An implementation of real-time detection of cross-site scripting attacks on cloud-based web applications using deep learning

Cross-site scripting has caused considerable harm to the economy and individual privacy. Deep learning consists of three primary learning approaches, and it is made up of numerous strata of artificial neural networks. Triggering functions that can be used for the production of non-linear outputs are contained within each layer. This study proposes a secure framework that can be used to achieve real-time detection and prevention of cross-site scripting attacks in cloud-based web applications, using deep learning, with a high level of accuracy. This project work utilized five phases cross-site scripting payloads and Benign user inputs extraction, feature engineering, generation of datasets, deep learning modeling, and classification filter for Malicious cross-site scripting queries. A web application was then developed with the deep learning model embedded on the backend and hosted on the cloud. In this work, a model was developed to detect cross-site scripting attacks using multi-layer perceptron deep learning model, after a comparative analysis of its performance in contrast to three other deep learning models deep belief network, ensemble, and long short-term memory. A multi-layer perceptron based performance evaluation of the proposed model obtained an accuracy of 99.47%, which shows a high level of accuracy in detecting cross-site scripting attacks.

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Hybrid Deep Learning Model for Real-Time Detection of Distributed Denial of Service Attacks in Software Defined Networks

10.1007/978-981-16-3915-9_1 ◽

2021 ◽

pp. 1-13

Author(s):

Auther Makuvaza ◽

Dharm Singh Jat ◽

Attlee M. Gamundani

Keyword(s):

Deep Learning ◽

Real Time ◽

Denial Of Service ◽

Learning Model ◽

Denial Of Service Attacks ◽

Software Defined Networks ◽

Distributed Denial Of Service ◽

Real Time Detection ◽

Deep Learning Model

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Application of YOLO Deep Learning Model for Real Time Abandoned Baggage Detection

2018 IEEE 7th Global Conference on Consumer Electronics (GCCE) ◽

10.1109/gcce.2018.8574819 ◽

2018 ◽

Cited By ~ 1

Author(s):

Tossaporn Santad ◽

Piyarat Silapasupphakornwong ◽

Worawat Choensawat ◽

Kingkarn Sookhanaphibarn

Keyword(s):

Deep Learning ◽

Real Time ◽

Learning Model ◽

Deep Learning Model

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Detecting Damage Building Using Real-Time Crowdsourced Images And Transfer Learning

10.21203/rs.3.rs-964756/v1 ◽

2021 ◽

Author(s):

Gaurav Chachra ◽

Qingkai Kong ◽

Jim Huang ◽

Srujay Korlakunta ◽

Jennifer Grannen ◽

...

Keyword(s):

Social Media ◽

Deep Learning ◽

Real Time ◽

Transfer Learning ◽

Learning Model ◽

Research Community ◽

Rescue Work ◽

The Public ◽

Social Media Platforms ◽

Deep Learning Model

Abstract After significant earthquakes, we can see images posted on social media platforms by individuals and media agencies owing to the mass usage of smartphones these days. These images can be utilized to provide information about the shaking damage in the earthquake region both to the public and research community, and potentially to guide rescue work. This paper presents an automated way to extract the damaged building images after earthquakes from social media platforms such as Twitter and thus identify the particular user posts containing such images. Using transfer learning and ~6500 manually labelled images, we trained a deep learning model to recognize images with damaged buildings in the scene. The trained model achieved good performance when tested on newly acquired images of earthquakes at different locations and ran in near real-time on Twitter feed after the 2020 M7.0 earthquake in Turkey. Furthermore, to better understand how the model makes decisions, we also implemented the Grad-CAM method to visualize the important locations on the images that facilitate the decision.

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Insights into deep learning for earthquake magnitude and location estimation

10.5194/egusphere-egu21-4718 ◽

2021 ◽

Author(s):

Jannes Münchmeyer ◽

Dino Bindi ◽

Ulf Leser ◽

Frederik Tilmann

Keyword(s):

Deep Learning ◽

Real Time ◽

Early Warning ◽

Source Parameters ◽

Spectral Composition ◽

Learning Model ◽

Location Estimation ◽

Training Set ◽

Earthquake Source Parameters ◽

Deep Learning Model

The estimation of earthquake source parameters, in particular magnitude and location, in real time is one of the key tasks for earthquake early warning and rapid response. In recent years, several publications introduced deep learning approaches for these fast assessment tasks. Deep learning is well suited for these tasks, as it can work directly on waveforms and can learn features and their relation from data.A drawback of deep learning models is their lack of interpretability, i.e., it is usually unknown what reasoning the network uses. Due to this issue, it is also hard to estimate how the model will handle new data whose properties differ in some aspects from the training set, for example earthquakes in previously seismically quite regions. The discussions of previous studies usually focused on the average performance of models and did not consider this point in any detail.Here we analyze a deep learning model for real time magnitude and location estimation through targeted experiments and a qualitative error analysis. We conduct our analysis on three large scale regional data sets from regions with diverse seismotectonic settings and network properties: Italy and Japan with dense networks (station spacing down to 10 km) of strong motion sensors, and North Chile with a sparser network (station spacing around 40 km) of broadband stations. We obtained several key insights. First, the deep learning model does not seem to follow the classical approaches for magnitude and location estimation. For magnitude, one would classically expect the model to estimate attenuation, but the network rather seems to focus its attention on the spectral composition of the waveforms. For location, one would expect a triangulation approach, but our experiments instead show indications of a fingerprinting approach. Second, we can pinpoint the effect of training data size on model performance. For example, a four times larger training set reduces average errors for both magnitude and location prediction by more than half, and reduces the required time for real time assessment by a factor of four. Third, the model fails for events with few similar training examples. For magnitude, this means that the largest events are systematically underestimated. For location, events in regions with few events in the training set tend to get mislocated to regions with more training events. These characteristics can have severe consequences in downstream tasks like early warning and need to be taken into account for future model development and evaluation.

