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Intrusion Detection System using Ensemble Learning Analytics 8

Intrusion Detection System using Ensemble Learning Analytics

引用

8th IEEE International Conference for Convergence in technology, I2CT 2023

作者： Dasari, Anil Kumar Biswas, Saroj Kr. Sanyal, Saptarsi Purkayastha, Biswajit National Institute of Technology Silchar Dept. of Computer Science & Engineering Silchar India

ISBN: (纸本)9798350334012

An Intrusion Detection System (IDS) monitors and analyses data to find any intrusions into a system or network. The network generates data at a tremendous volume, variety, and speed, making it difficult to detect attacks using conventional techniques like a virus detection system, misuse detection software i.e. the database of attack signatures that it uses to compare packets. Despite the researchers' significant efforts, IDS still struggles to identify new intrusions, to improve detection accuracy, and to reduce false alarm rates. To overcome the problems mentioned above this paper proposes an unique model named Intrusion Detection System using Machine Learning Analytics (IDSMLA), which uses SMOTE oversampling technique to deal with class imbalance problem, it also uses Minimum Redundancy Maximum Relevance (mRMR) to perform feature selection as feature selection reduces time complexity by eliminating irrelevant features and hence increasing the accuracy of the model and finally to perform classification task, the proposed model IDSMLA uses Extra Trees(ET) bagging ensemble technique. The performance of the proposed model IDSMLA is measured using accuracy and F1-score using 10-folds cross validation. Experimental results have demonstrated that the proposed model IDSMLA greatly outperforms different single-classifier based models, different ensemble models as well as different models present in literature. © 2023 IEEE.

关键词： Intrusion detection

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DeFy: P2P Crypto Lending 14

DeFy: P2P Crypto Lending

引用

14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Thalakottur, Joel Ray, Ben Abraham Victor, Alex Vyas, Aryan St. Francis Institute of Technology Dept. of Computer Engineering Mumbai India

ISBN: (纸本)9798350335095

The debt market is a substantial industry, estimated to be of significant value and projected to grow further in the near future. However, its size is marred by inefficiencies due to a lack of transparency and trust. To address these issues, DeFy, a blockchain-based system, offers loans to borrowers using their cryptocurrency assets as collateral. Leveraging smart contract technology and a decentralized network, DeFy creates a secure and transparent lending environment that operates globally without intermediaries. Empowering users with autonomy to select preferred lenders through a robust bidding system, DeFy enhances transparency in the borrowing process and empowers borrowers to make informed decisions. This user-driven lending approach provides an efficient and secure means for borrowers to access capital without liquidating their cryptocurrency holdings, while allowing lenders to earn interest on their capital through the lending process. © 2023 IEEE.

关键词： Blockchain

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Deep Learning Approach for Hate and Non Hate Speech Detection in Online Social Media 3

Deep Learning Approach for Hate and Non Hate Speech Detectio...

引用

3rd International Conference on Technological Advancements in Computational Sciences, ICTACS 2023

作者： Sharma, Parshuram Tiwari, Rakesh Kumar Technocrats Institute of Technology & Science Dept. of Computer Science Engineering Bhopal India

ISBN: (纸本)9798350342338

People's ability to freely and anonymously share their thoughts and feelings online on social media platforms is contributing to a rising issue of hate speech. Hate speech has the potential to hurt both people and groups, contribute to the polarisation of society, and even provoke acts of physical violence. Therefore, identifying and removing hate speech from online social media is an important task for the purpose of maintaining an online environment that is healthy and respectful. A method based on deep learning is proposed in this study for differentiating between hate speech and other types of communication that may be found in online social media. The solution that has been developed makes use of a natural language processing (NLP) and long term short memory (LSTM) model that is trained on a huge dataset consisting of tweets that have been annotated. Tweets that include hate speech and tweets that do not contain hate speech were both included in the dataset and were labelled by human annotators. Python software with IDE of Spyder version 3.7 is used to carry out the simulation work. The accuracy level reached overall is 91.14%. © 2023 IEEE.

关键词： Hate LSTM Machine Learning NLP Social Media Speech Twitter

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Zero Cost Online Food Delivery System with Machine Learning Prediction 7

Zero Cost Online Food Delivery System with Machine Learning ...

