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Machine Learning Based Secure Cardless Financial Transactions Using Multi Factor Authentication

Machine Learning Based Secure Cardless Financial Transaction...

引用

Mobile computing and Sustainable Informatics (ICMCSI), International conference on

作者： Prasanth. v. S Patnam Thousifa Sunkesula vyshnavi Ketham Yasaswitha A. Parveen Akhther Dept.of ECE Madanapalle Institute of Technology & Science Madanapalle India Dept. of CSE Sathyabama Institute of Science & Technology Chennai India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

In this rapidly changing world, ensure secure transactions is more important than ever. This paper discusses how combining artificial intelligence with multi-factor authentication enhances the security for cardless transactions. Machine Learning plays a vital role by automatically detecting and preventing fraud activities, while MFA adds extra layers of security. This paper includes algorithms like Convolutional Neural Networks Algorithm, Advanced Encryption Standards Encryption Algorithm and Cosine Similarity for feature mapping. These layers include using mobile devices and advanced identification methods like facial recognition, making the process secure and user-friendly. Together, these technologies create a robust defense system that not only prevents fraud but also ensures smooth and successful transactions. This combination facilitates the way for future growth of digital finance, providing both safety and convenience.

关键词： Machine learning algorithms Multi-factor authentication Face recognition Machine learning Fraud Encryption Security Object recognition Protection Standards

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Design and Comparison of Collaborative Filtering Technology for Product Suggestions in E-Commerce 1

Design and Comparison of Collaborative Filtering Technology ...

引用

1st IEEE International conference on Automation, computing and Renewable Systems, ICACRS 2022

作者： Rajasekar, P. Mohanraj, B. Padhi, S.N. Sivakumar, N. Lavanya, J. Charles Prabu, v. Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Tamil Nadu Chennai603203 India Department of Information Technology Karpagam College of Engineering Tamil Nadu Coimbatore641032 India Department of Mechanical Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522302 India Department of Computer Science and Engineering Panimalar Engineering College Chennai City Campus Tamil Nadu Chennai600029 India Department of Fashion Designing SRM Institute of Science and Technology CSH Kattankulathur campus Tamil Nadu Chennai603203 India Department of Computer Science Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai600 062 India

ISBN: (纸本)9781665460842

E-commerce websites and internet purchasing have never been more necessary. Product recommendations are a key tactic employed by online buying platforms. This study compares two different filtering algorithms that are used in product recommendationson websites like Amazon and Flipkart. Reviews of electrical devices, user data, and timestamps were all gathered from Kaggle and put into a dataset. These specifics then undergo several preprocessing steps. The steps are data visualization, duplicate value analysis, missing value analysis, and datatype analysis. The exhibited data is then used to train and test the machine learning models. For this purpose, two machine learning models are developed using two different algorithms. The algorithms K-Nearest Neighbor(KNN) and Support vector Decomposition (SvD) are employed. The models are then trained to acquire used in the prediction procedure after that. The best models for the prediction process are then determined by scrutinizing the results of both models. The Root Mean Square Error (RMSE), Mean Square Error (MSE), and Mean Absolute Error (MAE) values are located and compared because the findings of the analysis weren't *** erroneous data are plotted into a graph and evaluated to further demonstrate the point. As the SvD method is discovered to be the best of the group, the algorithm's greatest and worst predictions are discovered and examined. Even the poorest model constructed for the study's predictions turned out to be superior to several other prediction algorithms used for product suggestions. This algorithm can be deployed into the backend of any e-commerce website making it more effective. © 2022 IEEE

关键词： value engineering

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Reservoir Based Edge Training on RF Data To Deliver Intelligent and Efficient IoT Spectrum Sensors 7

Reservoir Based Edge Training on RF Data To Deliver Intellig...

