Online Social Network Security: A Comparative Review Using Machine Learning and Deep Learning

2021 ◽  
Author(s):  
Chanchal Kumar ◽  
Taran Singh Bharati ◽  
Shiv Prakash
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Social Network ◽  
Network Security ◽  
Online Social Network ◽  
Comparative Review

scholarly journals Adaptive Anomaly Detection Framework Model Objects in Cyberspace

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Hasan Alkahtani ◽  
Theyazn H. H. Aldhyani ◽  
Mohammed Al-Yaari
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Network Security ◽  
Anomaly Detection ◽  
Denial Of Service ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Ddos Attacks ◽  
Framework Model

Telecommunication has registered strong and rapid growth in the past decade. Accordingly, the monitoring of computers and networks is too complicated for network administrators. Hence, network security represents one of the biggest serious challenges that can be faced by network security communities. Taking into consideration the fact that e-banking, e-commerce, and business data will be shared on the computer network, these data may face a threat from intrusion. The purpose of this research is to propose a methodology that will lead to a high level and sustainable protection against cyberattacks. In particular, an adaptive anomaly detection framework model was developed using deep and machine learning algorithms to manage automatically-configured application-level firewalls. The standard network datasets were used to evaluate the proposed model which is designed for improving the cybersecurity system. The deep learning based on Long-Short Term Memory Recurrent Neural Network (LSTM-RNN) and machine learning algorithms namely Support Vector Machine (SVM), K-Nearest Neighbor (K-NN) algorithms were implemented to classify the Denial-of-Service attack (DoS) and Distributed Denial-of-Service (DDoS) attacks. The information gain method was applied to select the relevant features from the network dataset. These network features were significant to improve the classification algorithm. The system was used to classify DoS and DDoS attacks in four stand datasets namely KDD cup 199, NSL-KDD, ISCX, and ICI-ID2017. The empirical results indicate that the deep learning based on the LSTM-RNN algorithm has obtained the highest accuracy. The proposed system based on the LSTM-RNN algorithm produced the highest testing accuracy rate of 99.51% and 99.91% with respect to KDD Cup’99, NSL-KDD, ISCX, and ICI-Id2017 datasets, respectively. A comparative result analysis between the machine learning algorithms, namely SVM and KNN, and the deep learning algorithms based on the LSTM-RNN model is presented. Finally, it is concluded that the LSTM-RNN model is efficient and effective to improve the cybersecurity system for detecting anomaly-based cybersecurity.

scholarly journals Predicting depression using deep learning and ensemble algorithms on raw twitter data

2020 ◽  
Vol 10 (4) ◽  
pp. 3751
Author(s):  
Nisha P. Shetty ◽  
Balachandra Muniyal ◽  
Arshia Anand ◽  
Sushant Kumar ◽  
Sushant Prabhu
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Social Network ◽  
Mental Disorder ◽  
Virtual World ◽  
Common Mental Disorder ◽  
Data Set ◽  
Machine Learning Classifiers ◽  
Twitter Data ◽  
Ensemble Algorithms

Social network and microblogging sites such as Twitter are widespread amongst all generations nowadays where people connect and share their feelings, emotions, pursuits etc. Depression, one of the most common mental disorder, is an acute state of sadness where person loses interest in all activities. If not treated immediately this can result in dire consequences such as death. In this era of virtual world, people are more comfortable in expressing their emotions in such sites as they have become a part and parcel of everyday lives. The research put forth thus, employs machine learning classifiers on the twitter data set to detect if a person’s tweet indicates any sign of depression or not.

Filtering Dirty Words in Online Social Network by Applying Automated Filtering System

2014 ◽  
Vol 573 ◽  
pp. 560-564
Author(s):  
P. Kumari Bala ◽  
D. Jemi Florinabel ◽  
S. Sivasakthi
Keyword(s):  
Machine Learning ◽  
Social Network ◽  
Online Social Networks ◽  
Text Categorization ◽  
Online Social Network ◽  
Project Work ◽  
Rule Based ◽  
Short Text ◽  
Filter Algorithms ◽  
Content Based Filtering

The aim of the project work is automatically to filter the dirty words from other users without displaying to the profile owner. In Online Social Network may have possibilities of posting some dirty messages so it need to filter without displaying to owner. It has achieved by using Rule based Filtering System. The Rule Based Filtering System allows users customize to filter the noisy or dirty words by applying some filtering Criteria. It exploits Machine Learning (ML). Machine Learning is a text categorization techniques to specify some categories for assign the short text dirty words based on their content. The content-based filtering on messages posted on user space has specified the additional challenges to be given the short length of these messages. Online social networks not only make it easier for users to share their opinions with each other, but also serve as a platform for developing filter algorithms.

