The Improved LSTM and CNN Models for DDoS Attacks Prediction in Social Media

2019 ◽  
Vol 9 (1) ◽  
pp. 1-18 ◽  
Author(s):  
Rasim M. Alguliyev ◽  
Ramiz M. Aliguliyev ◽  
Fargana J Abdullayeva
Keyword(s):  
Social Networking ◽  
Short Term Memory ◽  
Classification Performance ◽  
Automatic Identification ◽  
Test Accuracy ◽  
The Novel ◽  
Short Term ◽  
Accuracy Test ◽  
Twitter Data ◽  
Long Short Term Memory

Automatic identification of conversations related to DDoS events in social networking logs helps the organizations act proactively through early detection of negative and positive sentiments in cyberspace. In this article, the authors describe the novel application of a deep learning method to the automatic identification of negative and positive sentiments in large volumes of social networking texts. The authors present classifiers based on Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) to address this problem domain. The improved CNN and LSTM architecture outperform the classification techniques that are common in this domain including classic CNN and classic LSTM in terms of classification performance, which is measured by recall, precision, f-measure, train loss, train accuracy, test loss, and test accuracy. In order to predict the occurrence probability of the DDoS events the next day, the negative and positive sentiments in social networking texts are used. To verify the efficacy of the proposed method experiments is conducted on Twitter data.

The Improved LSTM and CNN Models for DDoS Attacks Prediction in Social Media

2021 ◽  
pp. 436-454
Author(s):  
Rasim M. Alguliyev ◽  
Ramiz M. Aliguliyev ◽  
Fargana J Abdullayeva
Keyword(s):  
Social Networking ◽  
Short Term Memory ◽  
Classification Performance ◽  
Automatic Identification ◽  
Test Accuracy ◽  
The Novel ◽  
Occurrence Probability ◽  
Accuracy Test ◽  
Long Short Term Memory ◽  
F Measure

Automatic identification of conversations related to DDoS events in social networking logs helps the organizations act proactively through early detection of negative and positive sentiments in cyberspace. In this article, the authors describe the novel application of a deep learning method to the automatic identification of negative and positive sentiments in large volumes of social networking texts. The authors present classifiers based on Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) to address this problem domain. The improved CNN and LSTM architecture outperform the classification techniques that are common in this domain including classic CNN and classic LSTM in terms of classification performance, which is measured by recall, precision, f-measure, train loss, train accuracy, test loss, and test accuracy. In order to predict the occurrence probability of the DDoS events the next day, the negative and positive sentiments in social networking texts are used. To verify the efficacy of the proposed method experiments is conducted on Twitter data.

scholarly journals On Identifying Hashtags in Disaster Twitter Data

2020 ◽  
Vol 34 (01) ◽  
pp. 498-506 ◽  
Author(s):  
Jishnu Ray Chowdhury ◽  
Cornelia Caragea ◽  
Doina Caragea
Keyword(s):  
Disaster Response ◽  
Short Term Memory ◽  
Automatic Identification ◽  
Short Term ◽  
Term Memory ◽  
Learning Framework ◽  
Twitter Data ◽  
Long Short Term Memory ◽  
Search For Information ◽  
Unique Dataset

Tweet hashtags have the potential to improve the search for information during disaster events. However, there is a large number of disaster-related tweets that do not have any user-provided hashtags. Moreover, only a small number of tweets that contain actionable hashtags are useful for disaster response. To facilitate progress on automatic identification (or extraction) of disaster hashtags for Twitter data, we construct a unique dataset of disaster-related tweets annotated with hashtags useful for filtering actionable information. Using this dataset, we further investigate Long Short-Term Memory-based models within a Multi-Task Learning framework. The best performing model achieves an F1-score as high as $92.22%$. The dataset, code, and other resources are available on Github.1

scholarly journals Usage of social media Twitter in the Intervention of Diabetes Mellitus

2021 ◽  
Vol 1 (3) ◽  
pp. 63-69
Author(s):  
Medit Leonard, ◽  
Bethzy Williams
Keyword(s):  
Social Networking ◽  
Social Networking Sites ◽  
Short Term Memory ◽  
Diabetic Patients ◽  
Regular Expressions ◽  
Short Term ◽  
Twitter Data ◽  
Health Related ◽  
Long Short Term Memory ◽  
Prior Analysis

Social networking sites have been a common forum for exchanging health-related insights and information. This study aims to look at Twitter use in the intervention of diabetes. Specifically, utilising a prior analysis as a reference, we use a revised approach to analyse trends in the existing use of hash-tags, trending hash-tags, and the incidence of diabetes-related tweets. Our findings indicate that the diabetes population on Twitter has grown significantly over time, as well as proof that this community is becoming more capable of spreading diabetes-related health information. An enhanced system for storing, cleaning, and reviewing Twitter data relevant to diabetes, as well as the use of regular expressions to categorise subsets of tweets, are among our computational contributions. To recognise tweets from diabetic patients, we built a model focused on word embedding and long short- term memory.

scholarly journals Online Prediction of Ship Behavior with Automatic Identification System Sensor Data Using Bidirectional Long Short-Term Memory Recurrent Neural Network

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4211 ◽  
Author(s):  
Miao Gao ◽  
Guoyou Shi ◽  
Shuang Li
Keyword(s):  
Neural Network ◽  
Collision Avoidance ◽  
Recurrent Neural Network ◽  
Short Term Memory ◽  
Automatic Identification ◽  
Identification System ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Ship Behavior

The real-time prediction of ship behavior plays an important role in navigation and intelligent collision avoidance systems. This study developed an online real-time ship behavior prediction model by constructing a bidirectional long short-term memory recurrent neural network (BI-LSTM-RNN) that is suitable for automatic identification system (AIS) date and time sequential characteristics, and for online parameter adjustment. The bidirectional structure enhanced the relevance between historical and future data, thus improving the prediction accuracy. Through the “forget gate” of the long short-term memory (LSTM) unit, the common behavioral patterns were remembered and unique behaviors were forgotten, improving the universality of the model. The BI-LSTM-RNN was trained using 2015 AIS data from Tianjin Port waters. The results indicate that the BI-LSTM-RNN effectively predicted the navigational behaviors of ships. This study contributes significantly to the increased efficiency and safety of sea operations. The proposed method could potentially be applied as the predictive foundation for various intelligent systems, including intelligent collision avoidance, vessel route planning, operational efficiency estimation, and anomaly detection systems.

scholarly journals Sentiment on Twitter Data Set using Recurrent Neural Network - Long Short Term Memory

2019 ◽  
Vol 8 (11S) ◽  
pp. 1206-1211
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Short Term Memory ◽  
Short Term ◽  
Data Set ◽  
Term Memory ◽  
Twitter Data ◽  
The People ◽  
Long Short Term Memory

Social media is a combination of different platforms where a huge amount of user-generated data is collected. People from various parts of the country express their opinions, reviews, feedback and marketing strategies through social media such as Twitter, Facebook, Instagram, and YouTube. It is vital to explore, gather data, analyze them and consolidate the people views for better decision making. Sentiment analysis is a natural language processing for information extraction that identifies the user’s views. It is used for extracting reviews and opinions about the satisfaction of products, the events, and people for understanding the current trends of product or user’s behavior. The paper reviews and analyses the existing general approaches and algorithms for sentiment analysis. The proposed system selected to perform sentiment analysis on Twitter data set is Long Short Term Memory [LSTM] and evaluated with Naive Bayes Approach.

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