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This is brief bibliography of some the resources in my library of scams. They include reference books, fiction, magician's books and more. The list not currently complete but I'm working on it. Abagnale Finances, Internet Beat the Dealer: Science and Practice Robert B. Abagnale Finances Step Right Up: Moreau King of Fakirs J. An Introduction to Critical Thinking: Classic Treatise on Card Manipulation S. Erdnase Gambling cards The Fraud: A Comprehensive Guide to the Turn off "Getting Started" Home A notable discovery of coosnage, The second part of conny-catching, Elizabethan and Jacobean quartos.

Gambling cards , Gambling dice , Gambling monte , Gambling theory. Art of the Steal: Bringing Down the House. Catch Me If You Can. Concise 48 Laws of Power.

Confessions of Felix Krull, Confidence Man: Crooks Are Human Too. Deception, Fate, and Rotten Luck. The Making of a Carnival Con Artist. Forty Years a Gambler on the Mississippi. Gambling to Win in Australia. Great Racing Gambles and Frauds.

cyberspace crime

Greetings in Jesus Name: Grifter's Game Hard Case Crime. Publications Pages Publications Pages. Don't have an account? Consumer Fraud Enters U. Popular Literature Phase 2: Show Summary Details Summary and Keywords Consumer fraud is the intentional deception of one or more individuals with the promise of goods, services, or other financial benefits that either never existed, were never going to be provided, or were grossly misrepresented.

Usability tests proved that participants were unable to differentiate between legitimate and fake web sites while anti-phishing security indicators in major web browsers were almost invisible to the layman [ 43 ].

In This Article

Phishing content detection can be benefited from technical information attesting unauthorized redirects to other domains, the level of visual or structural similarity among online services as well as from previously reported bad user experience. The classifiers rely on features extracted from the page URL punctuation and random tokens in URLs , the page content spam words detection , the page layout and design sloppy HTML markup and clumsy stylesheets and network characteristics blacklisted domain or IP address, spoofed DNS records. Moreover, Cantina [ 44 ], a TF-IDF approach to detect fake web sites, evaluated lexical signatures extracted from the content of the suspicious web page.

Lastly, a distinct anti-phishing approach presented by Wenyin et al. Crowdsourced online encyclopedias like Wikipedia are susceptible to vandalism; in other words, blatantly unproductive false edits that undermine entries credibility and integrity, thereby forcing administrators to manually amend the content.

Reported incidents vary from easily spotted vulgar language to inconspicuous alterations in articles such as placing arbitrary names in historical narratives and tampering with dates. The Wikipedia platform provides access to full revision history where spiteful alterations of the context may be located easily, and if necessary reverted.

Thus, the reputation of a user inside the platform as well as the extent of alteration of an article across time may serve as additional strong signs of ill-motivated content. The proposed solutions combine the aforementioned characteristics with Natural Language Processing NLP and machine learning classification.

Wang and McKeown [ 46 ] proposed a shallow syntactic semantic modelling based on topic specific n-tags and syntactic n-grams models trained on web search results about the topic in question. Other researchers such as Chin et al. In particular, their study was based on the fact that Wikipedia authors strive to maintain a neutral and objective voice in contrast to vandals who aim at polarization and provocation. Meanwhile, latest researches [ 49 ] based on spatial e.

Cyberbullying is defined as an aggressive, intentional act carried out by a group or individual systematically, using electronic forms of contact. The victims of cyberbullying are usually users who are unable to carry out the proper legal actions as a response, due to, say, their young age. Early approaches to tackle the problem attempted to detect threatening and intimidating content by focusing on individual comments. State-of-the-art studies are concentrated around unified approaches where bullying detection relies on broader, heterogeneous features and text mining paradigms.

The proposed feature sets combine profane content [ 50 ], gender information [ 51 ], and user activity history across multiple social networks [ 52 ]. Unlike previous approaches, Potha and Maragoudakis [ 9 ] addressed this issue using time series modelling. That is, instead of monitoring an online conversation in a fixed window, they took advantage of the whole thread and modelled it as a signal whose magnitude is the degree of bullying content.

Users who disrupt the on-topic discussions at social media, chat rooms, fora and blogs, namely trolls, attempt to provoke readers into an emotional response. This can degrade the quality of the content of web services or inflict psychological trauma to the users. Trolling detection systems follow common text mining paradigms and utilize conventional supervised classifiers trained with statistical and syntactic features extracted from inapt messages posted by users with known identifiers.

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Opinion fraud, also known as review spamming, is the deliberate posting of deceptive and misleading fake reviews to promote or discredit target products and services such as hotels, restaurants, publications and SaaS [ 55 ] products. The main obstacle while designing countermeasures is the unpredictability of human reviewing methods. Popular review hosting sites such as Yelp. According to Heydari et al.

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Supervised learning is the dominant technique in opinion fraud detection. Most of the employed features fall into three groups namely a linguistic features; b behavioral features and c network features. Such features derive from the content of the review, the metadata information and the information about the targeted product or service. Previous work in the field pieced together content based features, genre identification, POS analysis, psycholinguistic deception detection, n-gram-based text categorization techniques [ 57 , 58 , 59 ] as well as deep syntactic stylometry patterns based on context free grammar parse trees [ 60 ].

Other researchers focused on identifying novel detection techniques that can be generalized across domains. They also tried to overcome the main obstacle in opinion fraud detection, that is the lack of ground trust information by employing unsupervised classification models and co-training algorithms with unlabelled data. To this direction, Akoglu et al.

