A Content-Based Approach for Detecting Smishing in Mobile Environment

Mishra, Sandhya; Soni, Devpriya

doi:10.2139/ssrn.3356256

Cited by 15 publications

(12 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When a prospective threat is discovered, the Watchdog subsystem shows a cautionary message on the screen and alerts the user. Mishra and Soni [34] developed a Content-Based Approach for discovering Smishing in cell phone, which classifies text messages based on their contents and URL behavior. To perceive the existence of URLs, phone numbers, email addresses, and hazardous phrases in SMS messages, text preprocessing and examination algorithms are used.…”

Section: B Technology-based Schemesmentioning

confidence: 99%

A Review of Smishing Attaks Mitigation Strategies

Njuguna

Kamau

Kaburu

2022

IJCIT

View full text Add to dashboard Cite

Mobile Smishing crime has continued to escalate globally due to technology enhancements and people's growing dependence on smartphones and other technologies. SMS facilitates the distribution of crucial information that is principally important for non-digital savvy users who are typically underprivileged. Smishing, often known as SMS phishing, entails transmitting deceptive text messages to lure someone into revealing individual information or installing malware. The number of incidences of smishing has increased tremendously as the internet and cellphones have spread to even the most remote regions of the globe.

show abstract

Section: B Technology-based Schemesmentioning

confidence: 99%

A Review of Smishing Attaks Mitigation Strategies

Njuguna

Kamau

Kaburu

2022

IJCIT

View full text Add to dashboard Cite

show abstract

“…2) Smishing detection. Smishing detection identifies malicious SMS messages by consulting blacklist/whitelist data [21] or by analyzing message content [3], [22], web page appearance [23], and downloaded Android application packages (APKs) [24]. The authors in [2] compared the performance of 18 existing detection algorithms using nine different datasets and demonstrated that most of the algorithms achieved accuracies of more than 95%.…”

Section: Related Workmentioning

confidence: 99%

Smishing Strategy Dynamics and Evolving Botnet Activities in Japan

Saeki

Kitayama

Koga³

et al. 2022

IEEE Access

View full text Add to dashboard Cite

XLoader and FakeSpy, the two major smishing botnets targeting Japan, change their attack strategies over various timescales. Based on recent observations of the botnets and Twitter data, we present empirical facts about their strategies and activity patterns and applied some of these strategic and activity patterns to malware detection and malicious domain detection. All the proposed methods yielded small false positive and negative rates, and are expected to run on user devices owing to their small computational cost. Recent malware detection methods based on traffic analysis extract TCP/IP traffic features if the upper layers of TCP are encrypted. In this study, Frida's hooking capability was employed to decode the upper layers (WebSocket and JSON-RPC) to create a list of all commands flowing over the botnet channel. The command-level traffic analysis presents decisive attack features because commands are transmitted according to strategies developed by the attackers. The proposed malicious domain detection method, on the other hand, exploited the tendency of the attackers to create domains in batches. Previous researchers focused on how benign and malicious domains were registered and used on the name servers. The proposed method, on the other hand, focuses on the arrival rate of SMS messages with URL links. The error rates become significantly small when users do not receive such messages very often.

show abstract

“…They have also done the keyword classification using classification algorithms in case the URL does not exist in SMS. In a study to recognize smishing messages [ 11 ], the authors presented a content-based approach. Whether the user is prompted to fill a form for revealing his credentials is inspected in this approach.…”

Section: Background Studymentioning

confidence: 99%

DSmishSMS-A System to Detect Smishing SMS

Mishra

Soni

2021

Neural Comput & Applic

Self Cite

View full text Add to dashboard Cite

With the origin of smart homes, smart cities, and smart everything, smart phones came up as an area of magnificent growth and development. These devices became a part of daily activities of human life. This impact and growth have made these devices more vulnerable to attacks than other devices such as desktops or laptops. Text messages or SMS (Short Text Messages) are a part of smartphones through which attackers target the users. Smishing (SMS Phishing) is an attack targeting smartphone users through the medium of text messages. Though smishing is a type of phishing, it is different from phishing in many aspects like the amount of information available in the SMS, the strategy of attack, etc. Thus, detection of smishing is a challenge in the context of the minimum amount of information shared by the attacker. In the case of smishing, we have short text messages which are often in short forms or in symbolic forms. A single text message contains very few smishing-related features, and it consists of abbreviations and idioms which makes smishing detection more difficult. Detection of smishing is a challenge not only because of features constraint but also due to the scarcity of real smishing datasets. To differentiate spam messages from smishing messages, we are evaluating the legitimacy of the URL (Uniform Resource Locator) in the message. We have extracted the five most efficient features from the text messages to enable the machine learning classification using a limited number of features. In this paper, we have presented a smishing detection model comprising of two phases, Domain Checking Phase and SMS Classification Phase. We have examined the authenticity of the URL in the SMS which is a crucial part of SMS phishing detection. In our system, Domain Checking Phase scrutinizes the authenticity of the URL. SMS Classification Phase examines the text contents of the messages and extracts some efficient features. Finally, the system classifies the messages using Backpropagation Algorithm and compares results with three traditional classifiers. A prototype of the system has been developed and evaluated using SMS datasets. The results of the evaluation achieved an accuracy of 97.93% which shows the proposed method is very efficient for the detection of smishing messages.

show abstract

A Content-Based Approach for Detecting Smishing in Mobile Environment

Cited by 15 publications

References 15 publications

A Review of Smishing Attaks Mitigation Strategies

A Review of Smishing Attaks Mitigation Strategies

Smishing Strategy Dynamics and Evolving Botnet Activities in Japan

DSmishSMS-A System to Detect Smishing SMS

Contact Info

Product

Resources

About