scholarly journals Positioning of Trending Topics and Analyzing the Tweets in Social Network using Deep Learning

2020 ◽  
Vol 9 (5) ◽  
pp. 1308-1312
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Social Network ◽  
Web Based ◽  
The Public ◽  
Trending Topics ◽  
Information Streams ◽  
Digital Time ◽  
Scientific Classification ◽  
Significant Bearing

In the Digital time, Twitter has developed to turn into a significant web based life to get to quick data about unique themes that are slanting in the public eye. In later, identification of topical substance utilizing classifiers on Twitter can sum up well past the enormous volume of prepared information. Since access to Twitter information is holed up behind a restricted pursuit API, normal clients can't have any significant bearing these classifiers legitimately to the Twitter unfiltered information streams. Or maybe, applications must pick what substance to recuperate through the pursuit API before sifting that content with topical classifiers. In this manner, other than these lines, it is basic to scrutinize the Twitter API near with the proposed topical classifier in a manner that limits the measure of adversely arranged information recovered. In this paper, we propose a succession of inquiry enhancement strategies utilizing Machine learning with the assistance of CNN that sum up thoughts of the most extreme inclusion issue to discover the subclass of question articulations inside as far as possible. It is utilized to cover most of the topically pertinent tweets without relinquishing accuracy. Among numerous bits of knowledge, proposed techniques fundamentally outflank the scientific classification dependent on the tweets and arrange the best of the tweets and pessimistic tweets in Twitter.

An intrusion detection system for health-care system using machine and deep learning

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sagar Pande ◽  
Aditya Khamparia ◽  
Deepak Gupta
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Deep Learning ◽  
Network Security ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Machine Learning Techniques ◽  
Content Type ◽  
Learning Techniques

Purpose One of the important key components of health care–based system is a reliable intrusion detection system. Traditional techniques are not adequate to handle complex data. Also, the diversified intrusion techniques cannot meet current network requirements. Not only the data is getting increased but also the attacks are increasing very rapidly. Deep learning and machine learning techniques are very trending in the area of research in the area of network security. A lot of work has been done in this area by still evolutionary algorithms along with machine learning is very rarely explored. The purpose of this study is to provide novel deep learning framework for the detection of attacks. Design/methodology/approach In this paper, novel deep learning is the framework is proposed for the detection of attacks. Also, a comparison of machine learning and deep learning algorithms is provided. Findings The obtained results are more than 99% for both the data sets. Research limitations/implications The diversified intrusion techniques cannot meet current network requirements. Practical implications The data is getting increased but also the attacks are increasing very rapidly. Social implications Deep learning and machine learning techniques are very trending in the area of research in the area of network security. Originality/value Novel deep learning is the framework is proposed for the detection of attacks.

scholarly journals Bots Recognition in Social Networks Using the Random Forest Algorithm

2019 ◽  
pp. 24-41
Author(s):  
M. G. Khachatrian ◽  
P. G. Klyucharev
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Network ◽  
Random Forest ◽  
Online Social Networks ◽  
Learning Algorithm ◽  
Online Social Network ◽  
Machine Learning Algorithms ◽  
Random Forest Algorithm ◽  
Twitter Account

Online social networks are of essence, as a tool for communication, for millions of people in their real world. However, online social networks also serve an arena of information war. One tool for infowar is bots, which are thought of as software designed to simulate the real user’s behaviour in online social networks.The paper objective is to develop a model for recognition of bots in online social networks. To develop this model, a machine-learning algorithm “Random Forest” was used. Since implementation of machine-learning algorithms requires the maximum data amount, the Twitter online social network was used to solve the problem of bot recognition. This online social network is regularly used in many studies on the recognition of bots.For learning and testing the Random Forest algorithm, a Twitter account dataset was used, which involved above 3,000 users and over 6,000 bots. While learning and testing the Random Forest algorithm, the optimal hyper-parameters of the algorithm were determined at which the highest value of the F1 metric was reached. As a programming language that allowed the above actions to be implemented, was chosen Python, which is frequently used in solving problems related to machine learning.To compare the developed model with the other authors’ models, testing was based on the two Twitter account datasets, which involved as many as half of bots and half of real users. As a result of testing on these datasets, F1-metrics of 0.973 and 0.923 were obtained. The obtained F1-metric values  are quite high as compared with the papers of other authors.As a result, in this paper a model of high accuracy rates was obtained that can recognize bots in the Twitter online social network.

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