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Fortifying Cloud DevSecOps Security Using Terraform Infrastructure as Code Analysis Tools

Fortifying Cloud DevSecOps Security Using Terraform Infrastr...

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2024 international conference on Electrical and computer engineering Researches, ICECER 2024

作者： Singh, Rashika Yeboah-Ofori, Abel Kumar, Saurabh Ganiyu, Aishat School of Computing and Eng University of West London United Kingdom University of Allahabad BTech in Computer Sc.and Eng India School of Eng Phys and Math Royal Holloway University United Kingdom

ISBN: (纸本)9798331539733

Fortifying Cloud Security has become inevitable due to challenges such as misconfigurations, coding errors, and compromised secrets or passwords that impact infrastructure as a service during infrastructure such as code automation (IaC). These challenges require code analysis tools to enhance security during infrastructure automation. Setting up a simple cloud architecture is quick, but human errors are still common, especially when cloud infrastructure can be deployed with just a few clicks. Terraform provides a ready-made infrastructure as code modules to build and scale cloud-hosted applications. However, cyber attackers exploit these vulnerabilities and gain access to sensitive data or resources without authorization due to configuration errors, inadequate storage, and infrastructure manipulation, resulting in unauthorized deployments or alterations. That affects the availability of resources during infrastructure deployment using attacks such as DoS attacks, injection attacks, Man in the Middle (MITM), malware spread, remote code execution (RCE), and phishing attacks to penetrate the cloud infrastructures. The paper aims to analyze Terraforms infrastructure as code in cloud security to fortify codes and assist DevSecOps engineers in identifying misconfiguration in Terraform scripts. The paper's contributions are threefold. First, we explore cloud security by securing IaC solutions on Terraform. We consider security issues, including misconfigurations and coding errors, present in Terraform IaC. Secondly, we implement a static analysis tool for terraform by comparatively analyzing existing tools. Finally, we provide a comparative analysis of terraform IaC on tools including Checkov, Tfsec, Tflint, and Terrascan for suitability based on their key features and performance metrics to enhance security. © 2024 IEEE.

关键词： Cloud security

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Lung Cancer Prediction and Risk Assessment: A Machine Learning Approach Integrating Symptoms and Etiological Factors 3

Lung Cancer Prediction and Risk Assessment: A Machine Learni...

引用

3rd IEEE international conference on Robotics, automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2024

作者： Hossain Sarkar, Md. Mosharrof Afrin, Sadiha Reza, Md Tuhin Bokshi, Md Abid Hasan Roni Mim, Sharmin Sultana Faridpur Engineering College Department of Computer Science and Engineering Faridpur Bangladesh Faridpur Engineering College Department of Electrical and Electronic Engineering Faridpur Bangladesh

ISBN: (纸本)9798331534400

Lung cancer requires accurate risk assessment and early detection due to its high prevalence and fatality rate. The simultaneous analysis of clinical symptoms and etiological factors using Machine Learning revolutionizes our understanding of lung cancer and patient outcomes. Machine Learning models are built on comprehensive patient data, including lifestyle, symptoms, and 12 variables. We used ten models for precise lung cancer diagnosis, including Decision Tree (DT), K-nearest neighbors (KNN), Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR), Naive Bayes (NB), XGBoost, Gradient Boost, CatBoost and AdaBoost. At the National Institute of Cancer Research & Hospital (NICRH) in Mohakhali, TB Gate Road, Dhaka, Bangladesh, we collected 346 data directly from patients using questionnaires, with permission from the director. For every model, we performed parameter tuning to determine the ideal ratio of complexity to accuracy. Notably, with the best accuracy of 98.43% and an excellent area under the curve (AUC) of 0.983, the Decision Tree (DT) performed better than any other classifier. The motivation behind this research is to predict the risk assignment of lung cancer using various machine learning models. © 2024 IEEE.

关键词： Lung cancer

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Combined Location Method Based on Error State Kalman Filter 4

Combined Location Method Based on Error State Kalman Filter

引用

4th international conference on computer engineering and Application, ICCEA 2023

作者： Yang, Qinghua Zhang, Xing School of Mechatronic Engineeringand Automation Shanghai University Shanghai China

ISBN: (纸本)9798350347548

Based on the data fusion method of iterative Error State Kalman Filter framework, this paper realizes the combined location of optical and inertial navigation. The method uses a tight coupling combination, uses the measurement value of Optical Position Sensor (OPS) to the marked point to correct the prior estimation of IMU, and takes the data error of OPS and IMU as the observation and state variables, thus simplifying the calculation of Jacobian matrix and avoiding the complexity caused by nonlinearity. The experiment shows that this method can effectively improve the positioning accuracy, enhance the robustness of OPS, and has good practicability. © 2023 IEEE.

关键词： Jacobian matrices

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Detection of Anthracnose on Mango Tree Leaf Using Convolutional Neural Network 15

Detection of Anthracnose on Mango Tree Leaf Using Convolutio...

