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检索条件"机构=Computer Science and Engineering Indian Institute of Information Technology"

共 45384 条记录，以下是101-110 订阅

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An Ensemble-Based Approach for Cyber Attack Detection in Financial Systems 5th

An Ensemble-Based Approach for Cyber Attack Detection in Fin...

引用

5th International Conference on Deep Learning, Artificial Intelligence and Robotics, ICDLAIR 2023

作者： Kumar, Dhanisht Srivastava, Harshit Bhamare, Kartik S. Kumar, Malay Department of Computer Science and Engineering Indian Institute of Information Technology Karnataka Dharwad India

ISBN: (纸本)9783031609343

Digital commerce is the process of exchanging money or assets using electronic or digital channels such as the internet or mobile devices. Instead of physical cash or checks, individuals or businesses can use digital platforms to transfer money, make payments or conduct financial transactions securely and efficiently. As more and more people conduct financial transactions online, the risk of cyberattacks targeting financial institutions will increase. This article presents an integrated approach to detecting cyber-attacks in the financial sector. The proposed method combines learning models including decision trees, random forests, and gradient boosting to increase the accuracy and robustness of the model. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Cyber attacks

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A Novel Framework for Securing information in Cyber Security System using Authentication, Intrusion Detection and Deep Learning-based Risk Prediction Tasks 5

A Novel Framework for Securing Information in Cyber Security...

引用

5th IEEE International Conference for Emerging technology, INCET 2024

作者： Naik, R. Lakshman Jain, Sourabh Department of Computer Science and Engineering Indian Institute of Information Technology Haryana Sonepat India

ISBN: (纸本)9798350361155

The cyber security has become a hot research topic nowadays. The term cyber security is frequently employed replaceable with the term data security. In recent days, each system faces the cyber security threat virtually and the model framework should have the capacity to combine the security features and work as the system's integral elements. Various works have been conducted to discover the answer to the evaluation challenge of cyber security and numerous architectures have been designed. However, the techniques face distinct issues though it is functioning well initially at the development time. Hence, an efficient and advanced cyber security framework is implemented in this paper to prevent cyber attacks using deep learning. Initially, to predict the risks in cyber security devices, the necessary data is fetched from the available resources. The gathered data is forwarded to the authentication operation and then the authenticated data is given to the intrusion detection stage. After that, the intrusion-detected data is subjected to the risk prediction stage, where the Weighted Recurrent Neural Network (WRNN) is supported to forecast the risk level of the network. Here, the Dung Beetle Optimizer (DBO) is utilized to tune the weights optimally in WRNN. This authentication, intrusion detection, and risk prediction operations have enriched the security of the cyber networks. Lastly, the efficacy and the security of the implemented model are analyzed with its related models by utilizing several performance metrics. © 2024 IEEE.

关键词： Forecasting

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An Efficient Framework for Crime Prediction Using Feature engineering and Machine Learning 1

引用

5th International Conference on Data and information sciences, ICDIS 2023

作者： Vengadeswaran Binu, Dhanush Rai, Lokesh Department of Computer Science and Engineering Indian Institute of Information Technology Kottayam Kottayam India

ISBN: (数字)9789819969067

ISBN: (纸本)9789819969050

The growth of crime in society produces insecurity among people and severely impacts the country’s economic development. Understanding crime patterns is necessary to provide a proactive response to curb criminal activities. Most crime prediction models harnessing machine learning/deep learning techniques fail to exhibit significant results for the vague and inconsistent dataset. This is due to the non-consideration of suitable pre-processing and feature engineering techniques, resulting in an unreliable and inaccurate model. Hence in this work, an efficient framework for crime prediction using Feature engineering and Machine learning is proposed. Initially, the vague and inconsistent input dataset is pre-processed using Feature Scaling and Encoding input values. After pre-processing DBSCAN clustering is applied to extract geospatial features, which is used for improving model accuracy. Then SelectKbest library is used to select the 10 best features for the prediction model. Further, the SMOTE library is used to oversample crime types with the lowest sample size. Finally, for classifying the crime types, XGBoost and LGBM classifier models are evaluated. The proposed crime prediction model was validated by deploying an Amazon EC2 P3 instance in the cloud environment. The proposed model yields an improved accuracy of 62% for the XGBoost Classifier and 63% for the LGBM Classifier compared with KNN and Decision Tree. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Forecasting

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Optimizing IoT Workloads for Minimal Time and Energy with UAV Networks Using Deep Q Network 18

Optimizing IoT Workloads for Minimal Time and Energy with UA...

