SMS Spam Filterinig Using Keyword Frequency Ratio
نویسندگان
چکیده
منابع مشابه
SMS Spam Detection using Machine Learning Approach
Over recent years, as the popularity of mobile phone devices has increased, Short Message Service (SMS) has grown into a multi-billion dollars industry. At the same time, reduction in the cost of messaging services has resulted in growth in unsolicited commercial advertisements (spams) being sent to mobile phones. In parts of Asia, up to 30% of text messages were spam in 2012. Lack of real data...
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In recent years, there has been considerable interest among people to use short message service (SMS) as one of the essential and straightforward communications services on mobile devices. The increased popularity of this service also increased the number of mobile devices attacks such as SMS spam messages. SMS spam messages constitute a real problem to mobile subscribers; this worries telecomm...
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Mobile SMS spam is on the rise and is a prevalent problem. While recent work has shown that simple machine learning techniques can distinguish between ham and spam with high accuracy, this paper explores the individual contributions of various textual features in the classification process. Our results reveal the surprising finding that simple is better: using the largest spam corpus of which w...
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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 solutions from email spam filtering. However it poses its own specific challenges. This paper motivates ...
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In this paper 1 a study of SMS messages in a large US based cellular carrier utilizing both customer reported SMS spam and network Call Detail Records (CDRs) is conducted to develop a comprehensive understanding of SMS spam in order to develop strategies and approaches to detect and control SMS spam activity. The analysis provides insights into content classification of spam campaigns as well a...
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ژورنال
عنوان ژورنال: International Journal of Security and Its Applications
سال: 2015
ISSN: 1738-9976,1738-9976
DOI: 10.14257/ijsia.2015.9.1.31