

Background: Chronic renal disease, often known as Chronic Kidney Disease (CKD), is an illness that causes a steady decline in kidney function. As per the World Health Organization survey, the incidence of CKD may increase from 10% to 13% by 2030. Because of the lack of symptoms in the initial phase, diagnosing CKD early on may be difficult. The key objective of this study is to develop a forecasting model for the early detection of chronic renal disease. Methods: In medical science, Machine Learning (ML) Techniques play a significant role in disease prediction despite numerous studies conducted to categorize CKD in patients using machine learning tools. Most researchers need to analyze the impact of feature selection techniques, yielding high-quality and reliable results. The efficiency of any Techniques/Algorithms depends on feature selection, feature extraction, and classifiers. In this work, the impact of feature selection is experimented with using the Exhaustive Feature Selection (EFS) method. For the early prediction of CKD, a comparative examination of machine learning classifiers, including Gradient Boost (GB), XGBoost, Decision Tree (DT), Random Forest (RF), and KNN (k-nearest neighbors), are utilized. Results: Two types of datasets, standard (New Model) & real-time data sets collected from the dialysis unit of a reputed hospital in Chennai, are used to carry out extensive experiment analysis. Various metrics, including Accuracy, Precision, Recall, and F1-score, are used to tabulate the results of experiments conducted to measure the performance of the proposed approach for various combinations of test and training data. Conclusion: CKD is an irreversible and silent disease;it might have a high impact on many people and begin to manifest themselves at an early age in life. This research paper analyses the effect of feature selection techniques on early CKD prediction. © 2023 The Authors

关键词： Forecasting

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A Learning-Based Ensemble Algorithm with Optimal Selection for Outlier Detection

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第14期49卷 171-192页

作者： Ginni, Girish Reddy Lade, Srinivasa Chakravarthi Computer Science Engineering GITAM University Gandhi Nagar Rushikonda Andhra Pradesh Visakhapatnam530 045 India Department of Computer Science and Engineering College GITAM University Gandhi Nagar Rushikonda Andhra Pradesh Visakhapatnam530 045 India

In this paper, we propose a Learning-based Ensemble Method with Optimal selection strategy (LbEM-OSS), which presents a new outlier detection algorithm that captures only outstanding ones of constituent models. Using KNN to define local regions and Pearson correlation to evaluate the detectors makes the ensemble robust. Our method can adapt and generalize better across different high-dimensional datasets by generating pseudo-ground truths with average and maximum aggregation strategies. On a wide range of benchmark datasets, LbEM-OSS outperformed both statistics-based and neural ensemble methods, which achieved state-of-the-art ROC-AUC as high as 97.78% in the best-case and 4-8% AUC improvements over existing methods on average. These results portray its potential for noise, different dimensionality, and heterogeneous data nature. Moreover, it is highly scalable and accurate, which makes it an essential application in practical fields like fraud detection, network security, and healthcare. This research highlights the need for dynamic selection approaches within ensemble methods, providing the groundwork for future developments in sound outlier detection. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Optimal detection

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Resource prioritization and allocation in fog computing using hybrid optimization

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Web Intelligence 2025年第Pre-press期Pre-press卷 1-22页

作者： Karpe, Sharmila Patil SH, Brahmananda Department of Computer Science and Engineering GITAM University GITAM School of Technology Bangalore Campus Karnataka India Department of Information Technology Walchand Institute of Technology Maharashtra Solapur India

With the rapid proliferation of IoT devices, the volume of data generated has reached unprecedented levels, necessitating efficient management strategies. Fog computing, complemented by 5G technologies, offers promising solutions to reduce service latency and enhance Quality of Service (QoS). However, allocating resources effectively remains challenging due to factors such as uncertainty, mobility, heterogeneity, and limited resources in fog computing environments. Traditional resource allocation (RA) algorithms often fall short of addressing these complexities. This study proposes a novel approach to RA in fog computing, utilizing a non-linear function to optimize resource allocation. An objective function is introduced, incorporating multi constraints such as resource utilization, service response rate, makespan, migration cost, and communication cost. The methodology emphasizes efficient resource allocation in crucial scenarios, facilitating rapid resource distribution where needed. The novel Coati Integrated Beluga Whale Optimization (CI-BWO) strategy is proposed to achieve optimal resource allocation in fog computing environments. By leveraging CI-BWO, this research aims to overcome the limitations of traditional RA methods and enhance the performance and scalability of fog computing applications. Finally, the superiority of the suggested strategy is assessed by comparison with many existing methods. When the task count is 200, the developed CI-BWO attained less migration cost of around 1.287, while existing models have acquired higher migration costs. © 2024 – IOS Press. All rights reserved.

