scholarly journals An Empirical Study on Fake News Detection System using Deep and Machine Learning Ensemble Techniques

2021 ◽  
Vol 12 (12) ◽  
Author(s):  
T V Divya ◽  
Barnali Gupta Banik
Keyword(s):  
Machine Learning ◽  
Empirical Study ◽  
Detection System ◽  
Fake News ◽  
Ensemble Techniques

Tebyan: Fake News Detection System (Preprint)

2021 ◽  
Author(s):  
Lamya Alderywsh ◽  
Aseel Aldawood ◽  
Ashwag Alasmari ◽  
Farah Aldeijy ◽  
Ghadah Alqubisy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Arab World ◽  
Detection System ◽  
Learning Algorithms ◽  
Performance Measure ◽  
Machine Learning Algorithms ◽  
Svm Classifier ◽  
Fake News ◽  
Typical Type ◽  
Performance Results

BACKGROUND There is a serious threat from fake news spreading in technologically advanced societies, including those in the Arab world, via deceptive machine-generated text. In the last decade, Arabic fake news identification has gained increased attention, and numerous detection approaches have revealed some ability to find fake news throughout various data sources. Nevertheless, many existing approaches overlook recent advancements in fake news detection, explicitly to incorporate machine learning algorithms system. OBJECTIVE Tebyan project aims to address the problem of fake news by developing a fake news detection system that employs machine learning algorithms to detect whether the news is fake or real in the context of Arab world. METHODS The project went through numerous phases using an iterative methodology to develop the system. This study analysis incorporated numerous stages using an iterative method to develop the system of misinformation and contextualize fake news regarding society's information. It consists of implementing the machine learning algorithms system using Python to collect genuine and fake news datasets. The study also assesses how information-exchanging behaviors can minimize and find the optimal source of authentication of the emergent news through system testing approaches. RESULTS The study revealed that the main deliverable of this project is the Tebyan system in the community, which allows the user to ensure the credibility of news in Arabic newspapers. It showed that the SVM classifier, on average, exhibited the highest performance results, resulting in 90% in every performance measure of sources. Moreover, the results indicate the second-best algorithm is the linear SVC since it resulted in 90% in performance measure with the societies' typical type of fake information. CONCLUSIONS The study concludes that conducting a system with machine learning algorithms using Python programming language allows the rapid measures of the users' perception to comment and rate the credibility result and subscribing to news email services.

scholarly journals Fakebuster: Fake News Detection System using Logistic Regression Technique In Machine Learning

2019 ◽  
Vol 9 (1) ◽  
pp. 2407-2410
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Web Application ◽  
Detection System ◽  
Model Performance ◽  
Machine Learning Algorithms ◽  
Fake News ◽  
Potential Threat ◽  
News Content ◽  
Detection Model

The uncontrollable spread of fake news through the net is irresistible in this globalization era. Fake news dissemination cannot be tolerated as the bad impacts of it to the society is really worrying. Furthermore, this will lead to more significant problems and potential threat such as confusion, misconceptions, slandering and luring users to share provocative lies made from fabricated news through their social media to occur. Within Malaysia context, there is lack in platform for fake news detection in Malay language articles and most of Malaysians received news through their social messaging applications. Fake news can be certainly solved by the aid of artificial intelligence which includes machine learning algorithms. The objective of this project is to propose a fake news detection model using Logistic Regression, to evaluate the performance of Logistic Regression as fake news detection model and to develop a web application that allows entry of a news content or news URL. In this study, Logistic Regression was applied in detecting fake news. Model development methodology is referenced and followed in this project. Based on existing studies, Logistic Regression showed a good performance in classification task. In addition, stancedetection approach is added to improve the accuracy of the model performance. Based on analysis made, this model within stance detection approach yields an excellent accuracy using TF-IDF feature in constructing this fake news model. This model is then integrated with web service that accepts input either news URL or news content in text which is then checked for its truth level through “FAKEBUSTER” application.

scholarly journals APPLICATION OF MACHINE LEARNING FOR INTRUSION DETECTION SYSTEM

2021 ◽  
Vol 9 (1) ◽  
pp. 314-323
Author(s):  
Nitin Pise
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Cyber Attacks ◽  
Classification Algorithms ◽  
Review Section ◽  
Work From Home ◽  
Ensemble Techniques ◽  
Feature Selection Techniques