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7th IEEE International conference on Smart computing (SMARTCOMP)

作者： Kokalj-Filipovic, Silvija Toliver, Paul Johnson, William Miller, Rob Peraton Labs Inc Basking Ridge NJ 07920 USA

ISBN: (纸本)9781665412520

Current radio frequency (RF) sensors at the Edge lack the computational resources to support practical, in-situ training for intelligent spectrum monitoring. This is true for sensor data classification in general. We propose a solution via Deep Delay Loop Reservoir computing (DLR), a processing architecture that supports general machine learning algorithms on compact mobile devices by leveraging delay-loop reservoir computing in combination with innovative electro-optical hardware. With both digital and photonic realizations of our design of the loops, DLR delivers reductions in form factor, hardware complexity and latency, compared to the State-of-the-Art (SoA). The main impact of the reservoir is to project the input data into a higher dimensional space of reservoir state vectors in order to linearly separate the input classes. Once the classes are well separated, traditionally complex, power-hungry classification models are no longer needed for the learning process. Yet, even with simple classifiers based on Ridge regression (RR), the complexity grows at least quadratically with the input size. Hence, the hardware reduction required for training on compact devices is in contradiction with the large dimension of state vectors. DLR employs a RR-based classifier to exceed the SoA accuracy, while further reducing power consumption by leveraging the architecture of parallel (split) loops. We present DLR architectures composed of multiple smaller loops whose state vectors are linearly combined to create a lower dimensional input into Ridge regression. We demonstrate the advantages of using DLR for two distinct applications: RF Specific Emitter Identification (SEI) for IoT authentication, and wireless protocol recognition for IoT situational awareness.

关键词： IoT reservoir computing edge computing in-situ training delay loops wireless security specific emitter identification RF fingerprinting RF protocol recognition

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Boosting algorithms to Identify Distributed Denial-of-Service Attacks 3

Boosting Algorithms to Identify Distributed Denial-of-Servic...

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3rd International conference on Emerging Electrical Energy, Electronics and computing Technologies 2021, ICE4CT 2021

作者： Kumar, v. Kumar, A. Garg, S. Payyavula, S.R. Birla Institute of Technology Mesra Ranchi India Samsung Research India Bangalore India

In the current pandemic situation, much work became automated using Internet of Things (IoT) devices. The security of IoT devices is a major issue because they can easily be hacked by third parties. Attackers cause interruptions in vital ongoing operations through these hacked devices. Thus, the demand for an efficient attack identification system has increased in the last few years. The present research aims to identify modern distributed denial-of-service (DDoS) attacks. To provide a solution to the problem of DDoS attacks, an openly available dataset (CICDDoS 2019) has recently been introduced and implemented. The attacks currently occurring in the dataset were identified using two machine learning methods, i.e. the light gradient boosting method (LGBM) and extreme gradient boosting (XGBoost). These methods have been selected because of their superior prediction ability in high volumes of data in less time than other methods require. The accuracy achieved by LGBM and XGBoost were 94.88% and 94.89% in 30 and 229 seconds(s), respectively. © Published under licence by IOP Publishing Ltd.

关键词： Denial-of-service attack

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Towards Fidelity-Optimal Qubit Mapping on NISQ Computers

Towards Fidelity-Optimal Qubit Mapping on NISQ Computers

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Quantum computing and Engineering (QCE), IEEE International conference on

作者： Sri Khandavilli Indu Palanisamy Manh v. Nguyen Thinh v. Le Tu N. Nguyen Thang N. Dinh Department of Computer Science Kennesaw State University Marietta GA USA Department of Computer Science Virginia Commonwealth University VA USA

Quantum computing is gaining momentum in revolutionizing the way we approach complex problem-solving. However, the practical implementation of quantum algorithms remains a significant challenge due to the error-prone and hardware limits of near-term quantum devices. For instance, physical qubit connections are limited, which necessitates the use of quantum SWAP gates to dynamically transform the logical topology during execution. In addition, to optimize fidelity, it is essential to ensure that 1) the allocated hardware has a low error rate and 2) the number of SWAP gates injected into the circuit is minimized. To address these challenges, we propose a suite of algorithms: the Fidelity-aware Graph Extraction Algorithm (FGEA) is used to identify the hardware region with the lowest probability of error, the Frequency-based Mapping Algorithm (FMA) allocates logical-physical qubits that reduce the potential distance of topological transformation, and the Heuristic Routing Algorithm (HRA) searches for an optimal swapping injection strategy. We evaluate the proposed algorithms on the IBM-provided Noisy Intermediate-Scale Quantum (NISQ) computer, using a dataset consisting of 17 different quantum circuits of various sizes. The circuits are executed on the IBM Toronto Falcon processor. The three proposed algorithms outperform the existing SABRE algorithm in reducing the number of SWAP gates required. Therefore, our proposed algorithms hold significant promise in enhancing the fidelity and reducing the number of SWAP gates required in implementing Quantum algorithms.

