2,005 research outputs found
The performance of soft computing techniques on content-based SMS spam filtering
Content-based filtering is one of the most widely used methods to combat SMS (Short
Message Service) spam. This method represents SMS text messages by a set of selected
features which are extracted from data sets. Most of the available data sets have
imbalanced class distribution problem. However, not much attention has been paid to
handle this problem which affect the characteristics and size of selected features and
cause undesired performance. Soft computing approaches have been applied successfully
in content-based spam filtering. In order to enhance soft computing performance,
suitable feature subset should be selected. Therefore, this research investigates how
well suited three soft computing techniques: Fuzzy Similarity, Artificial Neural Network
and Support Vector Machines (SVM) are for content-based SMS spam filtering
using an appropriate size of features which are selected by the Gini Index metric as
it has the ability to extract suitable features from imbalanced data sets. The data sets
used in this research were taken from three sources: UCI repository, Dublin Institute of
Technology (DIT) and British English SMS. The performance of each of the technique
was compared in terms of True Positive Rate against False Positive Rate, F1 score and
Matthews Correlation Coefficient. The results showed that SVM with 150 features
outperformed the other techniques in all the comparison measures. The average time
needed to classify an SMS text message is a fraction of a millisecond. Another test
using NUS SMS corpus was conducted in order to validate the SVM classifier with
150 features. The results again proved the efficiency of the SVM classifier with 150
features for SMS spam filtering with an accuracy of about 99.2%
Content based hybrid sms spam filtering system
World has changed. Everybody is connected.
Almost each and everyone have a mobile phone. Millions of
SMSs are going around the world over mobile networks in
every second. But about 113 of them are spam. SMS spam has
become a crucial problem with the increase of mobile
penetration around the world. SMS spam filtering is a
relatively new task which inherits many issues and solutions
from email spam filtering. However it poses its own specific
challenges. Server based approaches and Mobile application
based approaches are accommodate content based and
content less mechanism to do the SMS spam filtering. Though
there are approaches, still there is a lack of a hybrid solution
which can do general filtering at server level while user
specific filtering can be done on mobile level. This paper
presents a hybrid solution for SMS spam filtering where both
feature phone users as well as smart phone users get benefited.
Feature phone users can experience the general filter while
smart phone users can configure and filter SMSs based on
their own preferences rather than sticking in to a general
filter. Server level solution consists of a neural network along
with a Bayesian filter and device level filter consists of a
Bayesian filter. We have evaluated the accuracy of neural
network using spam huge dataset along with some randomly
used personal SMSs
Hybrid Spam Filtering for Mobile Communication
Spam messages are an increasing threat to mobile communication. Several
mitigation techniques have been proposed, including white and black listing,
challenge-response and content-based filtering. However, none are perfect and
it makes sense to use a combination rather than just one. We propose an
anti-spam framework based on the hybrid of content-based filtering and
challenge-response. There is the trade-offs between accuracy of anti-spam
classifiers and the communication overhead. Experimental results show how,
depending on the proportion of spam messages, different filtering %%@
parameters should be set.Comment: 6 pages, 5 figures, 1 tabl
SMS Spam Filtering: Methods and Data
Mobile or SMS spam is a real and growing problem primarily due to the availability of very cheap bulk pre-pay SMS packages and the fact that SMS engenders higher response rates as it is a trusted and personal service. SMS spam filtering is a relatively new task which inherits many issues and solu- tions from email spam filtering. However it poses its own specific challenges. This paper motivates work on filtering SMS spam and reviews recent devel- opments in SMS spam filtering. The paper also discusses the issues with data collection and availability for furthering research in this area, analyses a large corpus of SMS spam, and provides some initial benchmark results
Kemahiran pemikiran komputasional pelajar melalui modul pembelajaran berasaskan teknologi internet pelbagai benda
kemahiran pemikiran komputasional pelajar, ke arah lebih kreatif dan kritis
melalui penggunaan Modul Pembelajaran Berasaskan Teknologi Internet
Pelbagai Benda (MP-IoT) yang telah dibangunkan oleh penyelidik.
Pembangunan MP-IoT mengikut Model ADDIE dan melibatkan Teknologi
Arduino yang diterapkan dalam 5 aktiviti pembelajaran secara amali. Kajian
berbentuk kuantitatif jenis kuasi-eksperimental ini telah dijalankan ke atas 52
orang pelajar Tingkatan 4 dari 2 buah sekolah di daerah Batu Pahat, Johor dan
Kuala Kangsar, Perak. Data pula telah dianalisis secara deskriptif dan inferensi.
Satu set ujian pencapaian pra dan pasca sebagai instrument telah dibangunkan.
Analisis Item Indeks Kesukaran (IK), Indeks Diskriminasi, serta Interprestasi
skor bagi nilai Alpha Cronbach telah digunakan bagi memastikan soalan ujian
pencapaian sesuai digunakan. Manakala dalam proses pembangunan modul
MP-IoT, seramai 6 orang guru dari mata pelajaran Sains Komputer dipilih
sebagai pakar untuk mengenal pasti kesesuaian dari segi format, kandungan dan
kebolehgunaan modul yang dibangunkan Skala Likert lima mata digunakan
dalam kajian ini. Secara keseluruhannya, dapatan kajian menggunakan ujian-T
sampel berpasangan, menunjukkan terdapat perbezaan yang signifikan terhadap
tahap pencapaian pelajar kumpulan kawalan yang didedahkan dengan kaedah
konvensional dengan kumpulan rawatan yang didedahkan dengan modul MPIoT,
dengan
nilai
p-value
adalah
.000 iaitu
kurang
dari
.05 (p<0.05).
Selain
itu,
tahap
kemahiran pemikiran komputasional pelajar juga meningkat setelah
didedahkan dengan modul MP-IoT
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