Ensemble Learning-based Fake News and Disinformation Detection System

2021 ◽  
Author(s):  
Lumbardha Hasimi ◽  
Aneta Poniszewska-Maranda
Keyword(s):  
Ensemble Learning ◽  
Detection System ◽  
Fake News

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.

A Classification method of Fake News based on Ensemble Learning

2020 ◽  
Author(s):  
Sae Bom Lee ◽  
Joon Shik Lim ◽  
Jin Soo Cho ◽  
Sang Yeob Oh ◽  
Taeg Keun Whangbo ◽  
...  
Keyword(s):  
Ensemble Learning ◽  
Classification Method ◽  
Fake News

Aletheia: A Fake News Detection System for Hindi

2022 ◽  
Author(s):  
Jathin Badam ◽  
Akash Bonagiri ◽  
Kvln Raju ◽  
Dipanjan Chakraborty
Keyword(s):  
Detection System ◽  
Fake News

scholarly journals Misbehavior-Aware On-Demand Collaborative Intrusion Detection System Using Distributed Ensemble Learning for VANET

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1411 ◽  
Author(s):  
Fuad A. Ghaleb ◽  
Faisal Saeed ◽  
Mohammad Al-Sarem ◽  
Bander Ali Saleh Al-rimy ◽  
Wadii Boulila ◽  
...  
Keyword(s):  
Random Forest ◽  
Intrusion Detection ◽  
Ensemble Learning ◽  
Intrusion Detection System ◽  
Detection System ◽  
Lower Boundary ◽  
Normal Operation ◽  
Communication Overhead ◽  
On Demand ◽  
Collaborative Intrusion Detection

Vehicular ad hoc networks (VANETs) play an important role as enabling technology for future cooperative intelligent transportation systems (CITSs). Vehicles in VANETs share real-time information about their movement state, traffic situation, and road conditions. However, VANETs are susceptible to the cyberattacks that create life threatening situations and/or cause road congestion. Intrusion detection systems (IDSs) that rely on the cooperation between vehicles to detect intruders, were the most suggested security solutions for VANET. Unfortunately, existing cooperative IDSs (CIDSs) are vulnerable to the legitimate yet compromised collaborators that share misleading and manipulated information and disrupt the IDSs’ normal operation. As such, this paper proposes a misbehavior-aware on-demand collaborative intrusion detection system (MA-CIDS) based on the concept of distributed ensemble learning. That is, vehicles individually use the random forest algorithm to train local IDS classifiers and share their locally trained classifiers on-demand with the vehicles in their vicinity, which reduces the communication overhead. Once received, the performance of the classifiers is evaluated using the local testing dataset in the receiving vehicle. The evaluation values are used as a trustworthiness factor and used to rank the received classifiers. The classifiers that deviate much from the box-and-whisker plot lower boundary are excluded from the set of the collaborators. Then, each vehicle constructs an ensemble of weighted random forest-based classifiers that encompasses the locally and remotely trained classifiers. The outputs of the classifiers are aggregated using a robust weighted voting scheme. Extensive simulations were conducted utilizing the network security laboratory-knowledge discovery data mining (NSL-KDD) dataset to evaluate the performance of the proposed MA-CIDS model. The obtained results show that MA-CIDS performs better than the other existing models in terms of effectiveness and efficiency for VANET.

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