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Comparative Analysis of Classifiers for Cyber Attack Prediction on LUflow Dataset with Explainability 9

Comparative Analysis of Classifiers for Cyber Attack Predict...

引用

9th IEEE international conference for convergence in technology, I2CT 2024

作者： Gunti, Geethika Sree, Kuruva Divya Gupta, Deepa Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science & Engineering Bangalore India

ISBN: (纸本)9798350394474

As the cyber threats areevolving, intrusions exploit vulnerabilities in software or systems and can occur 24/7, targeting individuals and *** Intrusion detection system (IDS) is designed to help in the detection of cyberattacks as a part of cybersecurity. Researchers are developing machine learning(ML) based IDS to detect the attacks in a network, and this has proven to be very effective. In this work, we propose a framework using various machine learning classifiers to classify different types of attack classes include benign, malicious and outlier on different sets of LUFlow intrusion detection data. Random Forest and Decision tree have outperformed with an accuracy of 91% on combined year data whileconsidering the individual years data, logistic regression performed less well than all other algorithms including ensemble *** study includes the examination of precision, recall, and F1 score metrics for each of the employed classification and ensemble methods. Additionally, the most accurate model has been subjected to model interpretability procedures, which have shown the notable characteristics that have a major impact on the categorization of classes. © 2024 IEEE.

关键词： Decision trees

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Music Recommendation System based on Big Data using Pyspark 9

Music Recommendation System based on Big Data using Pyspark

引用

9th IEEE international conference for convergence in technology, I2CT 2024

作者： Jain, Nikunj Sah, Nabin Kumar Hawari, Jawed Khare, Sangita Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science And Engineering Bengaluru India

ISBN: (纸本)9798350394474

In the evolving landscape of digital music consumption, personalized song recommendation systems play a pivotal role in enhancing user engagement and satisfaction. This article presents an in-depth exploration and analysis of an innovative song recommendation framework designed to address the challenges of delivering tailored music experiences. Utilizing advanced machine learning techniques, especially K-Means Clustering Algorithm and data analytics, the proposed system aims to offer song suggestions according to user preferences. The proposed system introduces a hybrid song recommendation system that combines the two distinct approaches i.e. Content and Collaborative based filtering. Various algorithms were implemented for Content based filtering whereas Hadoop and Pyspark was used for implementation of Collaborative filtering. Wide research and Evaluations based on various metrics were considered. The research also draws the strengths and drawbacks or limitations of Content based Recommendation over Collaborative based Recommendation System. The results highlight the system's capability to overcome limitations eventually enhancing user experience by offering a better experience. © 2024 IEEE.

关键词： Recommender systems

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Wireless Channel Estimation using Federated Learning 9

Wireless Channel Estimation using Federated Learning

引用

9th IEEE international conference for convergence in technology, I2CT 2024

作者： Kaur, Jasneet Khan, M. Arif Charles Sturt University School of Computing Mathematics And Engineering NSW2678 Australia

ISBN: (纸本)9798350394474

Contemporary Machine Learning (ML) techniques can now be conveniently applied to the wireless channel estimation problem to reduce the estimation complexity and enhance the system's overall data rate. Traditionally, the wireless channel is estimated at the base station (BS), where BS receives the channel information from the users via the feedback channel. This poses a potential problem with the privacy and security of users' channel information as well as requires large bandwidth for the feedback channel in a system with a large number of users. To address, the dual issues of users' privacy and lessening the required bandwidth for the feedback channel, this paper proposes a Federation Learning (FL) based wireless channel estimation solution for a commonly used wireless communication system. In this technique, each user estimates its own channel and only shares the model parameters with the BS which then combines the information from all users to construct a joint wireless channel to be used for transmission for the next time slot. To evaluate the accuracy of proposed model, we used Stochastic Gradient Descent (SGD) to optimise the solution and compared the results with the results presented in [1]. The numerical results show that the proposed model renders more stable and accurate estimation results in conjunction with the use of SGD and FL. © 2024 IEEE.

关键词： Machine learning

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9th IEEE international conference for convergence in technology, I2CT 2024

作者： Akhilesh, P. Amal Krishna, K. Karthick Bharadwaj, S. Venugopalan, Manju Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science And Engineering Bengaluru India

ISBN: (纸本)9798350394474

technology is expanding and applicants are making their resumes look better by developing their skill set for the current environment. Each resume now looks very promising and manually screening resumes and finding out the potential candidate in time consuming and is not an efficient method. In this paper, we propose a method using Natural Language Processing (NLP) and Spark to filter relevant resumes. The work proposes a cosine similarity-based approach to measure text-similarity and hence find worthy candidates based on key information provided by the employer. The implementation of the proposed approach on a Spark platform helps in handling large-scale datasets, enabling efficient semantic similarity analysis in the context of job descriptions. The experiments conclude that the proposed method can efficiently speed up the process in short - listing worthy candidates and can effectively replace the process of hiring managers going through each resume. Our model demonstrates superior performance compared to a non-Spark-based resume filtering system, achieving an average reduction in processing time by 80%, translating to an average runtime decrease from 5 seconds to 1. This efficiency gain is scalable, presenting enhanced performance in candidate shortlisting, especially with larger datasets. © 2024 IEEE.

