What are some common suspicious profiling techniques?
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Suspicious profiling techniques have evolved significantly with the advent of social networking sites and the proliferation of online activities, ranging from financial transactions to personal communications. One common technique involves the dynamic behavioral analysis of social networking profiles, where indicators such as balance, energy, and anomaly are synthesized from daily activities to predict suspicious behavior with high accuracy. Similarly, in the realm of online dating and people recommenders, the sensitivity of recommender algorithms to scammers is assessed, revealing that collaborative strategies are more susceptible to fraudulent profiles compared to content-based techniques. The detection of coordinated malicious social campaigns is another sophisticated approach, combining authorship attribution techniques with behavioral analysis to identify and characterize suspicious profiles acting in concert. Collaborative filtering recommenders are also targeted through shilling attacks, where suspicious ratings are detected by constructing a time series and examining anomaly rating segments. Monitoring outbound communication from computers to identify attempts to access fraudulent domains is a technique used to calculate a trustworthiness score for domains, thereby preventing access to malicious sites. In the financial sector, techniques such as price-based detection, similarity matrices, and machine learning algorithms like support vector machines and random forests are employed to identify fake profiles in trading networks. Techniques to pollute electronic profiling involve creating cloned identities with divergent interests to mislead network eavesdroppers. Privacy-preserving schemes in microblogging platforms like Twitter distort real user profiles to protect against automatic profiling systems. Even in the field of genetic engineering, statistical data analysis processes like Principal Component Analysis (PCA) and Analysis of Variance (ANOVA) are used to evaluate variation sources, which can be adapted for suspicious profile detection. Lastly, the literature on anomalous user profile detection categorizes methods into supervised and unsupervised approaches, highlighting the importance of distinguishing real from fake profiles to prevent malicious activities.
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| Papers (5) | Insight |
|---|---|
| Common suspicious profiling techniques include testing domains for properties linked to known legitimate and fraudulent domains, calculating trustworthiness scores, and comparing them to predetermined thresholds for legitimacy determination. | |
25 Aug 2013 7 Citations | Common suspicious profiling techniques include authorship attribution, behavioral analysis, temporal similarity, and clustering algorithms for detecting and characterizing coordinated malicious social campaigns on Twitter. |
| Common suspicious profiling techniques include constructing time series of ratings, examining anomaly segments, and utilizing statistical metrics and target item analysis to detect shilling attacks in recommender systems. | |
09 Jun 2013 17 Citations | Common suspicious profiling techniques include balance, energy, and anomaly indicators based on user activities on social platforms, as proposed in the dynamic behavioral framework presented in the paper. |
| Not addressed in the paper. |
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