Enhanced authentication for de-duplication of big data on cloud storage system using machine learning approach

Cyber hacking can be defined as the process of observing the incidents happening in a computer network or system and inspecting them for indications of possible incidents, which includes either violation or threats of violation in the policies of computer security, the allowable use of policies or the practices of maintaining standard security. CHS aid the network in automating the process of intrusion detection. CHPS is software that consists of all the abilities of the anomalies. In addition, it also strives to widen the possible incidents and cyber hacking methodologies with similar abilities. In the case of CHPS, it allows administrators to turn off prevention attributes in anomaly products, making them work as a cyber hacking system. Respectively, for compressing the benefits of both IPS and CHS, a novel term, cyber hacking, and prevention systems (CHPS), is used for all the further chapters to infer both CHS and IPS approaches. In this research, three algorithms, namely decision stump method (DSM), support vector machine (SVM), and artificial neural network (ANN), were used. From the results obtained, the proposed ANNAccuracy of 92.3%, MSE of 0.000119, Log Loss of 0.4288, and Mathews Coefficient of 0.9010 were proposed. The tool used is Jupyter Notebook, and the language used is Python.

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Deep Learning-Based Vehicle Detection and Classification in Traffic Management for Intelligent Transportation Systems

Hemalakshmi,

Muthukumaravel

2024

Cross-Industry AI Applications

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For intelligent transportation systems (ITSs) and planning that makes use of exact location intelligence, accurate vehicle classification and detection are topics that are becoming more vital. Although computer vision and deep learning (DL) are smart techniques, there remain issues with effective real-time detection and categorization. The requirement for a large training dataset and the domain-shift problem are two prevalent issues in this area. This research proposes the use of the YOLOv3 (you only look once) algorithm to provide an effective and efficient framework for vehicle recognition and classification from traffic video surveillance data. Along with the other deep learning-based algorithms like faster RCNN and VGG16 pre-trained model, a machine learning model using bag of features (BoF) + support vector machine (SVM) is also compared and analyzed for detecting and classifying vehicles.

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Enhanced authentication for de-duplication of big data on cloud storage system using machine learning approach

Cited by 3 publications

References 28 publications

Distributed Data Processing Optimization Based on Edge Computing in Intelligent Transportation System

Distributed Data Processing Optimization Based on Edge Computing in Intelligent Transportation System

Artificial Neural Network-Based Efficient Cyber Hacking Detection System Using Deep Learning Approaches

Deep Learning-Based Vehicle Detection and Classification in Traffic Management for Intelligent Transportation Systems

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