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Review and Analysis on Filtering of Unwanted Multimedia Messages from Online Social Network User Walls

Martand Ratnam
2021 International Journal for Research in Applied Science and Engineering Technology  
Thus, online social networks can benefit from information filtering, which can be used to help users organise messages written in public areas by removing unnecessary words.  ...  Elsevier journal of Information Sciences, vol. 428, pp. 120-135. [19] David, R, Florentino, F & Jose, RM 2018, ‘Using evolutionary computation for discovering spam patterns from e-mail samples’,  ...  E-mails in the content-based category are analysed for spam-related keywords and patterns.  ... 
doi:10.22214/ijraset.2021.39629 fatcat:kn2e4ak2nnhepcatxpvtfy6spm

Spam Detection Approach for Secure Mobile Message Communication Using Machine Learning Algorithms

Luo GuangJun, Shah Nazir, Habib Ullah Khan, Amin Ul Haq
2020 Security and Communication Networks  
The SMS spam collection data set is used for testing the method. The dataset is split into two categories for training and testing the research.  ...  In this technique, machine learning classifiers such as Logistic regression (LR), K-nearest neighbor (K-NN), and decision tree (DT) are used for classification of ham and spam messages in mobile device  ...  [14] proposed the spam detection method. ey used evolutionary computation for discovering spam patterns from e-mail samples.  ... 
doi:10.1155/2020/8873639 fatcat:lctvaeot2rcw7dkdmpltex6qiy

An Efficient Spam Filtering using Supervised Machine Learning Techniques

Deepika Mallampati
2018 International Journal of Scientific Research in Computer Sciences and Engineering  
Among the approaches developed to stop spam, filtering is an important and popular one. Common uses for mail filters comprise organizing incoming email and removal of spam and computer viruses.  ...  Email spam or junk e-mail (unsolicited e-mail "usually of a commercial nature sent out in bulk") is one of the major problem of the today's Internet, carrying financial damage to companies and annoying  ...  However, spam, also known as unsolicited commercial/ bulk e-mail, is a bane of e-mail communication. Spam is commonly compared to paper junk mail.  ... 
doi:10.26438/ijsrcse/v6i2.3337 fatcat:lqmepgdbrrh3hg6nnm7lemdpxe

Review on Efficient Spam Detection Technique using Machine Learning

Tejal S. Murkute, Nitin K. Choudhari, Dipalee M. Kate
2022 Zenodo  
This project makes use of Spam Detection to tell spam from valid email. SVM, a machine learning method, is employed in this case to assess it.  ...  The amount of time users sifting through incoming mail and eliminating spam necessitates the implementation of spam detection software.  ...  Time-efficient spam e-mail filtering using n-gram models. Pattern Recognition Letters, 29(1), 19-33. 11. Gandhi, R. (2018). Support vector machine-introduction to machine learning algorithms.  ... 
doi:10.5281/zenodo.5878453 fatcat:stt2wnbj7jhvtdilmvw5z3pe4y

Machine learning for email spam filtering: review, approaches and open research problems

Emmanuel Gbenga Dada, Joseph Stephen Bassi, Haruna Chiroma, Shafi'i Muhammad Abdulhamid, Adebayo Olusola Adetunmbi, Opeyemi Emmanuel Ajibuwa
2019 Heliyon  
Machine learning methods of recent are being used to successfully detect and filter spam emails.  ...  The upsurge in the volume of unwanted emails called spam has created an intense need for the development of more dependable and robust antispam filters.  ...  filtering spam e-mails.  ... 
doi:10.1016/j.heliyon.2019.e01802 pmid:31211254 pmcid:PMC6562150 fatcat:n7qiq4tgnzh7xi6j5c2ah335hy

Implementation of K-Means Clustering for Intrusion Detection

Saba Karim, Mr Rousanuzzaman, Patel Ayaz Yunus, Patha Hamid Khan, Mohammad Asif
2019 International Journal of Scientific Research in Computer Science Engineering and Information Technology  
We present an examination, routed to security experts, of machine learning methods connected to the recognition of interruption, malware, and spam.  ...  Bot malware depend on the Internet for proliferation, speaking with the remote assailant and executing assorted noxious exercises.  ...  Take an example; an e-mail program might attempt to classify an e-mail as "legitimate" or as "spam" mail.  ... 
doi:10.32628/cseit1952332 fatcat:u6iew73zkvbnhfbjp74onk3yha

Implementation of ML Algorithm's for Cyber Security

Mohammad Asif, Prof. E. M. Chirchi
2021 International Journal of Scientific Research in Science and Technology  
We present an examination, routed to security experts, of machine learning methods connected to the recognition of interruption, malware, and spam.  ...  Bot malware depend on the Internet for proliferation, speaking with the remote assailant and executing assorted noxious exercises.  ...  Take an example; an e-mail program might attempt to classify an e-mail as "legitimate" or as "spam" mail.  ... 
doi:10.32628/cseit217416 fatcat:6ayyuqblnjb5rm7ksurmksejr4

Machine Learning for E-mail Spam Filtering: Review,Techniques and Trends [article]

