Different real-world applications have varying definitions of suspicious behaviors. Detection methods often look for the most suspicious parts of the data by optimizing scores, but quantifying the suspiciousness of a behavioral pattern is still an open issue.

Suspicious Behavior Detection: Current Trends and Future Directions

As the popularity of the social media increases, as evidenced in Twitter, Facebook and China's Renren, spamming activities also picked up in numbers and variety. On social network sites, spammers often disguise themselves by creating fake accounts and hijacking normal users' accounts for personal gains. Different from the spammers in traditional systems such as SMS and email, spammers in social media behave like normal users and they continue to change their spamming strategies to fool anti-spamming systems. However, due to the privacy and resource concerns, many social media websites cannot fully monitor all the contents of users, making many of the previous approaches, such as topology-based and content-classification-based methods, infeasible to use. In this paper, we propose a Supervised Matrix Factorization method with Social Regularization (SMFSR) for spammer detection in social networks that exploits both social activities as well as users' social relations in an innovative and highly scalable manner. The proposed method detects spammers collectively based on users' social actions and social relations. We have empirically tested our method on data from Renren.com, which is one of the largest social networks in China, and demonstrated that our new method can improve the detection performance significantly.

/pdf/discovering-spammers-in-social-networks-2ej2sukwle.pdf

Discovering spammers in social networks

Finding influential users of online health communities: a new metric based on sentiment influence

The growth of mobile phone users has lead to a dramatic increasing of SMS spam messages. Recent reports clearly indicate that the volume of mobile phone spam is dramatically increasing year by year. In practice, fighting such plague is difficult by several factors, including the lower rate of SMS that has allowed many users and service providers to ignore the issue, and the limited availability of mobile phone spam-filtering software. Probably, one of the major concerns in academic settings is the scarcity of public SMS spam datasets, that are sorely needed for validation and comparison of different classifiers. Moreover, traditional content-based filters may have their performance seriously degraded since SMS messages are fairly short and their text is generally rife with idioms and abbreviations. In this paper, we present details about a new real, public and non-encoded SMS spam collection that is the largest one as far as we know. Moreover, we offer a comprehensive analysis of such dataset in order to ensure that there are no duplicated messages coming from previously existing datasets, since it may ease the task of learning SMS spam classifiers and could compromise the evaluation of methods. Additionally, we compare the performance achieved by several established machine learning techniques. Im summary, the results indicate that the procedure followed to build the collection does not lead to near-duplicates and, regarding the classifiers, the Support Vector Machines outperforms other evaluated techniques and, hence, it can be used as a good baseline for further comparison.

https://www.dt.fee.unicamp.br/~tiago/smsspamcollection/IJISS13.pdf

Towards SMS Spam Filtering: Results under a New Dataset

The rapid popularization of smartphones has contributed to the growth of online Instant Messaging and SMS usage as an alternative way of communication The increasing number of users, along with the trust they inherently have in their devices, makes such messages a propitious environment for spammers In fact, reports clearly indicate that volume of spam over Instant Messaging and SMS is dramatically increasing year by year It represents a challenging problem for traditional filtering methods nowadays, since such messages are usually fairly short and normally rife with slangs, idioms, symbols and acronyms that make even tokenization a difficult task In this scenario, this paper proposes and then evaluates a method to normalize and expand original short and messy text messages in order to acquire better attributes and enhance the classification performance The proposed text processing approach is based on lexicographic and semantic dictionaries along with state-of-the-art techniques for semantic analysis and context detection This technique is used to normalize terms and create new attributes in order to change and expand original text samples aiming to alleviate factors that can degrade the algorithms performance, such as redundancies and inconsistencies We have evaluated our approach with a public, real and non-encoded data-set along with several established machine learning methods Our experiments were diligently designed to ensure statistically sound results which indicate that the proposed text processing techniques can in fact enhance Instant Messaging and SMS spam filtering

Text normalization and semantic indexing to enhance Instant Messaging and SMS spam filtering

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.

