scholarly journals An efficient hybrid system for anomaly detection in social networks

Cybersecurity ◽  
2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Md. Shafiur Rahman ◽  
Sajal Halder ◽  
Md. Ashraf Uddin ◽  
Uzzal Kumar Acharjee
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Network ◽  
Anomaly Detection ◽  
Research Area ◽  
Machine Learning Algorithms ◽  
Security And Privacy ◽  
Support Vector ◽  
The Social ◽  
Wide Range

AbstractAnomaly detection has been an essential and dynamic research area in the data mining. A wide range of applications including different social medias have adopted different state-of-the-art methods to identify anomaly for ensuring user’s security and privacy. The social network refers to a forum used by different groups of people to express their thoughts, communicate with each other, and share the content needed. This social networks also facilitate abnormal activities, spread fake news, rumours, misinformation, unsolicited messages, and propaganda post malicious links. Therefore, detection of abnormalities is one of the important data analysis activities for the identification of normal or abnormal users on the social networks. In this paper, we have developed a hybrid anomaly detection method named DT-SVMNB that cascades several machine learning algorithms including decision tree (C5.0), Support Vector Machine (SVM) and Naïve Bayesian classifier (NBC) for classifying normal and abnormal users in social networks. We have extracted a list of unique features derived from users’ profile and contents. Using two kinds of dataset with the selected features, the proposed machine learning model called DT-SVMNB is trained. Our model classifies users as depressed one or suicidal one in the social network. We have conducted an experiment of our model using synthetic and real datasets from social network. The performance analysis demonstrates around 98% accuracy which proves the effectiveness and efficiency of our proposed system.

scholarly journals Towards Inferring Influential Facebook Users

Computers ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 62
Author(s):  
Suleiman Ali Alsaif ◽  
Adel Hidri ◽  
Minyar Sassi Hidri
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Network ◽  
Exploratory Analysis ◽  
Machine Learning Algorithms ◽  
Statistical Tools ◽  
Social Actors ◽  
Graph Modeling ◽  
The Social ◽  
Facebook Pages

Because of the complexity of the actors and the relationships between them, social networks are always represented by graphs. This structure makes it possible to analyze the effectiveness of the network for the social actors who are there. This work presents a social network analysis approach that focused on processing Facebook pages and users who react to posts to infer influential people. In our study, we are particularly interested in studying the relationships between the posts of the page, and the reactions of fans (users) towards these posts. The topics covered include data crawling, graph modeling, and exploratory analysis using statistical tools and machine learning algorithms. We seek to detect influential people in the sense that the influence of a Facebook user lies in their ability to transmit and disseminate information. Once determined, these users have an impact on business for a specific brand. The proposed exploratory analysis has shown that the network structure and its properties have important implications for the outcome of interest.

scholarly journals Detection of Fake Profiles on Twitter Using Hybrid SVM Algorithm

2021 ◽  
Vol 309 ◽  
pp. 01046
Author(s):  
Sarangam Kodati ◽  
Kumbala Pradeep Reddy ◽  
Sreenivas Mekala ◽  
PL Srinivasa Murthy ◽  
P Chandra Sekhar Reddy
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Online Social Networks ◽  
Personal Data ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Reduction Techniques ◽  
Svm Algorithm ◽  
The Social ◽  
False News

Establishing and management of social relationships among huge amount of users has been provided by the emerging communication medium called online social networks (OSNs). The attackers have attracted because of the rapid increasing of OSNs and the large amount of its subscriber’s personal data. Then they pretend to spread malicious activities, share false news and even stolen personal data. Twitter is one of the biggest networking platforms of micro blogging social networks in which daily more than half a billion tweets are posted most of that are malware activities. Analyze, who are encouraging threats in social networks is need to classify the social networks profiles of the users. Traditionally, there are different classification methods for detecting the fake profiles on the social networks that needed to improve their accuracy rate of classification. Thus machine learning algorithms are focused in this paper. Therefore detection of fake profiles on twitter using hybrid Support Vector Machine (SVM) algorithm is proposed in this paper. The machine learning based hybrid SVM algorithm is used in this for classification of fake and genuine profiles of Twitter accounts and applied the dimension reduction techniques, feature selection and bots. Less number of features is used in the proposed hybrid SVM algorithm and 98% of the accounts are correctly classified with proposed algorithm.

scholarly journals Preserving Privacy in Multimedia Social Networks Using Machine Learning Anomaly Detection

2020 ◽  
Vol 2020 ◽  
pp. 1-14 ◽  
Author(s):  
Randa Aljably ◽  
Yuan Tian ◽  
Mznah Al-Rodhaan
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Access Control ◽  
Anomaly Detection ◽  
Privacy Preservation ◽  
Support Vector ◽  
Detection Techniques ◽  
Access Control Models ◽  
Control Models ◽  
Multimedia Social Networks