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A Contemporary Access of Improved BEE Colony Optimization for Scheduling Problems in Cloud Environment 3

A Contemporary Access of Improved BEE Colony Optimization fo...

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3rd International conference on Inventive Research in computing Applications, ICIRCA 2021

作者： Mohan, v.Murali Balajee, R.M. Jyothi, N.M. Kiranbabu, M.N.v. Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering A.P. Vaddeswaram Guntur India

ISBN: (纸本)9780738146270

Today's competitive environment improves interoperability across many devices, illuminating resource distribution for user demands and preferences. In cloud computing, task scheduling triggers the goal of increasing performance by leveraging resource distribution efficiently. Many approaches that lead to task optimization for resource utilization were carried out with an emphasis on static and dynamic models. Today's computing systems are dynamic, and meta-heuristic algorithms can be useful in solving problems. Evolutionary algorithms with swarm intelligence help provide ideal solutions to today's difficult problems. This research work has implemented an improvement of Bee colony optimization for scheduling the tasks in cloud environment. © 2021 IEEE.

关键词： Interoperability

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Design of Long-Term Evolution Based Mobile Edge computing Systems to Improve 5G Systems

Design of Long-Term Evolution Based Mobile Edge Computing Sy...

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International conference on Edge computing and Applications (ICECAA)

作者： v. Arun M. Azhagiri SRM Institute of Science and Technology Chennai Tamilnadu India Department of Computer Science and Engineering SRM Institute of Science and Technology Chennai Tamilnadu India

Mobile edge computing is an emerging technology that enables mobile devices to access cloud services with reduced latency by moving computing, storage, and network control closer to the network edge. This advancement allows mobile devices to operate for longer periods on a single charge. With the advent of 5G communications, there is increasing interest in exploring the integration of mobile edge computing within existing mobile networks. The proposed work aims to develop a portable 5G computing system that offloads computational tasks to edge servers, thereby reducing latency and improving energy efficiency. The work focuses on designing efficient workload allocation algorithms to optimize the distribution of tasks to the supported servers. By implementing and evaluating a practical mobile-based computing system based on Long-Term Evolution (LTE), this research aims to demonstrate the real-world effectiveness of such mobile edge computing programs, with particular emphasis on reducing delays and enhancing energy efficiency.

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Enhancing IoT Security: A Novel Attack Detection Model for IoT Logs with Machine Learning Techniques

Enhancing IoT Security: A Novel Attack Detection Model for I...

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International conference on Smart computing & Communications (ICSCC)

作者： Anu v Jacob Elizabeth Issac Athira Krishna Department of Computer Science Mar Athanasius college of Engineering Kothamangalam Kerala India

ISBN: (数字)9798350363104

ISBN: (纸本)9798350363111

The Internet of Things (IoT), has completely transformed the way that we interact with the world around us, by interconnecting everyday objects and devices through a common network. An enormous quantity of data is generated by this massive network of IoT devices, that delivers informative data to businesses and organizations. IoT log analysis is an essential procedure that includes gathering, processing, keeping track of, and analyzing logs produced by IoT devices. Important details regarding user activity, system performance, operational status, and possible attacks or problems are all contained in these logs. Through close examination of these logs, people and organizations can strengthen overall security, detect threats, and improve device health. This research suggests an ensemble model to identify different types of attacks in IoT log data by combining Random Forest and Auto encoder algorithms. This system surpasses previous systems in addition to enhancing system performance and achieves an accuracy of 90.6% compared to the other models Recall, accuracy, precision, fI1 score, and other known measures are used in the performance evaluation process.