关键词： Large datasets

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Covid-19 Classification using Fine-tuned EfficientNet Architecture 9

Covid-19 Classification using Fine-tuned EfficientNet Archit...

引用

9th IEEE international conference for convergence in technology, I2CT 2024

作者： Sruthi, Srigiri Emadaboina, Siddharth MacHavarapu, Pradyumna Singh, Rimjhim Padam Kanchan, Sneha Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science And Engineering Bengaluru India Universiti Tunku Abdul Rahman Department of Internet Engineering And Computer Science Malaysia

ISBN: (纸本)9798350394474

This work utilizes an Efficient Net-based deep learning architecture, leveraging its efficiency and scalability for image classification tasks. We employ a Dataset of chest X-ray images, including COVID-19 cases as well as other relevant Pathology, to train and evaluate the model. The training process involves data augmentation, model optimization, and early stopping to ensure robust and generalizable performance. It specifies the image size, channels, and class count, and then proceeds to create a pre-trained model using the EfficientNetB0 architecture. The model is built upon the pre-trained EfficientNetB0 model, with additional layers including Layer Normalization, Dense, and Dropout layers for fine-tuning on the Covid-19 data. Subsequently, the model is rigorously evaluated using standard performance metrics, including precision, recall, F1-score, and accuracy, and has proven to outperform other state-of-art models by a 3% increase in F1-score value. © 2024 IEEE.

关键词： Deep learning

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Data-Driven Analysis of Patch Inference Efficiency on Memory-Constrained Devices 15

Data-Driven Analysis of Patch Inference Efficiency on Memory...

引用

15th international conference on Information and Communication technology convergence, ICTC 2024

作者： Park, Gunju Hong, Seungtae Kim, Jeong-Si Electronics and Telecommunications Research Institute Artificial Intelligence Computing Research Lab. Deajeon Korea Republic of University of Science and Technology Daejeon Korea Republic of

ISBN: (纸本)9798350364637

Recent advancements in artificial intelligence have significantly propelled the development of deep learning inference on compact devices. As such, research into optimizing deep learning models for devices with constrained runtime memory resources has become increasingly active. Various optimization efforts have been pursued, including software algorithms like Quantization and Pruning. Moreover, optimizations that consider hardware resource constraints, such as Neural Architecture Search (NAS), have also been explored. Among these, the technique of spatially dividing the Activation Tensor and performing inference in Patch units has been studied. In Patch inference, identifying the appropriate points for Patch division and the optimal size of the Patches is crucial. This paper presents an experimental analysis aimed at exploring the optimal points for Patch inference considering memory and execution time constraints. Additionally, it proposes strategies for selecting optimal Patch parameters to enhance inference performance on resource-limited devices. © 2024 IEEE.

关键词： Deep learning

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Multi-UAV Employed Secure Product Management and Surveillance System in Industry 4.0 using Blockchain

Multi-UAV Employed Secure Product Management and Surveillanc...

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2024 IEEE international conference on Industry 4.0, Artificial Intelligence, and Communications technology, IAICT 2024

作者： Masuduzzaman, Md Raza, Syed Muhammad Shin, Soo Young Convergence Innovative Talents Training Group Kumoh National Institute of Technology Gumi39177 Korea Republic of Department of IT Convergence Engineering Kumoh National Institute of Technology Gumi39177 Korea Republic of

ISBN: (纸本)9798350353464

One of the significant challenges in Industry 4.0 is delivering secure data management during pre- and post-production. Furthermore, surveillance in restricted areas is also crucial since individuals can forfeit or seize the products in various circumstances. Therefore, using unmanned aerial vehicles (UAVs) can create new opportunities to improve surveillance and product management in Industry 4.0. This paper presents a multi-UAV-employed secure data management and surveillance system to overcome the above-mentioned challenges in Industry 4.0. Firstly, an inventory data management UAV-based automated product counting technique is developed using the radio frequency identification technique to avoid human involvement in production storage. Moreover, a surveillance UAV-based person detection technique using a deep learning (DL) model is proposed to detect humans in restricted areas. Additionally, blockchain-based secure product tracking techniques using a multi-access edge computing (MEC) server are utilized to provide integrity in the overall Industry 4.0 environment. The experimental result shows that the proposed UAV-based secure product management is efficacious for using the MEC server in the network in terms of latency analysis. Moreover, the surveillance UAV can successfully detect the person using the You Only Look Once (YOLOv8-small) DL model with higher accuracy. ©2024 IEEE.