关键词： Resource allocation

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Drug and Vaccine Extractive Text Summarization Insights Using Fine-Tuned Transformers

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Journal of Artificial Intelligence and Technology 2024年第4期4卷 351-362页

作者： Rajesh Bandaru Y.Radhika Department of Computer Science Engineering GITAM(Deemed to be University)VisakhapatnamIndia

Text representation is a key aspect in determining the success of various text summarizing *** using pretrained transformer models has produced encouraging *** the scope of applying these models in medical and drug discovery is not examined to a proper *** address this issue,this article aims to perform extractive summarization based on fine-tuned transformers pertaining to drug and medical *** research also aims to enhance sentence *** the extractive text summarization aspects of medical and drug discovery is a challenging task as the datasets are ***,this research concentrates on the collection of abstracts collected from PubMed for various domains of medical and drug discovery such as drug and COVID,with a total capacity of 1,370 abstracts.A detailed experimentation using BART(Bidirectional Autoregressive Transformer),T5(Text-to-Text Transfer Transformer),LexRank,and TexRank for the analysis of the dataset is carried out in this research to perform extractive text summarization.

关键词： BART BERT extractive text summarization LexRank TexRank

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An efficient missing value imputation and evaluation using GK-KH means and HTR-RNN

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International Journal of Business Intelligence and Data Mining 2023年第1期24卷 25-46页

作者： Syavasya, C.V.S.R. Muddana, A. Lakshmi Department of Computer Science and Engineering Gitam Deemed University Rudraram Hyderabad502329 India

The accuracy of the data mining (DM) outcomes might be affected by mining and analysing incomplete datasets with missing values (MV). Thus, a complete dataset is created by the imputation of MV, which makes the analysis easier. An effectual missing values imputation (MVI) is proposed and evaluated utilising Gaussian kernel-K harmonic means (GK-KH means) and hyperbolic tangent radial-recurrent neural networks (HTR-RNN) to combat this issue. At first, preprocessing is performed on the input data as of the CKD dataset wherein the duplicate form of the data gets eradicated. Next, the missing data are handled by ignoring them;and utilising GK-KH means, the MV is imputed. Next, the data are rationalised into a structured format. Then, SDRM-DHO selects the most optimal features as of the extracted features. Lastly, the HTR-RNN classifier accepts these chosen features as input. Proposed work performed well in more accurate missing value imputation. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Recurrent neural networks

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Enhanced Ransomware Detection using a Hybrid Quantum-Classical Convolutional Neural Network with Boosted Sooty Tern Optimization 4

Enhanced Ransomware Detection using a Hybrid Quantum-Classic...

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

作者： Chaithanya, B.N. Patil, Renuka R. Gitam School of Technology Gitam University Department of Computer Science and Engineering Nagadenahalli Bengaluru India