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15th international conference on computer and automation engineering, ICCAE 2023

作者： Yumang, Analyn N. Samilin, Christian Joseph N. Sinlao, John Christian P. Mapúa University School of Electrical Electronics and Computer Engineering Manila Philippines

ISBN: (纸本)9798350396225

Mangoes have been one of the most important products that are being produced mostly within tropical regions here in the Philippines. Anthracnose is the most common and serious disease that can occur on mango crops in the country. It is a disease caused by a fungus called Colletotrichum gloeosporioides, which targets leaves, fruits, twigs, and flowering panicles of the crop. For this study, the researchers' aim is to detect anthracnose disease in mango leaves and classify them as healthy or unhealthy. The system will implement (You Only Look Once, Version 3) YOLOv3, which uses the features learned in Convolutional Neural Network to detect a specific object, live videos, and even lesions of plants. The training package comprises around 80.282% of the photographs, while the trial package contains approximately 19.718% of the images. This is done by randomly splitting the data into two sets. This ratio distribution is frequently used in neural network applications. The PDF format was used on the images with 600 dpi for a better resolution. After training the system it obtained 60.680% mean average precision (mAP), 7.79fps, and a lower total validation loss of 20.93. After training the system and using the confusion matrix an accuracy of 83.33% was obtained. © 2023 IEEE.

关键词： Crops

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Detection of Panama Disease on Banana Leaves Using the YOLOv4 Algorithm 15

Detection of Panama Disease on Banana Leaves Using the YOLOv...

引用

15th international conference on computer and automation engineering, ICCAE 2023

作者： Ibarra, Nionel C. Rivera, Matthew P. Manlises, Cyrel O. Mapúa University School of Electrical Electronics and Computer Engineering Manila Philippines

ISBN: (纸本)9798350396225

Manual visual analysis is the most used method in determining plant diseases. Although for many years, farmers and growers have depended on knowledge and experience in examining the condition of their plants for any possible ailments, which can be tedious and demanding. Bananas are the third most-sold crop in the Philippines. The Philippines is the top export of bananas within Asia, accumulating more than 80% of gross banana exports from Asian countries. Yet, for as long as the banana exportation industry has been running, banana plantations have constantly been suffering under the threat of various diseases emerging through the years. But the affliction that has significantly impacted the export trade in the Philippines is the Fusarium oxysporum, Panama Tropical Race 4, and Panama Wilt, more commonly known as the Panama Disease. By utilizing the YOLOv4 algorithm, we can save time and effort from manually analyzing and determining the possible presence of the disease, as well as achieve real-time judgment, thus, can potentially assist in reducing the tremendous loss caused by Fusarium oxysporum. The device developed has a 90.0% accuracy in determining the presence of Panama Disease through photos of banana leaves. © 2023 IEEE.

关键词： Fungi

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Optimizing Fault Diagnosis in Smart Grids Using Convolutional Neural Networks for Enhanced Speed and Stability 24

Optimizing Fault Diagnosis in Smart Grids Using Convolutiona...

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9th international conference on Cyber Security and Information engineering, ICCSIE 2024

作者： Dai, Jiawei Zhang, Shengzong Xu, Junyao Zhang, Ruiqi Zhou, Junyi Li, Dongrun He, Penghao Bai, Haoran Electrical Engineering and Automation Northeast Electric Power University China Electrical Engineering and Automation Harbin Institute of Technology China

ISBN: (纸本)9798400718137

With the increasingly severe global energy crisis and the rapid development of computer and information technology, smart grids have emerged. However, with the transformation to smart grids, the number and types of various terminals, sensors, and new loads in the power system have increased significantly. The massive amount of information and mixed redundant and noisy information accessed by the grid poses a huge challenge for fault handling. Therefore, this article focuses on how to improve fault handling speed in the context of smart grids and how to efficiently and quickly select key fault features to enhance fault handling speed and ensure a stable and reliable power supply system. The study revolves around the optimization method for rapid fault handling based on machine learning. An intelligent grid fault diagnosis model based on convolutional neural networks was built using TensorFlow. The powerful feature extraction and learning capabilities of Convolutional Neural Networks (CNN) were used for fault data training and diagnosis. Experimental analysis was conducted on different aspects to establish fault diagnosis model parameters. The experimental results show that this fault diagnosis model exhibits good performance in terms of training time, accuracy, and stability. © 2024 Copyright held by the owner/author(s).

关键词： Convolutional neural networks

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Smart Agriculture: Enhancing Crop Management through IoT-Based Real-Time Monitoring and automation 9

Smart Agriculture: Enhancing Crop Management through IoT-Bas...

