SMS Spam Detection Using Noncontent Features

Xu, Qian; Xiang, Evan Wei; Yang, Qiang; Du, Jiachun; Zhong, Jiawei

doi:10.1109/mis.2012.3

Cited by 70 publications

(33 citation statements)

References 6 publications

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“…Moreover, it enables the researcher to distinguish between good features and irrelevant features. Eliminating redundant and noisy features could cause performance improvement [38]. There are several existing methods to perform feature selection, such as the wrapper and filter methods [39].…”

Section: B Model Enhancement By Feature Selectionmentioning

confidence: 99%

Using supervised machine learning algorithms to detect suspicious URLs in online social networks

Al-Janabi

Quincey

András

2017

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017

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Abstract-The increasing volume of malicious content in social networks requires automated methods to detect and eliminate such content. This paper describes a supervised machine learning classification model that has been built to detect the distribution of malicious content in online social networks (ONSs). Multisource features have been used to detect social network posts that contain malicious Uniform Resource Locators (URLs). These URLs could direct users to websites that contain malicious content, drive-by download attacks, phishing, spam, and scams. For the data collection stage, the Twitter streaming application programming interface (API) was used and VirusTotal was used for labelling the dataset. A random forest classification model was used with a combination of features derived from a range of sources. The random forest model without any tuning and feature selection produced a recall value of 0.89. After further investigation and applying parameter tuning and feature selection methods, however, we were able to improve the classifier performance to 0.92 in recall.

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Section: B Model Enhancement By Feature Selectionmentioning

confidence: 99%

Using supervised machine learning algorithms to detect suspicious URLs in online social networks

Al-Janabi

Quincey

András

2017

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017

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“…However, the performance of the filter was not evaluated due to incompatibility of the algorithm with the available SMS public data set. Xu et al (2012) presented a server side approach to detecting SMS Spam using noncontent features. Combination of temporal and network features were used for training and testing.…”

Section: Review Of Existing Sms Spam Filtering Approachesmentioning

confidence: 99%

“…Mobile Network Operators (MNO) are also interested in reducing the SMS Spam on their networks because of SMS Flooding (Yadav, Kumaraguru, Goyal, Gupta, & Naik, 2011), which makes the SMS channel more invasive and less secure (GSMA, 2011). The amount of SMS Spam is on the rise; hence, there is need for a technical approach to detect SMS Spam automatically and accurately (Nuruzzaman et al, 2012;Xu, Xiang, Yang, Du, & Zhong, 2012).…”

Section: Introductionmentioning

confidence: 99%

An Adaptive and Collaborative Server-Side SMS Spam Filtering Scheme Using Artificial Immune System

Onashoga

Abayomi‐Alli

Sodiya

et al. 2015

Information Security Journal: A Global Perspective

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SMS Spam, which is an unsolicited or unwanted message, is a major problem with Global System for Mobile Communication (GSM) subscribers. Existing Spam filters have not been able to stop the SMS Spam problem due to frequent drift in spammer's words, limited bag of words for training, device portability, and high computational overhead of filters. This paper presents a collaborative and adaptive server-side SMS Spam filter using Artificial Immune System (coined ExAIS_SMS). The proposed scheme involves five modules: the innate mechanism, the user feedback, the quarantine, the tokenizer, and the immune engine. In this study, a new English corpus consisting of 5,240 SMS messages from 20 different users was collected for the study. A comprehensive experimental analysis on the SMS data set reveals the constant changes of Spam keywords and the impact of user feedback for system adaptability. In order to prove the efficiency of the proposed scheme, ExAIS_SMS was benchmarked with existing systems using the NUS corpus. The result gave an overall accuracy of 99% for ExAIS_SMS, 98% for Bayesian, and 97% for a client side AIS. The results showed that ExAIS_SMS is an efficient SMS Spam filtering technique, especially in resource constrained mobile phones.

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“…Despite this, attack frequency still increases. Xu et al [4] claims that SMS Spamming is, nowadays, a serious attack and manipulate the use of the SMS by spreading the advertisement in bulk. Sending unwanted SMS such as advertisement can make the user feeling disturb [5] and this against the privacy of the mobile user [6].…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study with RapidMiner and WEKA Tools over some Classification Techniques for SMS Spam

Foozy

Ahmad

Abdollah

et al. 2017

IOP Conf. Ser.: Mater. Sci. Eng.

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Abstract. SMS Spamming is a serious attack that can manipulate the use of the SMS by spreading the advertisement in bulk. By sending the unwanted SMS that contain advertisement can make the users feeling disturb and this against the privacy of the mobile users. To overcome these issues, many studies have proposed to detect SMS Spam by using data mining tools. This paper will do a comparative study using five machine learning techniques such as Naïve Bayes, K-NN (K-Nearest Neighbour Algorithm), Decision Tree, Random Forest and Decision Stumps to observe the accuracy result between RapidMiner and WEKA for dataset SMS Spam UCI Machine Learning repository.

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SMS Spam Detection Using Noncontent Features

Cited by 70 publications

References 6 publications

Using supervised machine learning algorithms to detect suspicious URLs in online social networks

Using supervised machine learning algorithms to detect suspicious URLs in online social networks

An Adaptive and Collaborative Server-Side SMS Spam Filtering Scheme Using Artificial Immune System

A Comparative Study with RapidMiner and WEKA Tools over some Classification Techniques for SMS Spam

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