Alexy Bhowmick, Shyamanta M. Hazarika
2016 arXiv   pre-print
The initial exposition of the background examines the basics of e-mail spam filtering, the evolving nature of spam, spammers playing cat-and-mouse with e-mail service providers (ESPs), and the Machine  ...  We present a comprehensive review of the most effective content-based e-mail spam filtering techniques.  ...  These strategies include -word obfuscation, image spam, sending e-mail spam from hijacked computers, etc.  ... 
arXiv:1606.01042v1 fatcat:cblnuc4knfhehjwzjeeekbgf3m

Spam Detection Based on Feature Evolution to Deal with Concept Drift

Marcia Henke, Eulanda Miranda dos Santos, Eduardo Souto, Altair Olivo Santin
2021 Journal of universal computer science (Online)  
However, e-mails have become a major problem owing to the high amount of junk mail, named spam, which fill the e-mail boxes of users.  ...  Several approaches have been proposed to detect spam, such as filters implemented in e-mail servers and user-based spam message classification mechanisms.  ...  range for e-mail samples (non-spam) was concentrated between 0.40 and 0.60.  ... 
doi:10.3897/jucs.66284 dblp:journals/jucs/HenkeSSS21 fatcat:t3r2j4fdbnf4bmiznmeiufbxei

Detection of Email Spam using Natural Language Processing Based Random Forest Approach

M.A. Nivedha, S. Raja
2022 International journal of computer science and mobile computing  
spoken by people and the Random Forest approach uses multiple decision trees and uses a random node for filtering the spams.  ...  These spam emails may cause serious threat to the user i.e., the email addresses used for any online registrations may be collected by the malignant third parties (spammers) and they expose the genuine  ...  So, we move for machine learning approach, which uses certain algorithms and requires some sample emails for training and testing purposes, based on which the spam emails are automatically filtered [7  ... 
doi:10.47760/ijcsmc.2022.v11i02.002 fatcat:nqnacdqscfarneroogxr4can3q

Spam Email Classification using an Adaptive Ontology

Seongwook Youn, Dennis McLeod
2007 Journal of Software  
Hence the ontology would be customized for the user. This paper proposes to find an efficient spam email filtering method using adaptive ontology  ...  It is important to share information with each other for more effective spam filtering. Thus, it is necessary to build ontology and a framework for efficient email filtering.  ...  A good performance was obtained by reducing the classification error by discovering temporal relations in an email sequence in the form of temporal sequence patterns and embedding the discovered information  ... 
doi:10.4304/jsw.2.3.43-55 fatcat:xyban7golffhpfblbbbnqmc47q

Efficient Spam Email Filtering using Adaptive Ontology

Seongwook Youn, Dennis McLeod
2007 Fourth International Conference on Information Technology (ITNG'07)  
This paper proposes to find an efficient spam email filtering method using adaptive ontology  ...  Using ontology that is specially designed to filter spam, bunch of unsolicited bulk email could be filtered out on the system.  ...  A good performance was obtained by reducing the classification error by discovering temporal relations in an email sequence in the form of temporal sequence patterns and embedding the discovered information  ... 
doi:10.1109/itng.2007.86 dblp:conf/itng/YounM07 fatcat:6yqez72b5bfjpeklxdth3jlp4e

New Approach for Detection of IRC and P2P Botnets

Hossein Rouhani Zeidanloo, Farhoud Hosseinpour, Farhood Farid Etemad
2010 International Journal of Computer and Electrical Engineering  
The point that distinguishes our proposed detection framework from many other similar works is that there is no need for prior knowledge of Botnets such as Botnet signature.  ...  Since Artificial Immune System (AIS) is a new bio-inspired model which is applied for solving various problems in the field of information security, we used this concept in our proposed framework to make  ...  Obviously the data which is used for spam detection is a set of legitimate and spam message which is delivered in data capturing time. 1) Spam-related Activities: E-mail spam, known as Unsolicited Bulk  ... 
doi:10.7763/ijcee.2010.v2.271 fatcat:u7zkzaelcfdj7l6kyqd4pqvzzy

An artificial immunity approach to malware detection in a mobile platform

James Brown, Mohd Anwar, Gerry Dozier
2017 EURASIP Journal on Information Security  
This new mAIS has been compared with a variety of conventional AISs and mAISs using a dataset of information flows captured from malicious and benign Android applications.  ...  apps. mAISs differ from conventional AISs in that multiple-detector sets are evolved concurrently via negative selection.  ...  Government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copyright annotation thereon.  ... 
doi:10.1186/s13635-017-0059-2 fatcat:h4qw7hdmlrgwtgxlcbggyfpjuy

A Survey of Game Theoretic Approaches for Adversarial Machine Learning in Cybersecurity Tasks

Prithviraj Dasgupta, Joseph Collins
2019 The AI Magazine  
This article provides a detailed survey of the stateof-the-art techniques that are used to make a machine learning algorithm robust against adversarial attacks by using the computational framework of game  ...  Machine learning techniques are used extensively for automating various cybersecurity tasks.  ...  Acknowledgments The authors would like to acknowledge support from the US Office of Naval Research Summer Faculty Research program for supporting the work of Prithviraj Dasgupta at the US Naval Research  ... 
doi:10.1609/aimag.v40i2.2847 fatcat:aptetzccqfcwpcszm6s4kj7vtu
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