The use of short text messages in social media and instant messaging has become a popular communication channel during the last years. This rising popularity has caused an increment in messaging threats such as spam, phishing or malware as well as other threats. The processing of these short text message threats could pose additional challenges such as the presence of lexical variants, SMS-like contractions or advanced obfuscations which can degrade the performance of traditional filtering solutions. By using a real-world SMS data set from a large telecommunications operator from the US and a social media corpus, in this paper we analyze the effectiveness of machine learning filters based on linguistic and behavioral patterns in order to detect short text spam and abusive users in the network. We have also explored different ways to deal with short text message challenges such as tokenization and entity detection by using text normalization and substring clustering techniques. The obtained results show the validity of the proposed solution by enhancing baseline approaches.
In this paper, we present the design of Greystar, an innovative defense system for combating the growing SMS spam traffic in cellular networks. By exploiting the fact that most SMS spammers select targets randomly from the finite phone number space, Greystar monitors phone numbers from the grey phone space (which are associated with data only devices like laptop data cards and machine-to-machine communication devices like electricity meters) and employs a novel statistical model to detect spam numbers based on their footprints on the grey phone space. Evaluation using five month SMS call detail records from a large US cellular carrier shows that Greystar can detect thousands of spam numbers each month with very few false alarms and 15 of the detected spam numbers have never been reported by spam recipients. Moreover, Greystar is much faster in detecting SMS spam than existing victim spam reports, reducing spam traffic by 75 during peak hours. The Short Messaging Service (SMS), one of the most successful cellular services, generates millions of dollars in revenue for mobile operators. Estimates indicate that billions of text messages are traveling the airwaves daily. Nevertheless, text messaging is becoming a source of customer dissatisfaction due to the rapid surge of messaging abuse activities. Although spam is a well tackled problem in the email world, SMS spam experiences a yearly growth larger than 500 . In this paper we present, to the best of our knowledge, the first analysis of SMS spam traffic from a tier-1 cellular operator. Communication patterns of spammers are compared to those of legitimate cell-phone users and Machine to Machine (M2M) connected appliances. The results indicate that M2M systems exhibit communication profiles similar to spammers, which could mislead spam filters. Beyond the expected results, such as a large load of text messages sent out to a wide target list, other interesting findings are made. For example, the results indicate that the great majority of the spammers connect to the network with just a handful of different hardware models. We find the main geographical sources of messaging abuse in the US. We also find evidence of spammer mobility, voice and data traffic resembling the behavior of legitimate customers. Short Message Service text messages are indispensable, but they face a serious problem from spamming. This service-side solution uses graph data mining to distinguish spammers from nonspammers and detect spam without checking a message's contents. Short messaging service (SMS) is one of the fastest-growing telecom value-added services worldwide. However, mobile message spam is a side effect for ordinary mobile phone users that seriously troubles their daily life and, as a result, threatens the revenue of telecom operators. In this paper, we present an SMS antispam system that combines behavior-based social network and temporal (spectral) analysis to detect spammers with both high precision and recall. The system infrastructure and the proposed approximate neighborhood index solution, which solves the scalability issue of social networks, are described in detail. Experimental results demonstrate that our proposed system achieves excellent discrimination between spammers and legitimates, and even with fixed recall at 95 , the online system and offline detection subsystems maintain a precision of about 98 and 99.5 , respectively.