引用

18th IEEE International Conference on Vehicular Electronics and Safety, ICVES 2024

作者： Singhal, Priyansh Chauhan, Harshit Hazra, Abhishek Joshi, Piyush Indian Institute of Information Technology Sri City Department of Computer Science and Engineering India

ISBN: (纸本)9798350387278

The rapid growth in the use of IoT devices has highlighted significant challenges due to their limited computational power and battery life, often resulting in long task execution times and potential battery depletion. The problem we address is the inefficiency in task completion and high battery usage in IoT devices. Our key objective is to minimize time delays and energy consumption in IoT workloads. To solve this problem, we propose a partial offloading algorithm for IoT devices, utilizing Unmanned Aerial Vehicles (UAVs) due to their larger battery capacity and greater computational power. UAVs provide flexibility in task management by moving to high-demand areas. Given the battery constraints of UAVs, a network of UAVs is used to share tasks among neighboring UAVs, balancing the load. Additionally, tasks can be offloaded to a ground-based cloud server to prevent overloading the UAV network. Our main contribution is the implementation of a Deep Q Network (DQN) reinforcement learning approach to manage the dynamic nature of task distribution. We achieved a significant reduction in both time delays and energy consumption. Our results displayed approximately a 15-fold reduction in total delay and a 6-fold reduction in total energy consumption, demonstrating the effectiveness of DQNs in this task offloading scenario. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Real-Time Incremental Federated Learning for Weather Prediction with UAV based Data Collection 18

Real-Time Incremental Federated Learning for Weather Predict...

引用

18th IEEE International Conference on Vehicular Electronics and Safety, ICVES 2024

作者： Singhal, Priyansh Hazra, Abhishek Joshi, Piyush Indian Institute of Information Technology Sri City Department of Computer Science and Engineering India

ISBN: (纸本)9798350387278

This article presents a comprehensive framework for real-time incremental federated learning aimed at enhancing weather prediction accuracy through UAV-based data collection. In our proposed system, UAVs are deployed across multiple regions within cities to gather diverse and comprehensive weather data. The collected data is streamed in real-time to central city servers, which aggregate the data and transmit it to a state-level server. At the state server, data from multiple cities are combined, and models are incrementally trained on newly received data in a step-wise manner. Each incremental step involves training on 80% of the new data and testing on the remaining 20%. Our key contribution is the integration of a weighted federated learning approach, where periodic federated learning steps utilize the weighted FedAvg algorithm. The weights are determined based on the accuracy of each model, allowing for more effective aggregation of model updates. Experimental results demonstrate a significant improvement in prediction accuracy, achieving a 24% increase compared to baseline independent machine learning models trained separately at the state level. © 2024 IEEE.

关键词： Federated learning

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Improving Time Series Forecasting Accuracy with Transformers: A Comprehensive Analysis with Explainability 3

Improving Time Series Forecasting Accuracy with Transformers...

引用

3rd International Conference on Electrical, Electronics, information and Communication Technologies, ICEEICT 2024

作者： Joseph, Susmera Jo, Ashly Ann Raj, Ebin Deni Indian Institute of Information Technology Dept. of Computer Science Engineering Kottayam India

ISBN: (纸本)9798350369083

Time series forecasting is crucial in numerous sectors, including healthcare, energy, and finance. Transformer models, initially designed for natural language processing applications, demonstrate potential in capturing temporal dependencies within sequential data. In this paper, the application of transformer models in time-series forecasting is explored, and their performance and interpretability are investigated. Transparent models build trust and facilitate decision-making, especially in industries with ethical guidelines. While inherently interpretable time-series Transformers exist, there is a gap in research regarding post hoc methods, which is an area ripe for exploration. Understanding and comparing complex Transformer models for time series forecasting can benefit from advancements in explainability, ensuring accurate and comprehensible predictions. In comparison to five other transformer-based models and classical models, the Temporal Fusion Transformer (TFT) model outperformed them in short-term forecasting. Further enhancement of the model performance was achieved through a meticulous analysis of the Encoder-Decoder's importance, which contributed significantly to refining the forecasting accuracy. The vanilla transformer was explained using permutation feature importance. The model stability was analyzed using perturbation methods. © 2024 IEEE.

关键词： Spatio-temporal data

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Reconnaissance Detection in Decentralised IoT System using Model-Contrastive Federated Learning 18

Reconnaissance Detection in Decentralised IoT System using M...

引用

18th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2024

作者： Dutta, Rajdeep Kumar Chhetry, Bishal Matam, Rakesh Barbhuiya, Ferdous Ahmed Indian Institute of Information Technology Dept. of Computer Science and Engineering Guwahati India

ISBN: (纸本)9798350391725

The detection of reconnaissance attacks is crucial for safeguarding Internet of Things (IoT) environments, which are inherently more vulnerable and resource-constrained compared to traditional computing systems. Traditional centralised detection methods face significant challenges, such as privacy concerns and limited scalability due to the need to aggregate raw data on a central server. These issues become particularly pronounced in IoT environments where devices are diverse and geographically distributed. To address these challenges, we propose ReconGuard, a novel approach leveraging Model Contrastive Federated Learning (MCFL). ReconGuard enables collaborative training across multiple IoT devices without the necessity of sharing raw data, thereby preserving user privacy and enhancing data security. The approach integrates contrastive learning techniques, which improve the model's ability to discriminate between benign and malicious activities by contrasting positive (similar) and negative (dissimilar) data pairs. Our experimental results demonstrate that the ReconGuard-based detection system provides a scalable and privacy-preserving solution for identifying reconnaissance activities. These activities are often the initial step in more severe cyber threats, such as botnet attacks, which can lead to significant disruption and damage. By effectively detecting reconnaissance activities, ReconGuard enhances the overall cybersecurity framework of IoT environments. This research presents a viable and innovative method for improving cybersecurity in IoT systems, addressing the critical need for scalable, efficient, and privacy-preserving intrusion detection solutions. The use of MCFL in ReconGuard not only mitigates the challenges of data centralisation but also leverages the distributed nature of IoT networks to enhance robustness against cyber threats. © 2024 IEEE.