Nowadays, user’s privacy is a critical matter in multimedia social networks. However, traditional machine learning anomaly detection techniques that rely on user’s log files and behavioral patterns are not sufficient to preserve it. Hence, the social network security should have multiple security measures to take into account additional information to protect user’s data. More precisely, access control models could complement machine learning algorithms in the process of privacy preservation. The models could use further information derived from the user’s profiles to detect anomalous users. In this paper, we implement a privacy preservation algorithm that incorporates supervised and unsupervised machine learning anomaly detection techniques with access control models. Due to the rich and fine-grained policies, our control model continuously updates the list of attributes used to classify users. It has been successfully tested on real datasets, with over 95% accuracy using Bayesian classifier, and 95.53% on receiver operating characteristic curve using deep neural networks and long short-term memory recurrent neural network classifiers. Experimental results show that this approach outperforms other detection techniques such as support vector machine, isolation forest, principal component analysis, and Kolmogorov–Smirnov test.

scholarly journals MACHINE LEARNING METHODS IN MONITORING OPERATING BEHAVIOUR OF MARINE TWO-STROKE DIESEL ENGINE

Transport ◽  
2020 ◽  
Vol 35 (5) ◽  
pp. 462-473
Author(s):  
Aleksandar Vorkapić ◽  
Radoslav Radonja ◽  
Karlo Babić ◽  
Sanda Martinčić-Ipšić
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Fuel Consumption ◽  
Performance Monitoring ◽  
Absolute Error ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Operating Parameters ◽  
Detection Model ◽  
Modelling Framework

The aim of this article is to enhance performance monitoring of a two-stroke electronically controlled ship propulsion engine on the operating envelope. This is achieved by setting up a machine learning model capable of monitoring influential operating parameters and predicting the fuel consumption. Model is tested with different machine learning algorithms, namely linear regression, multilayer perceptron, Support Vector Machines (SVM) and Random Forests (RF). Upon verification of modelling framework and analysing the results in order to improve the prediction accuracy, the best algorithm is selected based on standard evaluation metrics, i.e. Root Mean Square Error (RMSE) and Relative Absolute Error (RAE). Experimental results show that, by taking an adequate combination and processing of relevant sensory data, SVM exhibit the lowest RMSE 7.1032 and RAE 0.5313%. RF achieve the lowest RMSE 22.6137 and RAE 3.8545% in a setting when minimal number of input variables is considered, i.e. cylinder indicated pressures and propulsion engine revolutions. Further, article deals with the detection of anomalies of operating parameters, which enables the evaluation of the propulsion engine condition and the early identification of failures and deterioration. Such a time-dependent, self-adopting anomaly detection model can be used for comparison with the initial condition recorded during the test and sea run or after survey and docking. Finally, we propose a unified model structure, incorporating fuel consumption prediction and anomaly detection model with on-board decision-making process regarding navigation and maintenance.

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 Vehicle Price Prediction using SVM Techniques

2020 ◽  
Vol 9 (8) ◽  
pp. 398-401
Keyword(s):  
Machine Learning ◽  
Research Area ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Data Set ◽  
Network Support ◽  
Java Application ◽  
Learning Techniques ◽  
The Individual

The prediction of price for a vehicle has been more popular in research area, and it needs predominant effort and information about the experts of this particular field. The number of different attributes is measured and also it has been considerable to predict the result in more reliable and accurate. To find the price of used vehicles a well defined model has been developed with the help of three machine learning techniques such as Artificial Neural Network, Support Vector Machine and Random Forest. These techniques were used not on the individual items but for the whole group of data items. This data group has been taken from some web portal and that same has been used for the prediction. The data must be collected using web scraper that was written in PHP programming language. Distinct machine learning algorithms of varying performances had been compared to get the best result of the given data set. The final prediction model was integrated into Java application

scholarly journals Election Control in Social Networks via Edge Addition or Removal

2020 ◽  
Vol 34 (02) ◽  
pp. 1878-1885
Author(s):  
Matteo Castiglioni ◽  
Diodato Ferraioli ◽  
Nicola Gatti
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Positive Result ◽  
Optimization Problem ◽  
Optimal Solution ◽  
The Social ◽  
Wide Range ◽  
Local Modification ◽  
Edge Addition ◽  
Over Time

We focus on the scenario in which messages pro and/or against one or multiple candidates are spread through a social network in order to affect the votes of the receivers. Several results are known in the literature when the manipulator can make seeding by buying influencers. In this paper, instead, we assume the set of influencers and their messages to be given, and we ask whether a manipulator (e.g., the platform) can alter the outcome of the election by adding or removing edges in the social network. We study a wide range of cases distinguishing for the number of candidates or for the kind of messages spread over the network. We provide a positive result, showing that, except for trivial cases, manipulation is not affordable, the optimization problem being hard even if the manipulator has an unlimited budget (i.e., he can add or remove as many edges as desired). Furthermore, we prove that our hardness results still hold in a reoptimization variant, where the manipulator already knows an optimal solution to the problem and needs to compute a new solution once a local modification occurs (e.g., in bandit scenarios where estimations related to random variables change over time).