引用

7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Reddy, Yaparla Adithya Kumar Swaroop, Chebrolu Satya Terence, Sebastian Reddy, Kummetha Sai Manoj Santhosh, Korrapati Karunya Institute of Technology and Sciences Dept. of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350397253

The growth of Food Delivery Application (FDA), helps customers to get variety of food items in their door steps. This helps restaurant to serve their customers with different food items, and to increase their business. FDA also provides jobs to millions of food delivery persons. But the major drawback of FDA is high commission charges for food orders. This article presents zero cost online food delivery system with ML prediction models. Zero cost online food delivery system provides user friendly interface to restaurant, home makers, customers and delivery persons. In this system, the restaurant or home makers can showcase their food items. The customers would be able to place the order. Delivery person can show interest towards delivery of the order. The customers have an option to select the delivery person for delivery and pay the delivery charges. All the payments are done through UPI payment. Advantage of the system is, it will not collect commission from home makers (or) restaurant owners and customers for the orders and also Machine Learning models is used to suggest food items based on different factors. Random Forest, Decision Tree Regression, K-Nearest Neighbor, and Extra Tree Classifier models are used to suggest food items to the customer. © 2023 IEEE.

关键词： Decision trees

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Adaptive Contour Noise Generation Network for Semi-supervised Medical Data Segmentation 6

Adaptive Contour Noise Generation Network for Semi-supervise...

引用

6th International Conference on Artificial Intelligence and Big Data, ICAIBD 2023

作者： Ye, Shunlong Nanjing University of Science and Technology Dept. of Computer Science and Engineering Nanjing China

ISBN: (纸本)9781665491259

When training convolutional neural networks, noise distribution in data can affect segmentation accuracy. Therefore, many neural networks will strengthen the segmentation ability of the model by artificially adding noise. However, because the noise distribution of the input data is not known, the model training process can only generate random noise, e.g., gaussian noise, but cannot simulate real-world noise. In the medical imaging domain, more attention is paid to the image contours, which can also be used for noising simulation. Meanwhile, considering the existence of rich unlabeled data sets in medical images, this paper proposes a semi-supervised framework for uncertainty estimation by using Monte Carlo Dropout and generates adaptive noise using the uncertainty map. Specifically, the framework consists of three parts, including a noise generation model, a teacher model, and a student model. The noise generation part processes the data input to the other two parts and uses the contour feature identified by the posteriori uncertainty map to simulate priori noise to guide the process of training segmentation model. Meanwhile, the student model updates its parameters based on the teacher model output and the ground truth annotations by minimizing a joint loss. Segmentation experiments on left atrium 3D data show that our method has competitive results with other SOTA semi-supervised methods. © 2023 IEEE.

关键词： Medical imaging

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A CNN Deep Learning Technique for Botnet Attack Detection for IoT Application 3

A CNN Deep Learning Technique for Botnet Attack Detection fo...

引用

3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Batham, Yashi Kumar Tiwari, Rakesh Technocrats Institute of Technology and Science Dept. of Computer Science and Engineering Bhopal India

ISBN: (纸本)9798350322842

The Internet of Things (IoT) is a significant technology that enables us to develop many helpful online applications. The Internet of Things allows remote operating items using preexisting network infrastructure. Services that are based on information technology, computers, and other technologies are included in the cyber world. The cyber-world sees many protocols and technological advancements that lead to improvements. When it comes to cyber-based services, security is an extremely significant issue. Classification methods based on machine learning (ML) and deep learning (DL) can accurately forecast the most effective model for a dataset. A cyber assault on a wide scale carried out by devices infected with malware and managed remotely is known as a botnet attack. Under the context of IoT applications, an effective method for detecting botnet attacks is presented in this research. © 2023 IEEE.

关键词： Internet of things

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引用

11th International Symposium on Computing and Networking Workshops, CANDARW 2023

作者： Singh, Yashdeep Karmakar, Sushanta Indian Institute of Technology Guwahati Dept. of Computer Science & Engineering Guwahati India

ISBN: (纸本)9798350306941

In this work we investigate the problem of popular matching in a 3-uniform 3-partite hypergraph, where the first set contains a set of agents and the second and the third set contain different class of items respectively. Agents have a preference list for hyperedges, whereas the members of the other sets do not have preferences. Each agent votes in favor of a preferred matching. A matching M is called popular if there does not exist a matching M′ such that the number of agents that prefer M′ is more than the number of agents that prefer M. This paper provides a characterization of popular matching in a 3-uniform 3-partite hypergraph and shows that the problem of deciding whether a given 3-uniform 3-partite hypergraph has a popular matching is NP-hard. We also prove the NP-hardness of popular matching in a k-uniform k-partite hypergraph (k > 3). Assuming that a given 3-uniform 3-partite hypergraph admits at least one popular matching, we compare the cardinality of the maximum size popular matching (denoted as M∗) with a maximum matching (denoted as Mmax) in the 3-uniform 3-partite hypergraph and show that | Mast geq 23| Mmax|. © 2023 IEEE.