SMS Spam Detection Using Noncontent Features

Online service providers benefit from collecting and analyzing preference data from users, including both implicit preference data (e.g., watched videos of a user) and explicit preference data (e.g., ranking data over candidates). However, it brings ethical and legal issues of data privacy at the same time. In this paper, we study the problem of aggregating individual's preference data in the local differential privacy (LDP) setting. One naive approach is to add Laplace random noises, which however suffers from low statistical utility and is fragile to LDP-specific poisoning attacks. Therefore, we propose a novel mechanism to improve the utility and the robustness simultaneously: the <italic>additive mechanism</italic>. The additive mechanism randomly outputs a subset of candidates with a probability proportional to their total scores. For preference data with Borda rule over <inline-formula><tex-math notation="LaTeX">$d$</tex-math><alternatives><mml:math><mml:mi>d</mml:mi></mml:math><inline-graphic xlink:href="wang-ieq1-3207486.gif"/></alternatives></inline-formula> items, its mean squared error bound is optimized from <inline-formula><tex-math notation="LaTeX">$O(\frac{d^{5}}{n\epsilon ^{2}})$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>O</mml:mi><mml:mo>(</mml:mo><mml:mfrac><mml:msup><mml:mi>d</mml:mi><mml:mn>5</mml:mn></mml:msup><mml:mrow><mml:mi>n</mml:mi><mml:msup><mml:mi>ε</mml:mi><mml:mn>2</mml:mn></mml:msup></mml:mrow></mml:mfrac><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="wang-ieq2-3207486.gif"/></alternatives></inline-formula> to <inline-formula><tex-math notation="LaTeX">$O(\frac{d^{4}}{n\epsilon ^{2}})$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>O</mml:mi><mml:mo>(</mml:mo><mml:mfrac><mml:msup><mml:mi>d</mml:mi><mml:mn>4</mml:mn></mml:msup><mml:mrow><mml:mi>n</mml:mi><mml:msup><mml:mi>ε</mml:mi><mml:mn>2</mml:mn></mml:msup></mml:mrow></mml:mfrac><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="wang-ieq3-3207486.gif"/></alternatives></inline-formula>, and its maximum poisoning risk bound is reduced from <inline-formula><tex-math notation="LaTeX">$+\infty$</tex-math><alternatives><mml:math><mml:mrow><mml:mo>+</mml:mo><mml:mi>∞</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href="wang-ieq4-3207486.gif"/></alternatives></inline-formula> to <inline-formula><tex-math notation="LaTeX">$O(\frac{d^{2}}{n\epsilon })$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>O</mml:mi><mml:mo>(</mml:mo><mml:mfrac><mml:msup><mml:mi>d</mml:mi><mml:mn>2</mml:mn></mml:msup><mml:mrow><mml:mi>n</mml:mi><mml:mi>ε</mml:mi></mml:mrow></mml:mfrac><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="wang-ieq5-3207486.gif"/></alternatives></inline-formula>. We also theoretically investigate minimax lower bounds of <inline-formula><tex-math notation="LaTeX">$\epsilon$</tex-math><alternatives><mml:math><mml:mi>ε</mml:mi></mml:math><inline-graphic xlink:href="wang-ieq6-3207486.gif"/></alternatives></inline-formula>-LDP preference data aggregation, and prove the error rate of <inline-formula><tex-math notation="LaTeX">$O(\frac{d^{4}}{n\epsilon ^{2}})$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>O</mml:mi><mml:mo>(</mml:mo><mml:mfrac><mml:msup><mml:mi>d</mml:mi><mml:mn>4</mml:mn></mml:msup><mml:mrow><mml:mi>n</mml:mi><mml:msup><mml:mi>ε</mml:mi><mml:mn>2</mml:mn></mml:msup></mml:mrow></mml:mfrac><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="wang-ieq7-3207486.gif"/></alternatives></inline-formula> is optimal for the Borda rule. Experimental results validate that our proposed approaches averagely reduce estimation error by <inline-formula><tex-math notation="LaTeX">$50\%$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>50</mml:mn><mml:mo>%</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="wang-ieq8-3207486.gif"/></alternatives></inline-formula> and are more robust to adversarial poisoning attacks.

Jiachun Du

Papers

SMS Spam Detection Using Noncontent Features

Analyzing Preference Data With Local Privacy: Optimal Utility and Enhanced Robustness