引用

9th international conference on Information Technology and Digital Applications, ICITDA 2024

作者： Paliyanny, Hushalictmy Thinakaran, Rajermani Jalari, Somasekar Neerugatti, Vikram Nalluri, Madhusudana Rao Cholla, Ravindra Raman Inti International University Faculty of Data Science and Information Technology Malaysia Faculty of Engineering and technology Department of Computer Science and Engineering Bangalore India Icfai Foundation for Higher Education Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798331510732

Internet of Things plays an important role in agriculture in order to provide an innovative and smart solution to traditional farming. IOT is all about connecting physical devices to the internet and can access from a remote place. So that crop monitoring becomes easy even if the farmer is in a remote place. Due to automation, human efforts are required less. Also the IoT data helps the farmer make decisions about planting, harvesting or fertilizing. This paper explains the system we have implemented for monitoring the agriculture with the help of IoT. The system is developed in order to provide real time information on crop conditions to the farmer. For that, it uses various types of sensors connected to the microcontroller in order to measure parameters like temperature, soil moisture, and water level, and accordingly, it activates actuators like a fan, motor or pump to manage ventilation and irrigation. We have used the GSM module to send the alert to the farmer and the WiFi module is used to send the agriculture data to the cloud platform. So that farmer can monitor the farm or crops remotely. The system is continuously collecting farm data from the sensors. If the crop soil is very dry them system will turn on the water pump. Also, for high temperature, the fan will get activated in order to cool down that area. This feature is mostly used in indoor farming. The farmer can monitor the farm condition in real time with the help of cloud platform. This system is helpful for improving productivity, crop management, and save resources i.e. efficient use of water which prevents water wastage. © 2024 IEEE.

关键词： Soil moisture

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Intelligent Face Recognition System to Enhance Security in Residential Areas

Intelligent Face Recognition System to Enhance Security in R...

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2023 international conference on New Frontiers in Communication, automation, Management and Security, ICCAMS 2023

作者： Santosh Reddy, P. Mamatha, A. Sreelatha, P.K. Bnm Institute of Technology Department of Computer Science & Engineering Bangalore India Ramaiah Institute of Technology Department of Computer Science & Engineering Bangalore India Presidency University Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350317060

The Rising price of security measures has been a significant concern recently. The study suggests creating a real-time facial recognition system to process images quickly to reduce monitoring costs and ensure that homes and businesses are secure. The primary objective of this research was to develop a deep learning-based facial recognition method for use in smart homes. This study aims to improve existing face recognition structures by using a hybrid of the SVM Increased and a convolutional neural network (CNN). The usefulness of the planned model is shown by comparing it to other state-of-the-art methods. The study presents a tree-based deep neural network for cloud-based face recognition that uses less data storage space without sacrificing accuracy. Multiple volumes are generated from the model's input volume, and trees are then constructed for each volume, considering their height and branching structure. Each split is represented by a layer of convolution and two non-linear functions forming a residual function together. Evaluate the proposed model on many open-source datasets and compare it to state-of-the-art deep models for face recognition. © 2023 IEEE.

关键词： Convolution

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Expression recognition based on convolutional difference contextual feature fusion

Expression recognition based on convolutional difference con...

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2022 international conference on Electronic Information engineering and computer Science, EIECS 2022

作者： Lu, Shengfu Yang, Chuang Li, Mi Kang, Jiaming Faculty of Information Technology Beijing University of Technology Beijing International Collaboration Base on Brain Informatics and Wisdom Services Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing China Department of Automation Faculty of Information Technology Beijing University of Technology Beijing China Department of Automation Faculty of Information Technology Beijing China

ISBN: (纸本)9781510663312

Facial expression recognition has always been a focus of study in emotion computing. With the aim of solving the problem that previous facial expression recognition studies are susceptible to individual differences and background noise, as well as the loss of facial expression features in deep networks of deep convolutional networks, we propose a facial expression method based on convolutional difference contextual feature fusion. The convolutional difference module can carry out difference operation between emotional image and neutral image in the input image, eliminate individual difference and background noise in expression features and improve the accuracy and generalization of the network. Finally, convolutional difference and contextual features are combined to improve the accuracy of the network further. Experiment results of 4 public facial expression datasets illustrate that compared with the existing facial expression recognition methods, this method has higher accuracy, can extract facial expression features more effectively, and has a better generalization ability. © 2023 SPIE.

关键词： Convolutional neural networks

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Detection and Classification of Defects in 3D Printing using a Novel Skewness and Kurtosis-based Parameter of Sound Signals and Machine Learning

Detection and Classification of Defects in 3D Printing using...

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2024 international conference on Control, automation and Diagnosis, ICCAD 2024

作者： Lopes, Thiago Glissoi Kennerly, Victoria Dutra Aguiar, Paulo Roberto Junior, Cristiano Soares De Carvalho Monson, Paulo Monteiro Daddona, Doriana Marilena São Paulo State University - Unesp Department of Electrical Engineering Bauru Brazil University of São Paulo - Usp Department of Electrical and Computer Engineering São Carlos Brazil University of Naples Frederico Ii - UniNa Department of Chemical Material and Industrial Production Engineering Naples Italy

ISBN: (纸本)9798350361025

This work proposes a monitoring strategy based on kurtosis and skewness of sound signals to detect and classify the machine conditions in fused deposition modeling (FDM). The methodology consisted in experimental tests conducted in a 3D printer in which an electret microphone was attached to the extruder support. The signals were acquired by an oscilloscope at 200 kHz, and then digitally processed in MATLAB. The results showed that the proposed parameter along with machine learning models produced a significant improvement when compared to the use of the skewness and kurtosis alone. © 2024 IEEE.

关键词： Condition monitoring

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