scholarly journals Identification of user’s credibility on twitter social networks

2021 ◽  
Vol 24 (1) ◽  
pp. 554
Author(s):  
Faraz Ahmad ◽  
S. A. M. Rizvi
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Nearest Neighbours ◽  
The Social ◽  
Class Labelling ◽  
Social Media Platforms ◽  
Media Platform ◽  
The Given

<p>Twitter is one of the most influential social media platforms, facilitates the spreading of information in the form of text, images, and videos. However, the credibility of posted content is still trailed by an interrogation mark. Introduction: In this paper, a model has been developed for finding the user’s credibility based on the tweets which they had posted on Twitter social networks. The model consists of machine learning algorithms that assist not only in categorizing the tweets into credibility classes but also helps in finding user’s credibility ratings on the social media platform. Methods and results: The dataset and associated features of 100,000 tweets were extracted and pre-processed. Furthermore, the credibility class labelling of tweets was performed using four different human annotators. The meaning cloud and natural language understanding platforms were used for calculating the polarity, sentiment, and emotions score. The K-Means algorithm was applied for finding the clusters of tweets based on features set, whereas, random forest, support vector machine, naïve Bayes, K-nearest-neighbours (KNN), J48 decision tree, and multilayer perceptron were used for classifying the tweets into credibility classes. A significant level of accuracy, precision, and recall was provided by all the classifiers for all the given credibility classes.</p>

BMADSN: Big data multi-community anomaly detection in social networks

2019 ◽  
pp. 002072091989106 ◽  
Author(s):  
Manjunatha HC ◽  
Mohanasundaram R
Keyword(s):  
Social Networks ◽  
Big Data ◽  
Social Network ◽  
Anomaly Detection ◽  
Research Area ◽  
Detection Techniques ◽  
Graph Metrics ◽  
The People ◽  
Illegal Activities ◽  
The Given

In today's world, most of the people are using social networks for day-to-day activities. The most frequently used social sites are Facebook, Twitter, Google+, etc. These popular social networks are used by some of the users for abnormal or illegal activities. It is very important and necessary to identify and avoid such illegal activities without harming anyone in the society. In recent decades, social networks are becoming a popular research area for most researchers. Many authors are doing research on social network datasets and proposing various anomaly detection mechanisms to identify anomalous activities in both static and dynamic growing social networks. Various anomaly detection techniques are proposed by the authors to investigate malicious activities in social networks. In general, the process of identifying anomaly activities of the users in the given dataset is called anomaly detection. The anomaly detection in social networks is the process of investigating whether the users of the given social networks are involved in illegal activities or not. In this work, we proposed a most elegant approach to identify the anomalous or outlier users in the given social network. The proposed approach is considering the users participated in multiple communities of social networks. The designed algorithms are implemented and tested in a big data environment three node cluster using open source Hadoop ecosystem tools. Algorithm1 is used to investigate the nodes/users who participated in multiple communities of the given social network’s dataset. Algorithm2 takes the set of users participated in multiple communities and apply graph metrics such as degree and community score to predict the users involved in the anomalous activity.

scholarly journals Child Behavioral Analysis: Machine Learning based Investigation for Autism Screening and Early Diagnosis

2021 ◽  
Vol 13 (2) ◽  
pp. 1199-1208
Author(s):  
N. Ajaypradeep ◽  
Dr.R. Sasikala
Keyword(s):  
Machine Learning ◽  
Early Diagnosis ◽  
Cross Validation ◽  
Parameter Tuning ◽  
Autism Spectrum ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Therapeutic Practice ◽  
Nearest Neighbours ◽  
Wide Range

Autism is a developmental disorder which affects cognition, social and behavioural functionalities of a person. When a person is affected by autism spectrum disorder, he/she will exhibit peculiar behaviours and those symptoms initiate from that patient’s childhood. Early diagnosis of autism is an important and challenging task. Behavioural analysis a well known therapeutic practice can be adopted for earlier diagnosis of autism. Machine learning is a computational methodology, which can be applied to a wide range of applications in-order to obtain efficient outputs. At present machine learning is especially applied in medical applications such as disease prediction. In our study we evaluated various machine learning algorithms [(Naive bayes (NB), Support Vector Machines (SVM) and k-Nearest Neighbours (KNN)] with “k-fold” based cross validation for 3 datasets retrieved from the UCI repository. Additionally we validated the effective accuracy of the estimated results using a clustered cross validation strategy. The process of employing the clustered cross validation scrutinises the parameters which contributes more importance in the dataset. The strategy induces hyper parameter tuning which yields trusted results as it involves double validation. On application of the clustered cross validation for a SVM based model, we obtained an accuracy of 99.6% accuracy for autism child dataset.

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