Due to Covid-19 pandemic, the most of the organizations have permitted their employees to work from home. Also, it is every essential to have security at the highest level so that information will flow in the safe and trusted environment between the different organizations. There is always threat of misuses and different intrusions for communication of the data securely over the internet. As more and more people are using online transactions for the different purposes, it is found that the cyber attackers have become more active. Three in four organizations have faced the different cyber-attacks in the year 2020. So, the detection of intrusion is very important. The paper introduces the intrusion detection system and describes its classification. It discusses the different contributions to the literature in literature review section. The paper discusses the application of the different feature selection techniques for reducing the number of features, use of the different classification algorithms for the intrusion detection and it shows how machine learning is used effectively. KDD99 benchmark dataset was used to implement and measure the performance of the system and good results are obtained and the performance of the different classifier algorithms was compared. Tree based classifiers such as J48 and ensemble techniques such as random forest give the best performance on KDD99 dataset.

scholarly journals An Effecient Fake News Detection System Using Machine Learning

2019 ◽  
Vol 8 (10) ◽  
pp. 3125-3129 ◽  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Language Processing ◽  
Negative Impact ◽  
Detection System ◽  
Vital Role ◽  
Machine Learning Algorithms ◽  
Easy Access ◽  
Fake News ◽  
K Nearest Neighbors

Social media plays a major role in several things in our life. Social media helps all of us to find some important news with low price. It also provides easy access in less time. But sometimes social media gives a chance for the fast-spreading of fake news. So there is a possibility that less quality news with false information is spread through the social media. This shows a negative impact on the number of people. Sometimes it may impact society also. So, detection of fake news has vast importance. Machine learning algorithms play a vital role in fake news detection; Especially NLP (Natural Language Processing) algorithms are very useful for detecting the fake news. In this paper, we employed machine learning classifiers SVM, K-Nearest Neighbors, Decision tree, Random forest. By using these classifiers we successfully build a model to detect fake news from the given dataset. Python language was used for experiments.

Inappropriate thermal image detection system using machine learning

2020 ◽  
Vol 14 (2) ◽  
pp. 102-111
Author(s):  
S. W. Kwon ◽  
I. S. Song ◽  
S. W. Lee ◽  
J. S. Lee ◽  
J. H. Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Detection System ◽  
Thermal Image ◽  
Image Detection

Anomaly Activities Detection System in Critical Water SCADA Infrastructure Using Machine Learning Techniques

2019 ◽  
Vol 28 (1) ◽  
pp. 343-384 ◽  
Author(s):  
Gamal Eldin I. Selim ◽  
EZZ El-Din Hemdan ◽  
Ahmed M. Shehata ◽  
Nawal A. El-Fishawy
Keyword(s):  
Machine Learning ◽  
Detection System ◽  
Machine Learning Techniques ◽  
Learning Techniques

Supervised Classifier Approach for Intrusion Detection on KDD with Optimal MapReduce Framework Model in Cloud Computing

2019 ◽  
Vol 12 ◽  
Author(s):  
M. Ilayaraja ◽  
S. Hemalatha ◽  
P. Manickam ◽  
K. Sathesh Kumar ◽  
K. Shankar
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Intrusion Detection ◽  
Decision Tree ◽  
Learning Strategies ◽  
Nearest Neighbor ◽  
Detection System ◽  
K Nearest Neighbor ◽  
Mapreduce Model ◽  
The Web

Cloud computing is characterized as the arrangement of assets or administrations accessible through the web to the clients on their request by cloud providers. It communicates everything as administrations over the web in view of the client request, for example operating system, organize equipment, storage, assets, and software. Nowadays, Intrusion Detection System (IDS) plays a powerful system, which deals with the influence of experts to get actions when the system is hacked under some intrusions. Most intrusion detection frameworks are created in light of machine learning strategies. Since the datasets, this utilized as a part of intrusion detection is Knowledge Discovery in Database (KDD). In this paper detect or classify the intruded data utilizing Machine Learning (ML) with the MapReduce model. The primary face considers Hadoop MapReduce model to reduce the extent of database ideal weight decided for reducer model and second stage utilizing Decision Tree (DT) classifier to detect the data. This DT classifier comprises utilizing an appropriate classifier to decide the class labels for the non-homogeneous leaf nodes. The decision tree fragment gives a coarse section profile while the leaf level classifier can give data about the qualities that influence the label inside a portion. From the proposed result accuracy for detection is 96.21% contrasted with existing classifiers, for example, Neural Network (NN), Naive Bayes (NB) and K Nearest Neighbor (KNN).

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