Automatic Detection of Smishing Attacks by Machine Learning Methods

Balim, Caner; Günal, Efnan Şora

doi:10.1109/ubmyk48245.2019.8965429

Cited by 17 publications

(4 citation statements)

References 7 publications

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“…Another direction suggested is concerning the language and location shifts of future work by analyzing alternative languages from the ones used initially in current studies and understanding how local cultures leave their mark on a language that impacts text analysis. The language change effects are interesting for analysis because they can corroborate the power and breadth of the application of the techniques identified or proposed in the reviewed literature, demonstrating the general applicability (or not) of the methods or tools under evaluation [105,[108][109][110][111][112].…”

Section: Scenario Changes and Extensionsmentioning

confidence: 92%

Enabling Public Security Text-Based Analytics: A Survey to Outline Research Directions

De Carvalho,

Dos Santos,

Nepomuceno

et al. 2024

Preprint

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Text mining is a technological trend often highlighted in the continuous exchange of information through interconnected media. Its applicability goes beyond private organizations, as the public sector also requires it to treat textual information regarding services offered. Within this scenario, public security emerges as a prominent user of text mining that seeks to ensure the construction of data and knowledgebases to support decision-making about law enforcement actions to ensure citizen welfare. The primary objectives of this article are: (i) to develop a survey to identify text mining applications, techniques, opportunities, and challenges in public security, and (ii) to outline research directions concerning these topics and provide insights so that interested researchers can develop new studies. The literature was searched within four databases: Scopus, IEEE Xplore, ACM Digital Library, and Web of Science. A filtering process was applied to extract the works most aligned with the target theme, resulting in the selection of 194 of the most relevant works for a literature review. There were identified nineteen key applications of text mining related to public security and the most recurrent techniques and technologies reported between 2014 to 2021, supporting outlining three axes for future directions: one with possible expansion of objectives for new research; another on changes and adaptations in scopes for the methodological context; and the last one on expansions and changes in application scenarios based on the literature.

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Section: Scenario Changes and Extensionsmentioning

confidence: 92%

Enabling Public Security Text-Based Analytics: A Survey to Outline Research Directions

De Carvalho,

Dos Santos,

Nepomuceno

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Meanwhile, the URL source code is also determined to see if the form tag is present in the message. [11] proposed a content-based technique known as automatic detection of smishing using machine learning algorithms using Support Vector Machine (SVM), Logistic Regression (LR), and Random Forest (RF). The core of the proposed work consists of preprocessing, feature extraction, and classification.…”

Section: Anti-smishing Techniquesmentioning

confidence: 99%

Review of Smishing Detection Via Machine Learning

Mahmood,

Hameed

2023

Iraqi Journal of Science

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Smishing is a cybercriminal attack targeting mobile Short Message Service (SMS) devices that contains a malicious link, phone number, or email. The attacker intends to use this message to steal the victim's sensitive information, such as passwords, bank account details, and credit cards. One method of combating smishing is to raise awareness and educate users about the various tactics used by SMS phishers. But even so, this method has been criticized for becoming inefficient because smishing tactics are continually evolving. A more promising anti-smishing method is to use machine learning. This paper introduces a number of machine learning algorithms that can be used for detecting smishing. Furthermore, the differences and similarities among them as well as the pros and cons of each are presented to support future research into more effective anti-smishing solutions for securing mobile devices from cyber criminals.

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“…Moreover, older datasets become less effective as smishing campaigns evolve, leading to concept drift [20]. Another issue is that some researchers consider smishing to be a subset of spam and group them together in datasets [1,3,7]. While phishing is inherently malicious, spam can encompass unsolicited advertising.…”

Section: Introductionmentioning

confidence: 99%

Program Models California State University-San Marcos

Erickson¹,

Anderson²,

Zlotkowski³

2023

Learning With the Community

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While smishing (SMS Phishing) attacks have risen to become one of the most common types of social engineering attacks, there is a lack of relevant smishing datasets. One of the biggest challenges in the domain of smishing prevention is the availability of fresh smishing datasets. Additionally, as time persists, smishing campaigns are shut down and the crucial information related to the attack are lost. With the changing nature of smishing attacks, a consistent flow of new smishing examples is needed by both researchers and engineers to create effective defenses. In this paper, we present the community-sourced smishing datasets from the smishtank.com. It provides a wealth of information relevant to combating smishing attacks through the breakdown and analysis of smishing samples at the point of submission. In the contribution of our work, we provide a corpus of 1090 smishing samples that have been publicly submitted through the site. Each message includes information relating to the sender, message body, and any brands referenced in the message. Additionally, when a URL is found, we provide additional information on the domain, Virus-Total results, and a characterization of the URL. Through the open access of fresh smishing data, we empower academia and industries to create robust defenses against this evolving threat.

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Automatic Detection of Smishing Attacks by Machine Learning Methods

Cited by 17 publications

References 7 publications

Enabling Public Security Text-Based Analytics: A Survey to Outline Research Directions

Enabling Public Security Text-Based Analytics: A Survey to Outline Research Directions

Review of Smishing Detection Via Machine Learning

Program Models California State University-San Marcos

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