关键词： b- matching matching in hypergraph popular matching

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Sybil Attack Models of MM-Wave Communication for LEO Satellite Network in Cybersecurity

Sybil Attack Models of MM-Wave Communication for LEO Satelli...

引用

2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Mohamed, Abdiasis Ahmed Chang, Shih Hao National Taipei University of Technology Dept. Computer Science and Information Engineering Taiwan

ISBN: (纸本)9798350324174

With the rapid progress of six-generation (6G) wireless communication technologies, Low Earth Orbit (LEO) satellite global network technology played a very important role and promoted millimeter-wave (mm-wave) systems gained demand. However, security remains a critical concern in wireless network design and usage. In this paper, we propose a method based on an artificial intelligence technique, machine-learning algorithms in cybersecurity. This approach handles significant security dataset training, using different techniques such as decision trees and Recurrent Neural networks, which allow feature selection and Sybil attack classification. In addition, we refer to the guidelines and specifications of orbit satellite standards in ISO 16164-2015 and ISO 27852-16 to validate the satellite transmitter. Simulation results arise an improvement in security and the ability to detect and prevent Sybil attacks. Overall, this approach makes a significant contribution to the field of satellite cybersecurity by providing a standard-compliant solution © 2023 IEEE.

关键词： Recurrent neural networks

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Nutrition Detection during Gestation Period using ML Algorithms 14

Nutrition Detection during Gestation Period using ML Algorit...

引用

14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Dixit, Sunanda Sriram, Navayug Ashritha, S.B. Datta, Paidi Venkata Revanth Krishna, Bolem Jagadesh Dept. of Computer Science and Engineering BMS Institute of Technology and Management Bangalore India

The issue of low birth weight in infants is a significant concern in prenatal care, and it can have adverse effects on the newborn's health, sometimes leading to mortality. This problem contributes to high rates of child mortality worldwide. Artificial intelligence, particularly ML, offers potential solutions for predicting whether a fetus will be born small for its gestational age. Early detection of fetal developmental issues is critical, as timely intervention can increase gestation days and improve fetal weight at birth, reducing the risk of neonatal morbidity and mortality. This proposal aims to explore various machine learning methods for predicting small-for-gestational-age infants, emphasizing the importance of early detection and intervention to improve outcomes. © Grenze Scientific Society, 2023.

关键词： Machine learning

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Secured Full-Duplex UAV-Aided Spectrum-Sharing Network Based on NOMA 20

Secured Full-Duplex UAV-Aided Spectrum-Sharing Network Based...

引用

20th IEEE International Conference on Mobile Ad Hoc and Smart Systems, MASS 2023

作者： Hasan, Moh Khalid Yu, Shucheng Song, Min Stevens Institute of Technology Dept. of Electrical and Computer Engineering Hoboken United States

ISBN: (纸本)9798350324334

Non-Orthogonal Multiple Access (NOMA) has emerged as an effective approach for improving spectral efficiency. In addition, Unmanned Aerial Vehicle (UAV)-aided communication offers rapid deployment and flexibility in challenging environments. Studying physical layer security in these scenarios is crucial to protect sensitive data from eavesdropping in spectrum-sharing networks. Nonetheless, there is limited research on comprehensive secrecy analysis in NOMA-based spectrum-sharing networks, particularly in the context of full-duplex UAV-assisted scenarios. In this paper, we investigate the secrecy performance of a UAV-aided and full-duplex NOMA-based spectrum-sharing network. The system primarily involves a primary source communicating with a full-duplex secondary source, which then simultaneously relays combined data from both sources to the primary and secondary receivers. Considering the susceptibility of both transmission stages to an external eavesdropper, the secondary source employs artificial jamming to enhance secrecy for both primary and secondary users. We analytically derive the ergodic secrecy achievable rate (ESAR) for the primary and secondary networks. The closed-form expressions are obtained by characterizing the channels' probabilistic nature while considering the Nakagami-m fading properties of each channel. Through our analysis and Monte Carlo simulations, we demonstrate that our proposed scheme achieves a positive ESAR and outperforms benchmark schemes relying on OMA. © 2023 IEEE.

关键词： Antennas

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