scholarly journals Social Sentiment Sensor in Twitter for Predicting Cyber-Attacks Using ℓ1 Regularization

2018 ◽  
Author(s):  
Aldo Hernandez-Suarez ◽  
Gabriel Sanchez-Perez ◽  
Karina Toscano-Medina ◽  
Victor Martinez-Hernandez ◽  
Hector Perez-Meana ◽  
...  
Keyword(s):  
Language Processing ◽  
Online Social Networks ◽  
Data Science ◽  
Cyber Attacks ◽  
Machine Learning Techniques ◽  
Security Attacks ◽  
Social Phenomena ◽  
Online Social Media ◽  
Social Events ◽  
ℓ1 Regularization

In recent years, online social media information has been subject of study in several data science fields due to its impact on users as a communication and expression channel. Data~gathered from online platforms such as Twitter has the potential to facilitate research over social phenomena based on sentiment analysis, which usually employs Natural Language Processing and Machine Learning techniques to interpret sentimental tendencies related to users opinions and make predictions about real events. Cyber attacks are not isolated from opinion subjectivity on online social networks. Various security attacks are performed by hacker activists motivated by reactions from polemic social events. In this paper, a methodology for tracking social data that can trigger cyber attacks is developed. Our main contribution lies in the monthly prediction of tweets with content related to security attacks and the incidents detected based on ℓ1 regularization.

scholarly journals Modified Fuzzy Approach to Automatic Classification of Cyber Hate Speech from the Online Social Networks (OSN’s)

2021 ◽  
Vol 35 (2) ◽  
pp. 139-144
Author(s):  
Ashok Kumar Nanduri ◽  
G.L. Sravanthi ◽  
K.V.K.V.L. Pavan Kumar ◽  
Sadhu Ratna Babu ◽  
K.V.S.S. Rama Krishna
Keyword(s):  
Machine Learning ◽  
Online Social Networks ◽  
Hate Speech ◽  
Machine Learning Techniques ◽  
Online Media ◽  
Fuzzy Approach ◽  
Single Class ◽  
Rule Based ◽  
Online Social Media

The extensive use of online media and sharing of data has given considerable benefits to humankind. Sentimental analysis has become the most dynamic and famous application area in current days, which is mainly used in knowing the public's opinion. Most algorithms of machine learning are used as principle methods for sentimental analysis. Even though several methods are available for classification and reviews, all of them belong to a single class of classification which differs among several different classes. No methods are available for the classifying of multi-class instances. Therefore, fuzzy methods are used for classifying the instances depended on multi-class for achieving a clear-cut view by indicating suitable labels to objects during the classification of text. This paper includes the categorization of cyberhate information. If there is a growth in dislike speeches of the online social network may lead to a worse impact amongst social activities, which causes tensions among communication and regional. So, there is the most demand for cyberhate conversation detection automatically through online social media. Generally, an updated process of fuzzy words is designed that includes two stages of training for the classification of cyberhate conversation into 4 forms, race, disability, sexual orientation, and religion. Depended on the types of classification, experiments have been conducted on these four forms by gathering different conversations through online media. Systems based on rules of fuzzy approach have been used. This fuzzy with rule-based is for the classification of features using Machine Learning techniques such as the words that implants for future bag-of-words and extraction methods. In this, the cyberhate conversations are taken from OSN's depended on the attributes defined in a dataset using rule-based fuzzy.

scholarly journals Cybersecurity Data Science: An Overview from Machine Learning Perspective

2020 ◽  
Author(s):  
Iqbal H. Sarker
Keyword(s):  
Machine Learning ◽  
Data Science ◽  
Cyber Attacks ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Research Issues ◽  
Security Incident ◽  
Intelligent Decision Making ◽  
Learning Techniques ◽  
Intelligent Decision

In a computing context, cybersecurity is undergoing massive shifts in technology and its operations in recent days, and data science is driving the change. Extracting security incident patterns or insights from cybersecurity data and building corresponding data-driven model, is the key to make a security system automated and intelligent. To understand and analyze the actual phenomena with data, various scientific methods, machine learning techniques, processes, and systems are used, which is commonly known as data science. In this paper, we focus and briefly discuss cybersecurity data science, where the data is being gathered from relevant cybersecurity sources, and the analytics complement the latest data-driven patterns for providing more effective security solutions. The concept of cybersecurity data science allows making the computing process more actionable and intelligent as compared to traditional ones in the domain of cybersecurity. We then discuss and summarize a number of associated research issues and future directions. Furthermore, we provide a machine learning-based multi-layered framework for the purpose of cybersecurity modeling. Overall, our goal is not only to discuss cybersecurity data science and relevant methods but also to focus the applicability towards data-driven intelligent decision making for protecting the systems from cyber-attacks.