关键词： Contrastive Learning

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YOLOv8 Based Detection of Fall Armyworm in Field Crops 5

YOLOv8 Based Detection of Fall Armyworm in Field Crops

引用

5th IEEE India Council International Subsections Conference, INDISCON 2024

作者： Mhala, Prit Gupta, Amogh Sharma, Sanjeev Indian Institute of Information Technology Pune Department of Computer Science and Engineering Pune India

ISBN: (纸本)9798350376753

In India, the role of agriculture is pivotal contributing to the growth of the indian economy and employment. It is essential to focus on the security of food which ensures individuals' health. Pest plays a major role in damaging and harming crop growth leading to effective control measures for pests. One such pest, Spodoptera frugiperda(Fall armyworm), poses significant damage to various crops. Deep Learning provides promising solutions by using Object detection techniques to detect the fall armyworm pest at early stages to avoid damage. This work intends to use YOLO as an object detection method. This paper achieved remarkable results using the YOLOv8 models. A mean average precision (mAP50) value of 78.60%, 80.67%, and 89.50% was achieved using YOLOv81, YOLOv8m, and YOLOv8n, respectively. The best results were achieved by YOLOv8n giving 89.50% mean average precision. This work, thus aims for precise detection of fall armyworms to enhance crop safety. © 2024 IEEE.

关键词： Invertebrates

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A Diagnosis Model Based on Federated Learning for Lung Cancer Classification 2nd

A Diagnosis Model Based on Federated Learning for Lung Cance...

引用

2nd International Conference on science, engineering Management and information technology, SEMIT 2023

作者： Babu, Ann Mary Bellamkonda, Sivaiah Computer Science and Engineering Department Indian Institute of Information Technology Kerala Kottayam India

ISBN: (纸本)9783031722837

Lung cancer, causing a significant number of fatalities annually, is a prominent contributor to global mortality rates among individuals of both genders, with an estimated five million cases resulting in death each year. The utilization of Computed Tomography (CT) scans contribute valuable data for the detection of lung cancer. The main objectives of this study are to diagnose malignant lung nodules in the provided input lung images and to classify lung nodule based on its level of severity. This study proposes a solution wherein locally computed updates are combined to stimulate the learning of a shared model, while the training data remains dispersed throughout mobile devices. Federated Learning (FL) is an advanced technology that operates in a collaborative and decentralized manner, with the primary objective of preserving privacy. Its purpose is to address the obstacles posed by data silos and the sensitivity of data. Therefore, the FL approach is employed for the classification of medical images in collaborative and real-world circumstances. A new approach is proposed in this paper, where the model is trained on data distributed across multiple institutions, and the training process occurs locally at each institution. The performance of the proposed method in terms of accuracy, specificity and sensitivity is evaluated and compared with that of the existing models such as Googlenet, Alexnet, and SVM. The proposed method performs comparably better in all three performance measures indicating that it works better than other methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Lung cancer

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IoT-Based Pesticide Detection in Fruits and Vegetables Using Hyperspectral Imaging and Deep Learning 4th

IoT-Based Pesticide Detection in Fruits and Vegetables Usi...

引用

4th EAI International Conference on Cognitive Computing and Cyber Physical Systems, IC4S 2023

作者： Augustin, Anju Kiliroor, Cinu C. Department of Computer Science and Engineering Indian Institute of Information Technology Kottayam Kottayam India

ISBN: (纸本)9783031488870

Fruits and vegetables contain rich nutrients and vitamins. So that they are part of our daily diet. For proper cell growth and health, we need these nutrients. Today in most crops during their growth and post-harvesting preservation different kinds of pesticides were used. Normal usage of such pesticides not that much affects health. But the actual situation is beyond our control. From soil preparation to the post-harvesting stage, pesticides are being added at alarming rates. It affects our health in a harmful way and leads to major health issues. Various studies exist to detect the pesticide levels in fruits and vegetables. This article analyses different existing methods of pesticide detection and examines their features and problems. Through this study, it is understood that Hyperspectral Imaging (HSI) is a very good method, and with it, more accurate results can be obtained by Transfer Learning (few-shot learning). This paper proposes an architecture and algorithm based on HSI and few-shot learning. Future studies are needed in this area to convert an RGB image to a spectral image because the HSI device is very expensive. © 2024, ICST institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Nutrients

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