SmiDCA: Smishing attack detection for mobile computing on smishing dataset
Nowadays nearly everyone is using mobile computer/devices such as smart-phones and laptops to conduct their business transactions and for social purposes. While this trend has significantly transformed working and personal lifestyles worldwide, it has also led to serious concerns about threats to...
| Main Author: | |
|---|---|
| Format: | Thesis |
| Language: | English |
| Published: |
2019
|
| Subjects: | |
| Online Access: | http://psasir.upm.edu.my/id/eprint/83849/ http://psasir.upm.edu.my/id/eprint/83849/1/FSKTM%202019%2015%20-%20IR.pdf |
| _version_ | 1848859651025666048 |
|---|---|
| author | Hasan, Dahah Ahmed Haidarah |
| author_facet | Hasan, Dahah Ahmed Haidarah |
| author_sort | Hasan, Dahah Ahmed Haidarah |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Nowadays nearly everyone is using mobile computer/devices such as smart-phones
and laptops to conduct their business transactions and for social purposes. While this
trend has significantly transformed working and personal lifestyles worldwide, it has
also led to serious concerns about threats to security and privacy among individuals
as well as organizations. One of the most widespread security threats is phishing
attacks launched for the purpose of stealing certain sensitive information of victims
and then abusing this information to illegally obtain confidential data. There are
many types of phishing attack such as social phishing, spear-phishing, pharming,
and smishing. Recently Joo et al. (2017) proposed an improved security prototype
to detecting Smishing attack on mobile computing known as S-Detector. Their
model is able to distinguish between normal SMS message and phishing. However
Goel and Jain (2017a) claimed that S-Detector does not address three SMS security
message features. First, S-Detector cannot not check for login page within the SMS
message. Second, it is not efficient in detecting self-answering messages and Lastly, text normalization is not achieved. To solve these issues (Sonowal and Kuppusamy,
2018) propose new technique called SmiDCA. In this research, we re-implement
SmiDCA using dataset called smishing dataset for Harm ans Spam (Almeida, 2017).
The re-implement SmiDCA technique is analyzed SMS messages and extracted the
security features of SMS to detect the smishing SMS messages efficiently. |
| first_indexed | 2025-11-15T12:32:43Z |
| format | Thesis |
| id | upm-83849 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T12:32:43Z |
| publishDate | 2019 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-838492020-10-23T09:10:28Z http://psasir.upm.edu.my/id/eprint/83849/ SmiDCA: Smishing attack detection for mobile computing on smishing dataset Hasan, Dahah Ahmed Haidarah Nowadays nearly everyone is using mobile computer/devices such as smart-phones and laptops to conduct their business transactions and for social purposes. While this trend has significantly transformed working and personal lifestyles worldwide, it has also led to serious concerns about threats to security and privacy among individuals as well as organizations. One of the most widespread security threats is phishing attacks launched for the purpose of stealing certain sensitive information of victims and then abusing this information to illegally obtain confidential data. There are many types of phishing attack such as social phishing, spear-phishing, pharming, and smishing. Recently Joo et al. (2017) proposed an improved security prototype to detecting Smishing attack on mobile computing known as S-Detector. Their model is able to distinguish between normal SMS message and phishing. However Goel and Jain (2017a) claimed that S-Detector does not address three SMS security message features. First, S-Detector cannot not check for login page within the SMS message. Second, it is not efficient in detecting self-answering messages and Lastly, text normalization is not achieved. To solve these issues (Sonowal and Kuppusamy, 2018) propose new technique called SmiDCA. In this research, we re-implement SmiDCA using dataset called smishing dataset for Harm ans Spam (Almeida, 2017). The re-implement SmiDCA technique is analyzed SMS messages and extracted the security features of SMS to detect the smishing SMS messages efficiently. 2019-01 Thesis NonPeerReviewed text en http://psasir.upm.edu.my/id/eprint/83849/1/FSKTM%202019%2015%20-%20IR.pdf Hasan, Dahah Ahmed Haidarah (2019) SmiDCA: Smishing attack detection for mobile computing on smishing dataset. Masters thesis, Universiti Putra Malaysia. Mobile computing Data sets |
| spellingShingle | Mobile computing Data sets Hasan, Dahah Ahmed Haidarah SmiDCA: Smishing attack detection for mobile computing on smishing dataset |
| title | SmiDCA: Smishing attack detection for mobile computing on smishing dataset |
| title_full | SmiDCA: Smishing attack detection for mobile computing on smishing dataset |
| title_fullStr | SmiDCA: Smishing attack detection for mobile computing on smishing dataset |
| title_full_unstemmed | SmiDCA: Smishing attack detection for mobile computing on smishing dataset |
| title_short | SmiDCA: Smishing attack detection for mobile computing on smishing dataset |
| title_sort | smidca: smishing attack detection for mobile computing on smishing dataset |
| topic | Mobile computing Data sets |
| url | http://psasir.upm.edu.my/id/eprint/83849/ http://psasir.upm.edu.my/id/eprint/83849/1/FSKTM%202019%2015%20-%20IR.pdf |