SMS Spam Under the Microscope: How Smart Filtering Technologies Protect You

Unwanted text messages represent a deeper nuisance than email spam, as they affect large numbers of users who trust the message content and follow the sender’s instructions by clicking on fake links, which can result in financial losses such as exposure of sensitive data or monetary theft, while at the same time telecom companies also face financial losses due to these messages, including increased network operating costs and customer service expenses, in addition to brand damage and regulatory threats

SMS filtering mechanisms differ from email filtering in several ways, since reliable SMS datasets are rare compared to the large datasets available for email, and the shorter length of text messages limits the features that can be extracted for classification, therefore the practical solution lies in developing dedicated tools and models that can accurately identify, classify, or block unwanted messages before they reach users

From Email to SMS Filtering in Saudi Arabia

Experience in email filtering shows that many of the technologies used there can also be effective in combating SMS spam, as the surface-level similarity between email and text messaging suggests that proven methods—such as direct content filtering and collaborative content filtering—can be adapted to work with SMS

Direct Content Filtering

Direct content filtering relies on analyzing the message text itself to determine whether it is spam, and the process usually begins with simple keyword filters, then evolves into complex rule sets like SpamAssassin systems, and sometimes involves automatic text classification using machine learning.

SMS spammers use advanced techniques to avoid detection, as they often send small batches of messages first to monitor SMS network responses and determine the allowable message size limits, which highlights that relying on content-based filtering has become essential to counter the growing threat of SMS spam.

Collaborative Content Filtering

Collaborative filtering relies on users sharing information about spam messages, where the process involves creating a unique digital fingerprint for each reported spam message and sharing these fingerprints among a group of users or service providers, and when a new message arrives, a fingerprint is generated and compared against known fingerprints, so if matches are found, the message is classified as spam.

Through these processes, an up-to-date and diverse dataset of spam messages can be built, containing information such as the date received and the source, which helps researchers understand the nature of spam messages and how they evolve over time, including techniques used by senders to hide or modify message content to evade filters.

Challenges of SMS Filtering Compared to Email

1. Short message length: The maximum for SMS is 70 characters in Arabic and 160 in English, which limits the amount of analyzable content
2. Informal language: SMS messages often include abbreviations, spelling errors, and emojis, making analysis more difficult
3. Lack of message context: Emails provide headers with valuable filtering information, whereas SMS messages contain much less metadata
4. Mobile device limitations: Most devices—especially in emerging markets—lack robust on-device filters or spam folders, which reduces the effectiveness of collaborative filtering

SMS Spam Data

SMS messages have become more economically viable for attackers due to the availability of unlimited prepaid plans, and since SMS is considered a trusted service, users feel comfortable exchanging sensitive information through it, which in turn increases the demand for SMS and the need for more advanced filtering technologies. Simple detection methods rely on analyzing message traffic volume to identify suspicious senders, while sophisticated attackers use complex techniques to bypass such filters, and as a result, most companies have turned to machine learning–based filtering algorithms.

Machine learning techniques for SMS filtering depend heavily on the quality and quantity of pre-collected training data, and to achieve high filtering accuracy using text classification, datasets must include a representative and well-labeled collection of both spam and legitimate messages, allowing models to learn subtle patterns and distinctions between messages and thus enabling more accurate detection of future spam while accounting for linguistic and contextual features unique to the Middle East. However, in the SMS context, large public datasets are not yet available, as messages pass through privately owned networks that rarely allow sharing of user data for research purposes, and the field itself is relatively new compared to email filtering.

Regulatory Framework for SMS Filtering

Current anti-spam measures in mobile networks include spoofing and sender identity verification technologies, and the Communications, Space & Technology Commission (CST) has issued a regulatory document titled Regulations for Reducing Intrusive Messages and Calls, which governs SMS and voice message transmission and sets controls to limit spam, as it defines intrusive and unsolicited promotional messages, explains filtering mechanisms, and establishes financial penalties for violators that may reach SAR 5 million, according to Article (2.5) of the Telecommunications Law.

Regulation of Promotional Messages

The Executive Regulations of the Telecommunications Law impose strict controls on promotional messages, including:
• Sender identification: The sender’s identity must be clear and approved by the authority.
• Sending time: Promotional messages may only be sent between 9:00 AM and 10:00 PM.
• Message content: Content of an inappropriate, political, religious, or pornographic nature is prohibited.
• Opt-out mechanism: Messages must include a clear option for recipients to unsubscribe from future communications.

Regulation of Fraudulent and Fake Messages

Fraudulent and fake messages are strictly prohibited, and these include:
• Fraudulent messages: Those aimed at deceiving recipients to obtain personal or financial information.
• Fake messages: Those sent using a falsified sender identity to deceive recipients.

SMS Filtering Mechanisms

Blacklists

Blacklists are used to block messages sent to numbers reported by users or service providers, and these lists are managed by service providers in cooperation with the authority.

Content-Based Filters

Content filters detect messages containing banned words or phrases, and they are applied at the network level by service providers.

Behavior-Based Filters

Behavioral filters detect abnormal messaging patterns—such as sending large volumes of messages within a short period—and help identify fraudulent or spam activity