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Real-time automated diagnosis of colorectal cancer invasion depth using a deep learning model with multimodal data (with video)

Gastrointestinal Endoscopy ◽

10.1016/j.gie.2021.11.049 ◽

2021 ◽

Author(s):

Zihua Lu ◽

Youming Xu ◽

Liwen Yao ◽

Wei Zhou ◽

Wei Gong ◽

...

Keyword(s):

Colorectal Cancer ◽

Deep Learning ◽

Real Time ◽

Learning Model ◽

Cancer Invasion ◽

Automated Diagnosis ◽

Invasion Depth ◽

Multimodal Data ◽

Deep Learning Model

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Real-Time Estimation of Eye Movement Condition Using a Deep Learning Model

10.1007/978-3-030-90963-5_11 ◽

2021 ◽

pp. 132-143

Author(s):

Akihiro Sugiura ◽

Yoshiki Itazu ◽

Kunihiko Tanaka ◽

Hiroki Takada

Keyword(s):

Deep Learning ◽

Real Time ◽

Eye Movement ◽

Time Estimation ◽

Learning Model ◽

Movement Condition ◽

Real Time Estimation ◽

Deep Learning Model

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A deep learning model for real-time mortality prediction in critically ill children

Critical Care ◽

10.1186/s13054-019-2561-z ◽

2019 ◽

Vol 23 (1) ◽

Cited By ~ 5

Author(s):

Soo Yeon Kim ◽

Saehoon Kim ◽

Joongbum Cho ◽

Young Suh Kim ◽

In Suk Sol ◽

...

Keyword(s):

Deep Learning ◽

Real Time ◽

Critically Ill ◽

Learning Model ◽

Mortality Prediction ◽

Critically Ill Children ◽

Deep Learning Model

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IGRNet: A Deep Learning Model for Non-Invasive, Real-Time Diagnosis of Prediabetes through Electrocardiograms

Sensors ◽

10.3390/s20092556 ◽

2020 ◽

Vol 20 (9) ◽

pp. 2556

Author(s):

Liyang Wang ◽

Yao Mu ◽

Jing Zhao ◽

Xiaoya Wang ◽

Huilian Che

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Real Time ◽

Clinical Symptoms ◽

Characteristic Curve ◽

Learning Model ◽

Machine Learning Algorithms ◽

Test Set ◽

Non Invasive ◽

Deep Learning Model

The clinical symptoms of prediabetes are mild and easy to overlook, but prediabetes may develop into diabetes if early intervention is not performed. In this study, a deep learning model—referred to as IGRNet—is developed to effectively detect and diagnose prediabetes in a non-invasive, real-time manner using a 12-lead electrocardiogram (ECG) lasting 5 s. After searching for an appropriate activation function, we compared two mainstream deep neural networks (AlexNet and GoogLeNet) and three traditional machine learning algorithms to verify the superiority of our method. The diagnostic accuracy of IGRNet is 0.781, and the area under the receiver operating characteristic curve (AUC) is 0.777 after testing on the independent test set including mixed group. Furthermore, the accuracy and AUC are 0.856 and 0.825, respectively, in the normal-weight-range test set. The experimental results indicate that IGRNet diagnoses prediabetes with high accuracy using ECGs, outperforming existing other machine learning methods; this suggests its potential for application in clinical practice as a non-invasive, prediabetes diagnosis technology.

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DiPLIP: Distributed Parallel Processing Platform for Stream Image Processing Based on Deep Learning Model Inference

Electronics ◽

10.3390/electronics9101664 ◽

2020 ◽

Vol 9 (10) ◽

pp. 1664

Author(s):

Yoon-Ki Kim ◽

Yongsung Kim

Keyword(s):

Image Processing ◽

Deep Learning ◽

Parallel Processing ◽

Real Time ◽

Large Scale ◽

Learning Model ◽

Streaming Data ◽

Model Inference ◽

Processing Platform ◽

Deep Learning Model

Recently, as the amount of real-time video streaming data has increased, distributed parallel processing systems have rapidly evolved to process large-scale data. In addition, with an increase in the scale of computing resources constituting the distributed parallel processing system, the orchestration of technology has become crucial for proper management of computing resources, in terms of allocating computing resources, setting up a programming environment, and deploying user applications. In this paper, we present a new distributed parallel processing platform for real-time large-scale image processing based on deep learning model inference, called DiPLIP. It provides a scheme for large-scale real-time image inference using buffer layer and a scalable parallel processing environment according to the size of the stream image. It allows users to easily process trained deep learning models for processing real-time images in a distributed parallel processing environment at high speeds, through the distribution of the virtual machine container.

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