ISBN: (纸本)9798331523923

Ransomware attacks threaten organizations by encrypting files or locking systems and keeping them inaccessible unless a ransom is paid. Early detection of ransomware attacks helps organizations avoid financial losses, theft of data, and harm to their reputations. This early detection will also reduce the risks related to malicious activities. Detection by signature-based or heuristic approaches tends to be traditional and fails when evolvement in ransomware attacks is considered. Thus, a need for innovative techniques of deep learning and optimization algorithms can be observed. The method proposed in this study focuses on developing a novel multi-stage framework for detecting ransomware attacks. Firstly, correlation coefficient-based min-max normalization (CCMMN) for data preprocessing is used since it makes the data elements more uniform and makes the detection system more efficient. Feature selection is then carried out and optimized using the hybrid Osprey optimization algorithm with an enhancing sparrow search algorithm (OOA-ESSA) to arrive at the most relevant feature for classification. Last, this paper applies a hybrid quantum-classical convolutional neural network with boosted sooty tern optimization (HQCCNN-BSTO) in ransomware detection. The classic model is replaced by a quantum and hybrid classical neural network that may be complemented by an optimization algorithm to increase the speed and efficiency of the detection. The method that has been proposed here offers a flexible and efficient approach to the problems associated with the detection of ransomware threats in the practical environment. Thus, high effectiveness is proved by the 99% accuracy rate of the suggested method. This exceptional outcome proves the robustness of the approach and its ability to adapt to various patterns of dependence and data changes. © 2025 IEEE.

关键词： Convolutional neural networks

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Comparative Analysis of Neurodegenerative disease detection using Artificial Intelligence

Comparative Analysis of Neurodegenerative disease detection ...

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2024 International Conference on Knowledge engineering.and Communication Systems, ICKECS 2024

作者： Vidya, J. Yendapalli, Vamsidhar Gitam University Gitam School of Technology Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350359688

Neurodegeneration is one of the features of several debilitating diseases that are rising rapidly. As they are not curable and progressive, early detection may help the patients and the caretakers to maintain a good quality of life. Advancements in computational methods have made it possible to diagnose and classify NDD (neurodegenerative diseases) using the automated procedure, which will be more effective than manual detection. This paper aims to comprehensively describe the neurodegenerative disease classification using ML and DL techniques that may help the community working on early detection of the disease. © 2024 IEEE.

关键词： Contrastive Learning

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Enhancing Spatial Steganography for Optimal Data Concealment with Image Quality Preservation

Enhancing Spatial Steganography for Optimal Data Concealment...

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2024 International Conference on Recent Innovation in Smart and Sustainable Technology, ICRISST 2024

作者： Padma, R. Yendapalli, Vamsidhar Gitam University Gitam School of Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350309140

In the domain of steganography, the act of concealing data within an image poses a significant challengebalancing between the precision of the carrier image and the capacity for embedding information. This article introduces an innovative spatial steganography approach employing evolutionary algorithms. Proposed method introduces novel approaches to strengthen the matching of the LSB between the carrier and the concealed image. This process leads to an up-scaled concealing efficiency while minimizing deformation. The technique involved optimized pixel traversal in both row and column wise directions, pixel relocation, pixel swapping confidential bits, and inverting confidential data. A generic steganography model rooted on evolutionary algorithms is formulated to explore optimal solutions. Experimental investigations of the RECS [11] and SDID [16] method confirm that the performance of the proposed method is comparatively better than the traditional methods in terms of higher Embedding rate by 29%, PSNR by 19%, and SSIM by 15% respectively. © 2024 IEEE.

关键词： Embeddings

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Design of a Physics-Informed Learning Model for the Electromagnetic Modeling of Functional Materials

Design of a Physics-Informed Learning Model for the Electrom...

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2024 IEEE Microwaves, Antennas, and Propagation Conference, MAPCON 2024

作者： Narang, Naina Lingam, Greeshma Department of Computer Science and Engineering GITAM Deemed to be University Vishakhapatnam Vishakhapatnam India Department of Computer Science and Engineering GITAM Deemed to be University Hyderabad Hyderabad India

ISBN: (纸本)9798350379693

There is a crucial need for an intelligent system to assist material scientists in fabricating and testing functional materials, such as microwave-absorbing materials. Most researchers are searching for novel materials for reduced-size microwave absorbing materials needed in various civil and military applications. The existing models used by material scientists suffer from various challenges, such as trial and error, the requirement of large dataset, and sophisticated instrumentation. This paper proposes a physics-informed learning-based model to assist the researchers in selecting, modeling, and characterizing any material depending on the user-defined application. An example is demonstrated for dielectric material to achieve the minima of the reflection coefficient. © 2024 IEEE.

关键词： Military applications

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