Emotional Tweets Analysis on Social Media with Short Text Classification Using Various Machine Learning Techniques

2020 ◽  
Vol 17 (12) ◽  
pp. 5477-5482
Author(s):  
Shaik Rahamat Basha ◽  
M. Surya Bhupal Rao ◽  
P. Kiran Kumar Reddy ◽  
G. Ravi Kumar
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Language Processing ◽  
Text Processing ◽  
Human Life ◽  
Machine Learning Techniques ◽  
Human Beings ◽  
Short Text ◽  
Online Social Media ◽  
The World

Online Social media are a huge source of regular communication since most people in the world today use these services to stay communicating with each other in their modern lives. Today’s research has been implemented on emotion recognition by message. The majority of the research uses a method of machine learning. In order to extract information from the textual text written by human beings, natural language processing (NLP) techniques were used. The emotion of humans may be expressed when reading or writing a message. Human beings are willing, since human life is filled with a variety of emotions, to feel various emotions. This analysis helps us to realize the use of text processing and text mining methods by social media researchers in order to classify key data themes. Our experiments presented that the two main social networks in the world are conducting text-based mining on Facebook and Twitter. In this proposed study, we categorized the human feelings such as joy, fear, love, anger, surprise, sadness and thankfulness and compared our results using various methods of machine learning.

Comparative Analysis of Machine Learning Techniques Using Predictive Modeling

2020 ◽  
Vol 13 ◽  
Author(s):  
Ritu Khandelwal ◽  
Hemlata Goyal ◽  
Rajveer Singh Shekhawat
Keyword(s):  
Machine Learning ◽  
Comparative Analysis ◽  
Data Science ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Future Trends ◽  
Data Set ◽  
Learning Stage ◽  
Learning Techniques ◽  
Different Types

Introduction: Machine learning is an intelligent technology that works as a bridge between businesses and data science. With the involvement of data science, the business goal focuses on findings to get valuable insights on available data. The large part of Indian Cinema is Bollywood which is a multi-million dollar industry. This paper attempts to predict whether the upcoming Bollywood Movie would be Blockbuster, Superhit, Hit, Average or Flop. For this Machine Learning techniques (classification and prediction) will be applied. To make classifier or prediction model first step is the learning stage in which we need to give the training data set to train the model by applying some technique or algorithm and after that different rules are generated which helps to make a model and predict future trends in different types of organizations. Methods: All the techniques related to classification and Prediction such as Support Vector Machine(SVM), Random Forest, Decision Tree, Naïve Bayes, Logistic Regression, Adaboost, and KNN will be applied and try to find out efficient and effective results. All these functionalities can be applied with GUI Based workflows available with various categories such as data, Visualize, Model, and Evaluate. Result: To make classifier or prediction model first step is learning stage in which we need to give the training data set to train the model by applying some technique or algorithm and after that different rules are generated which helps to make a model and predict future trends in different types of organizations Conclusion: This paper focuses on Comparative Analysis that would be performed based on different parameters such as Accuracy, Confusion Matrix to identify the best possible model for predicting the movie Success. By using Advertisement Propaganda, they can plan for the best time to release the movie according to the predicted success rate to gain higher benefits. Discussion: Data Mining is the process of discovering different patterns from large data sets and from that various relationships are also discovered to solve various problems that come in business and helps to predict the forthcoming trends. This Prediction can help Production Houses for Advertisement Propaganda and also they can plan their costs and by assuring these factors they can make the movie more profitable.

Computational Methods for Socio-Computer Interaction

2018 ◽  
Author(s):  
Wai-Tat Fu ◽  
Mingkun Gao ◽  
Hyo Jin Do
Keyword(s):  
Computational Methods ◽  
User Interfaces ◽  
Social Processes ◽  
Computational Techniques ◽  
Special Focus ◽  
Design Features ◽  
Social Phenomena ◽  
Online Social Media ◽  
Social Opinions ◽  
Computer Interaction

From the Arab Spring to presidential elections, various forms of online social media, forums, and networking platforms have been playing increasing significant roles in our societies. These emerging socio-computer interactions demand new methods of understanding how various design features of online tools may moderate the percolation of information and gradually shape social opinions, influence social choices, and moderate collective action. This chapter starts with a review of the literature on the different ways technologies impact social phenomena, with a special focus on theories that characterize how social processes are moderated by various design features of user interfaces. It then reviews different theory-based computational methods derived from these theories to study socio-computer interaction at various levels. Specific examples of computational techniques are reviewed to illustrate how they can be useful for influencing social processes for various purposes. The chapter ends with how future technologies should be designed to improve socio-computer interaction.

scholarly journals Twitter Sentiment Analysis towards COVID-19 Vaccines in the Philippines Using Naïve Bayes

Information ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 204
Author(s):  
Charlyn Villavicencio ◽  
Julio Jerison Macrohon ◽  
X. Alphonse Inbaraj ◽  
Jyh-Horng Jeng ◽  
Jer-Guang Hsieh
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
Data Science ◽  
Naive Bayes ◽  
The Philippines ◽  
Naïve Bayes ◽  
Social Networking Site ◽  
Bayes Model ◽  
The Government ◽  
Processing Techniques

