WITHDRAWN: An effective approach to protect social media account from spam mail – A machine learning approach

Sharma, V. D.; Yadav, Santosh Kumar; Yadav, Sumit; Singh, Kamakhya Narain; Sharma, Suraj

doi:10.1016/j.matpr.2020.12.377

Cited by 20 publications

(11 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Table 12 covers a number of existing spam classification works that employ various Machine Learning (ML) methodologies. To protect social media accounts from spam, Sharma et al (2021) used Decision Tree (DT) and K-Nearest Neighbor (K-NN) classifiers. They tested their method using the UCI machine learning e-mail spam dataset.…”

Section: Spam Text Classification Techniquesmentioning

confidence: 99%

A systematic literature review on spam content detection and classification

Kaddoura

Chandrasekaran²,

Popescu

et al. 2022

PeerJ Computer Science

View full text Add to dashboard Cite

The presence of spam content in social media is tremendously increasing, and therefore the detection of spam has become vital. The spam contents increase as people extensively use social media, i.e., Facebook, Twitter, YouTube, and E-mail. The time spent by people using social media is overgrowing, especially in the time of the pandemic. Users get a lot of text messages through social media, and they cannot recognize the spam content in these messages. Spam messages contain malicious links, apps, fake accounts, fake news, reviews, rumors, etc. To improve social media security, the detection and control of spam text are essential. This paper presents a detailed survey on the latest developments in spam text detection and classification in social media. The various techniques involved in spam detection and classification involving Machine Learning, Deep Learning, and text-based approaches are discussed in this paper. We also present the challenges encountered in the identification of spam with its control mechanisms and datasets used in existing works involving spam detection.

show abstract

Section: Spam Text Classification Techniquesmentioning

confidence: 99%

A systematic literature review on spam content detection and classification

Kaddoura

Chandrasekaran²,

Popescu

et al. 2022

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

“…Figure 7 depicts the various phishing email detection methods utilised in the literature, and also the volume of publications that use each method. The most prevalent phishing email detection algorithms are supervised approaches, such as support vector machines (SVM) [44]- [50], logistic regression (LR) [44], [45], [48], [51]- [56], Decision Tree (DT) [48]- [50], [57]- [62], and Naïve Bayes (NB) [44], [63]- [65]. Unsupervised approaches such as kmeans clustering [48], [66]- [70] and deep learning methods have also been adopted [45], [51], [52], [63], [71]- [82].…”

Section: B Machine Learning Techniquesmentioning

confidence: 99%

“…The decision tree can be applied to the categorical or continuous variables. Instances of research in the literature using DT are [48]- [50], [56]- [62], [64], [67], [75], [87]- [101].…”

Section: ) Decision Tree (Dt)mentioning

confidence: 99%

See 1 more Smart Citation

A Systematic Literature Review on Phishing Email Detection Using Natural Language Processing Techniques

et al. 2022

View full text Add to dashboard Cite

Every year, phishing results in losses of billions of dollars and is a major threat to the Internet economy. Phishing attacks are now most often carried out by email. To better comprehend the existing research trend of phishing email detection, several review studies have been performed. However, it is important to assess this is sue from different perspectives . None of the surveys have ever comprehensively studied the use of Natural Language Processing (NLP) techniques for detection of phishing except one that shed light on the use of NLP techniques for classification and training purposes , while exploring a few alternatives. To bridge the gap, this study aims to systematically review and synthesise research on the use of NLP for detecting phishing emails. Based on specific predefined criteria, a total of 100 research articles published between 2006 and 2022 were identified and analysed. We study the key research areas in phishing email detection using NLP, machine learning algorithms used in phishing detection email, text features in phishing emails, datasets and resources that have been used in phishing emails, and the evaluation criteria. The findings include that the main research area in phishing detection studies is feature extraction and selection, followed by methods for classifying and optimizing the detection of phishing emails. Amongst the range of classification algorithms, support vector machines (SVMs) are heavily utilised for detecting phishing emails . The most frequently used NLP techniques are found to be TF-IDF and word embeddings. Furthermore, the most commonly used datasets for benchmarking phishing email detection methods is the Nazario phishing corpus. Also, Python is the most commonly used one for phishing email detection. It is expected that the findings of this paper can be helpful for the scientific community, especially in the field of NLP application in cybersecurity problems. This survey also is unique in the sense that it relates works to their openly available tools and resources. The analysis of the presented works revealed that not much work had been performed on Arabic language phishing emails using NLP techniques. Therefore, many open issues are associated with Arabic phishing email detection.INDEX TERMS Phishing email detection; systematic literature review; natural language processing; machine learning.

show abstract

“…Text classification (also known as text categorization) is a critical component of text analysis, which is the process of assigning appropriate predefined labels or tags to unstructured text such as phrases, paragraphs, questions, or documents in order to solve a variety of natural language processing problems. It is widely used in a wide range of applications, including spam email detection (Sharma et al, 2021;Ma, Yamamori & Thida, 2020;Taylor & Ezekiel, 2020), sentiment analysis (Waheeb et al, 2020;Alshamsi et al, 2020;Sudhir & Suresh, 2021), question answering (Perevalov & Both, 2021), news categorization (Fanny, Muliono & Tanzil, 2018;Mallick, Mishra & Chae, 2020), and user intent classification (Liu et al, 2020). Classification of pertinent text has demonstrated significant promise in a variety of domains, including marketing, product management, customer service, medical, and others.…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of K-Nearest Neighbour and Naïve Bayes Performances on Malay Text Classification

Muhait¹,

Mohemad

Noor

et al. 2021

cset

View full text Add to dashboard Cite

Police narrative reports are critical in assisting the investigation officer in uncovering hidden information during the criminal investigation process. In recent years, detecting criminal linkages by locating modus operandi in a massive volume of unstructured police reports has become a significant challenge. Here have been few studies on text classification in the Malay language due to some limitations that need to be addressed. Text classification is the process of properly categorizing text into a set of categories. In this study, classification techniques are used to predict the class of modus operandi for housebreaking crime documents using a Malay crime dataset. The dataset used in this study for housebreaking crime is a real dataset from the Royal Police Department of Malaysia. The purpose of this paper is to compare the accuracy of the K-Nearest Neighbour (KNN) and Naive Bayes algorithms for classifying Malay Crime Reports based on their mode of operation. The experiment results show that Naïve Bayes achieved a high accuracy rate of 97.86% with a 9 second execution time, whereas KNN achieved an accuracy rate of 88.43% with a 48 second execution time.

show abstract

WITHDRAWN: An effective approach to protect social media account from spam mail – A machine learning approach

Cited by 20 publications

References 11 publications

A systematic literature review on spam content detection and classification

A systematic literature review on spam content detection and classification

A Systematic Literature Review on Phishing Email Detection Using Natural Language Processing Techniques

Comparative Study of K-Nearest Neighbour and Naïve Bayes Performances on Malay Text Classification

Contact Info

Product

Resources

About