For a systematic review of the opinion fraud detection techniques the reader should refer to the work of Heydari et al. Having evaluated all the above, let us analyze our initial observations of employment scam. To begin with, one can immediately grasp that employment scam detection is a non-trivial, primarily text-based problem that is closely affiliated with the aforementioned problems, but still presents several peculiarities.

Most of them derive from the limited context surrounding a job ad, the brief user interaction with the ATS, and most importantly the fact that the malicious content aims by definition to be as indistinguishable as possible from the legitimate one. As a matter of fact, employment scam lacks strong contextual information. Furthermore, the activity of the composer of a post within an ATS through time is limited, that is, the user may generate a single advertisement, broadcast it and then not further interact with the ATS. In some cases, assailants impersonate existing businesses or recruiting agencies, which makes it harder to deduce the real origin of the job posting.

On the contrary, in problems such as trolling or cyber bullying detection, the analyst is able to compose additional contextual and temporal information, regarding the reputation of the misbehaving user, their sequence of actions, and their online footprint mined from multiple open social platforms. At the same time, ATS are offered as web applications over HTTP, which typically do not entail any dedicated network communication protocol as for example in email spam.

As in phishing or wikipedia vandalism, this fact alone makes it impossible to rely on multiple protocol layers for additional indications. As for the application layer, structural anomalies e. Moreover, information such as the location of a job or uploading the corporate logo are often neglected even by expert users.

As with opinion fraud detection discussed in Section 4. Added to that, our experimentation presented in Section 6 also confirmed that unilateral classifiers will mislabel at least one out of ten job ads. More precisely, opinion fraud heavily relies on detecting the outliers among a large number of available reviews about the same product on the same or similar websites.

In other words, the cardinality and the coherence of legitimate reviews are of the essence, whereas in employment scam maintaining a consistent hiring history for legitimate companies is neither straightforward nor practical. Lastly, it is questionable whether alternate approaches such as sentiment analysis could be effectively applied to employment scam, as compared to biased reviews trying to hype or defame products or businesses, the content of a job ad is usually written in neutral language.


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In summary, Table 1 presents the feature categories used for detecting malicious content in all six relevant problems discussed in Section 4. Employment scam detection features used in Section 6 are also added.


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In future work, we would like to experiment with more feature categories. In our effort to provide a clear picture of the problem to the research community we decided to release a set of relevant data. We anticipate that the EMSCAD dataset will act as a valuable testbed for future researchers while developing and testing robust job fraud detection systems. EMSCAD contains 17, legitimate and fraudulent job ads 17, in total published between to All the entries were manually annotated by specialized Workable employees.

The annotation process pertained to out-of-band quality assurance procedures. Two characteristic examples of fraudulent jobs are given in Figure 1. Each record in the dataset is represented as a set of structured and unstructured data. Fields can be of four types, namely string as in the job title, HTML fragment like the job description, binary such as the telecommuting flag, and nominal as in the employment type e. The detailed list of field types is displayed in Table 2.

Book Profiling The Fraudster : Removing The Mask To Prevent And Detect Fraud 2014

The original dataset is highly unbalanced. Furthermore, it contains duplicates and entries with blank fields due to the fact that fraudsters can quickly and repeatedly try to post the same job ad in identical or different locations. As a result, for our experimentation, we created a balanced corpus of legitimate and fraudulent job ads by randomly selecting among the entries that contained significant information in most fields for both classes and by skipping duplicates. Then, we trained two different classification models as presented in Section 6.

At this point we must underline that some entries in the full dataset may have been misclassified. For example, fraudulent entries may have managed to slip away from the manual annotation process and were thus misclassified as legitimate or on the other hand legitimate entries may have been marked as fraudulent due to an error in judgement.

Overall, we expect their number to be insignificant. In order to gain better insight into the dataset and provide a baseline to the research community, we subjected our balanced dataset to a multistep experiment. First off, we sanitized all entries and filtered out any unexpected non-English words by identifying non-ascii character sequences in texts using regular expression pattern matching. Afterwards, we used the long-established bag of words modelling, we trained six popular WEKA classifiers [ 65 ] and we evaluated their performance Section 6.

At a next step Section 6. Finally we compared the results. The first experiment consists of the bag of words bow modeling of the job description, benefits, requirements and company profile HTML fields shown in Table 2. Before feeding our data to six classifiers, namely ZeroR, OneR , Naives Bayes, J48 decision trees, random forest and logistic regression LR , we applied stopword filtering excluding most common English parts of speech such as articles and propositions.

The results are displayed in Table 3 and Table 4. As shown, the random forest classifier had the highest precision 0. Naive Bayes and J48 decision trees followed, both achieving similar F-measures of 0. Logistic regression performed poorly and its training time proved to be about six times slower than J48 even on a small dataset.

Although the ordinary random forest classifier showed promising results, it is important to emphasize that as described in Section 5 the preliminary balanced corpus is curated and its size is small in order to rush to firm conclusions. The goal of the second step was to build a preliminary ruleset consisting of contextual, linguistic and metadata features that derive from statistical observations and empirical evaluation of the balanced dataset. Those features are summarized in Table 1 and are presented in detail in the following sections.

In the subsequent diagrams, legitimate job ads are displayed in blue, whereas fraudulent ones in red. Although it can be argued that the following rules are specific to EMSCAD dataset, it is an interesting topic of future work to prove whether or not these rules apply in general. As illustrated in Figure 2 a, the dataset indicates the vast majority of scammers Moreover, a good indicator of a fraudulent posting is whether it advertises a telecommuting work from home position. The predictive power of this feature is stronger if one takes into account that the amount malicious postings that contain the characteristic is over two times greater than the corresponding benign ones.