关键词： Radio frequency identification (RFID)

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Exploring Supervised Machine Learning Classifiers for Epileptic Seizure Detection over Two Distinct Preprocessed Datasets 9

Exploring Supervised Machine Learning Classifiers for Epilep...

引用

9th IEEE international conference for convergence in technology, I2CT 2024

作者： Sharma, Ashish Singh, Vinai K Motherhood University Haridwar Uttarakhand Roorkee India Lebanese French University College of Engineering And Computer Science Department of Information Technology Kurdistan Region Iraq

ISBN: (纸本)9798350394474

Purpose: Medical practitioners face difficulty reviewing continuous ElectroEncephaloGram (EEG) recordings in terms of automatically detecting seizures for diagnosing ***: Machine learning (ML) techniques are extensively used for many application areas. We apply supervised ML methods are support vector machine (SVM), decision tree (DT), random forest (RF), k-nearest neighbor (k-NN), and extreme gradient boost (XGBoost) to compare two Dataset-1: 'PREPROCESSED CHB-MIT SCALP EEG DATABASE', and Dataset-2: 'PREPROCESSED EEG DATASET WITH EPILEPTIC SEIZURE FROM SNMC BAGALKOT, INDIA' distinct datasets' performance for classifying non-seizure andseizure ***:It is shown that the approximate classification accuracy reaches for Dataset-1 and Dataset-2 up to 74% and 99%, respectively. The highest values under performance analysis have been shown through each dataset's computing are precision of 77.72% and 99.25%, recall of 69.43% and 99.44%, f1-score of 71.51% and 99.31%;and Matthews Correlation Coefficients (MCC) of 48.58% and 98.61% metrics ***: Hence, the XGBoost is an efficient classifier for both datasets used in this research. ML classifier is an effective tool for neuro practitioners to detect epileptic seizures in a biomedical EEG signal. © 2024 IEEE.

关键词： Support vector machines

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Deep Reinforcement Learning based Dynamic Channel Bonding for Wi-Fi Networks 21

Deep Reinforcement Learning based Dynamic Channel Bonding fo...

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21st IEEE international Symposium on Parallel and Distributed Processing with Applications, 13th IEEE international conference on Big Data and Cloud computing, 16th IEEE international conference on Social computing and Networking and 13th international conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Chen, Hao Liu, Peng You, Lizhao Guo, Ziyang Luo, Jiajun Sun, Xinghua Wang, Taotao Fu, Liqun Xiamen University Department of Information and Communication Engineering China Shenzhen Research Institute of Xiamen University Shenzhen China Huawei Technologies Co. Ltd. Wireless Technology Lab 2012 Laboratories China Sun Yat-sen University School of Electronics and Communication Engineering China Shenzhen University College of Electronics and Information Engineering Shenzhen China

ISBN: (纸本)9798350329223

This paper presents a decentralized Deep Reinforcement Learning (DRL)-based dynamic channel bonding (DCB) algorithm (i.e., drlDCB) for Wi-Fi networks. Most existing RL-based channel bonding algorithms either are centralized (i.e., requiring a central controller and global information for training), or can easily fall into local optimum (e.g., over-using channels for demand) as the number of stations increases. Moreover, they may converge slowly due to the use of measured throughput or delay over a duration as reward. In contrast, drlDCB is a purely distributed algorithm where each access point gets trained using local information, and addresses the local optimum and the slow convergence problems through the following new designs: 1) a new piecewise throughput model to avoid occupying too many channels if demand is satisfied, and 2) a new reward design using estimated throughput. The use of estimated throughput allows per-frame adaptation and fast convergence speed, while the accurate estimation generalizes the application scenarios, allowing an offline-trained model to be used in a dynamic environment. Simulation results in the static scenario with fixed loads and the dynamic scenario with random loads show that drlDCB outperforms state-of-the-art DCB algorithms in terms of network throughput, delay and convergence speed. © 2023 IEEE.

关键词： Reinforcement learning

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Autograding of Programming Skills 8

Autograding of Programming Skills

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8th IEEE international conference for convergence in technology, I2CT 2023

作者： Narmada, Nakka Pati, Peeta Basa Amrita School of Computing Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350334012

To evaluate a learner's knowledge of programming language skills, assessments are given. Grading of these is usually done manually which not only is tedious but prone to error due to repetition and fatigue. In this work, we employ pre-trained language models to perform automated grading of "C"programming language. Embeddings from different transformers on pre-assessed codes are used as feature vectors to train a wide range of regressors for the scoring task. Root-mean-square error (RMSE) is the metric utilized to compare the scores of these regressors. It's observed that embeddings from T5-model with CatBoost regressor gives the least error around 15%. © 2023 IEEE.

关键词： Embeddings

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