A year into the COVID-19 pandemic and one of the longest recorded lockdowns in the world, the Philippines received its first delivery of COVID-19 vaccines on 1 March 2021 through WHO’s COVAX initiative. A month into inoculation of all frontline health professionals and other priority groups, the authors of this study gathered data on the sentiment of Filipinos regarding the Philippine government’s efforts using the social networking site Twitter. Natural language processing techniques were applied to understand the general sentiment, which can help the government in analyzing their response. The sentiments were annotated and trained using the Naïve Bayes model to classify English and Filipino language tweets into positive, neutral, and negative polarities through the RapidMiner data science software. The results yielded an 81.77% accuracy, which outweighs the accuracy of recent sentiment analysis studies using Twitter data from the Philippines.

scholarly journals A Data Science Analysis of Academic Staff Workload Profiles in Spanish Universities: Gender Gap Laid Bare

2021 ◽  
Vol 11 (7) ◽  
pp. 317
Author(s):  
Ismael Cabero ◽  
Irene Epifanio
Keyword(s):  
Gender Gap ◽  
Data Science ◽  
Academic Staff ◽  
Machine Learning Techniques ◽  
Academic Disciplines ◽  
University Environment ◽  
Learning Techniques ◽  
Spanish Universities ◽  
New Perspective

This paper presents a snapshot of the distribution of time that Spanish academic staff spend on different tasks. We carry out a statistical exploratory study by analyzing the responses provided in a survey of 703 Spanish academic staff in order to draw a clear picture of the current situation. This analysis considers many factors, including primarily gender, academic ranks, age, and academic disciplines. The tasks considered are divided into smaller activities, which allows us to discover hidden patterns. Tasks are not only restricted to the academic world, but also relate to domestic chores. We address this problem from a totally new perspective by using machine learning techniques, such as cluster analysis. In order to make important decisions, policymakers must know how academic staff spend their time, especially now that legal modifications are planned for the Spanish university environment. In terms of the time spent on quality of teaching and caring tasks, we expose huge gender gaps. Non-recognized overtime is very frequent.

scholarly journals A Survey on Twitter Sentimental Analysis with Machine Learning Techniques

2018 ◽  
Vol 7 (2.32) ◽  
pp. 462
Author(s):  
G Krishna Chaitanya ◽  
Dinesh Reddy Meka ◽  
Vakalapudi Surya Vamsi ◽  
M V S Ravi Karthik
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Machine Learning Techniques ◽  
Buying Behavior ◽  
Online Retail ◽  
User Reviews ◽  
Business Plans ◽  
Learning Techniques ◽  
User Data

Sentiment or emotion behind a tweet from Twitter or a post from Facebook can help us answer what opinions or feedback a person has. With the advent of growing user-generated blogs, posts and reviews across various social media and online retails, calls for an understanding of these afore mentioned user data acts as a catalyst in building Recommender systems and drive business plans. User reviews on online retail stores influence buying behavior of customers and thus complements the ever-growing need of sentiment analysis. Machine Learning helps us to read between the lines of tweets by proving us with various algorithms like Naïve Bayes, SVM, etc. Sentiment Analysis uses Machine Learning and Natural Language Processing (NLP) to extract, classify and analyze tweets for sentiments (emotions). There are various packages and frameworks in R and Python that aid in Sentiment Analysis or Text Mining in general. 

scholarly journals Natural Language Processing by Enhanced Honey Encryption Technique

2019 ◽  
Vol 8 (12S) ◽  
pp. 159-163
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Cyber Attacks ◽  
Binary Form ◽  
Brute Force ◽  
Natural Languages ◽  
Cipher Text ◽  
The Right ◽  
Binary Strings

Traditional encryption systems and techniques have always been vulnerable to brute force cyber-attacks. This is due to bytes encoding of characters utf8 also known as ASCII characters. Therefore, an opponent who intercepts a cipher text and attempts to decrypt the signal by applying brute force with a faulty pass key can detect some of the decrypted signals by employing a mixture of symbols that are not uniformly dispersed and contain no meaningful significance. Honey encoding technique is suggested to curb this classical authentication weakness by developing cipher-texts that provide correct and evenly dispersed but untrue plaintexts after decryption with a false key. This technique is only suitable for passkeys and PINs. Its adjustment in order to promote the encoding of the texts of natural languages such as electronic mails, records generated by man, still remained an open-end drawback. Prevailing proposed schemes to expand the encryption of natural language messages schedule exposes fragments of the plaintext embedded with coded data, thus they are more prone to cipher text attacks. In this paper, amending honey encoded system is proposed to promote natural language message encryption. The main aim was to create a framework that would encrypt a signal fully in binary form. As an end result, most binary strings semantically generate the right texts to trick an opponent who tries to decipher an error key in the cipher text. The security of the suggested system is assessed..

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