关键词： Adaptation models Accuracy System performance Standards organizations Organizations Data models Real-time systems Internet of Things Security Random forests

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Learning-based Energy Consumption Prediction 17

Learning-based Energy Consumption Prediction

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17th International conference on Future Networks and Communications / 19th International conference on Mobile Systems and Pervasive computing / 12th International conference on Sustainable Energy Information Technology, FNC/MobiSPC/SEIT 2022

作者： Estrada, Rebeca Asanza, víctor Torres, Danny Bazurto, Adrian valeriano, Irving Escuela Superior Politécnica del Litoral Espol CTI FIEC Guayaquil Ecuador

As more people send information to the cloud-fog infrastructure, this brings many problems to the management of computer energy consumption. Therefore, energy consumption management of servers, fog devices and cloud computing platform should be investigated to comply with the Green IT requirement. In this paper, we propose an energy consumption prediction model consisting of several components such as hardware design, data pre-processing, characteristics extraction and selection. Our main goal is to develop a non-invasive meter based on a network of sensors that includes a microcontroller, the MQTT communication protocol and the energy measurement module. This meter measures voltage, current, power, frequency, energy and power factor while a dashboard is used to present the energy measurements in real-time. In particular, we perform measurements using a workstation that has similar characteristics to the servers of a Datacenter locate at the Information Technology Center in ESPOL, which currently provide this type of services in Ecuador. For convenience, we evaluated different linear regression models to select the best one and to predict future energy consumption based on the several measurements from the workstation during several hours which enables the consumer to optimize and to reduce the maintenance costs of the IT equipment. The supervised machine learning algorithms presented in this work allow us to predict the energy consumption by hours and by days. © 2022 The Authors. Published by Elsevier B.v. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the conference Program Chairs.

关键词： Energy utilization

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Federated Dynamic Sparse Training: computing Less, Communicating Less, Yet Learning Better 36

Federated Dynamic Sparse Training: Computing Less, Communica...

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36th AAAI conference on Artificial Intelligence / 34th conference on Innovative Applications of Artificial Intelligence / 12th Symposium on Educational Advances in Artificial Intelligence

作者： Bibikar, Sameer vikalo, Haris Wang, Zhangyang Chen, Xiaohan Univ Texas Austin Dept Elect & Comp Engn Austin TX 78712 USA

ISBN: (纸本)9781577358763

Federated learning (FL) enables distribution of machine learning workloads from the cloud to resource-limited edge devices. Unfortunately, current deep networks remain not only too compute-heavy for inference and training on edge devices, but also too large for communicating updates over bandwidth-constrained networks. In this paper, we develop, implement, and experimentally validate a novel FL framework termed Federated Dynamic Sparse Training (FedDST) by which complex neural networks can be deployed and trained with substantially improved efficiency in both on-device computation and in-network communication. At the core of FedDST is a dynamic process that extracts and trains sparse sub-networks from the target full network. With this scheme, "two birds are killed with one stone:" instead of full models, each client performs efficient training of its own sparse networks, and only sparse networks are transmitted between devices and the cloud. Furthermore, our results reveal that the dynamic sparsity during FL training more flexibly accommodates local heterogeneity in FL agents than the fixed, shared sparse masks. Moreover, dynamic sparsity naturally introduces an "in-time self-ensembling effect" into the training dynamics, and improves the FL performance even over dense training. In a realistic and challenging non i.i.d. FL setting, FedDST consistently outperforms competing algorithms in our experiments: for instance, at any fixed upload data cap on non-iid CIFAR-10, it gains an impressive accuracy advantage of 10% over FedAvgM when given the same upload data cap;the accuracy gap remains 3% even when FedAvgM is given 2x the upload data cap, further demonstrating efficacy of FedDST. Code is available at: https://***/bibikar/feddst.

关键词： Learning systems

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