On Detecting Deception
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1 On Detecting Deception Sadia Afroz Privacy, Security and Automation Lab (PSAL) Drexel University
2 What is Deception? Deception: An adversarial behavior that disrupts regular behavior of a system
3 Deception in Different Areas Deception in Writing Style Deception in Website (Phishing) Deception in Blog Comment
4 Deception in Writing Style Writing by changing regular writing style
5 A Gay Girl In Damascus A blog by Amina Arraf Facts about Amina: A Syrian-American activist Lives in Damascus
6
7
8 A Gay Girl In Damascus
9 Fake picture (copied from Facebook) A Gay Girl In Damascus
10 A Gay Girl In Damascus Fake picture (copied from Facebook) The real Amina Thomas MacMaster A 40-year old American male
11
12 Hoax
13 Deception in Writing Style Goal: Distinguish regular writing from deceptive writings
14 Approach
15 Approach Data Collection
16 Approach Data Collection Feature Extraction
17 Approach Data Collection Feature Classification Extraction
18 Approach Data Collection Feature Classification Extraction Feature Ranking
19 Data collection Short-term deception: Extended-Brennan- Greenstadt Corpus Regular Imitation Long-term deception: -Thomas-Amina Hoax corpus Regular Deceptive Obfuscation Hemingway-Faulkner Imitation corpus Regular Imitation
20 Classification We used WEKA for machine learning. Classifier: Experimented with several classifiers Choose the best classifier for a feature set 10-fold cross-validation 90% of data used for training 10% of data used for testing
21 Feature sets We experimented with 3 feature sets: Writeprints Lying-detection features 9-features
22 Feature sets We experimented with 3 feature sets: Writeprints 700+ features, SVM Includes features like frequencies of word/character n-grams, partsof-speech n-grams. Lying-detection features 9-features
23 Feature sets We experimented with 3 feature sets: Writeprints 700+ features, SVM Lying-detection features 20 features, J48 decision tree Previously used for detecting lying. Includes features like rate of Adjectives and Adverbs, sentence complexity, frequency of self-reference. 9-features
24 Feature sets We experimented with 3 feature sets: Writeprints 700+ features, SVM Lying-detection features 20 features, J48 decision tree 9-features 9 features, J48 decision tree Used for authorship recognition Includes features like readability index, number of characters, average syllables.
25 Results Short-term deception: Extended-Brennan- Greenstadt Corpus Regular: 98% Imitation: 85% Obfuscation: 89% Long-term deception: -Thomas-Amina Hoax corpus 14% was detected as deceptive Regular authorship recognition shows inconsistency in writing style. Hemingway-Faulkner Imitation corpus Regular: 86.2% Imitation: 88.6%
26 Deception in Website: Phishing Real bank Alice uses online bank
27 Deception in Website: Phishing URL Browser Indicator SSL Real bank Alice uses online bank
28 Deception in Website: Phishing URL Browser Indicator SSL Real bank Alice uses online bank Fake bank
29 Deception in Website: Phishing URL Browser Indicator SSL Real bank Alice uses online bank Fake bank Alice thinks everything that looks like her bank Is her bank!
30 Approach: PhishZoo
31 Approach: PhishZoo Real site
32 Approach: PhishZoo Real site Extracts visual elements of the site
33 Approach: PhishZoo Images Visible text Real site Extracts visual elements of the site
34 Approach: PhishZoo Images Visible text Real site Extracts visual elements of the site
35 Approach: PhishZoo Images Visible text Real site Extracts visual elements of the site
36 Approach: PhishZoo Real site Extracts visual elements of the site Images Visible text Profile Stored
37 Approach: PhishZoo Real site Extracts visual elements of the site Images Visible text Profile Stored Fake site
38 Approach: PhishZoo Real site Extracts visual elements of the site Images Visible text Profile Stored Fake site Extracts visual elements of the site
39 Approach: PhishZoo Real site Extracts visual elements of the site Images Visible text Profile Stored Fake site Extracts visual elements of the site
40 Approach: PhishZoo Real site Extracts visual elements of the site Images Visible text Profile Stored Fake site Extracts visual elements of the site Images Visible text
41 Approach: PhishZoo Real site Extracts visual elements of the site Images Visible text Profile Stored Fake site Extracts visual elements of the site Images Visible text
42 Approach: PhishZoo Real site Extracts visual elements of the site Images Visible text Profile Stored Fake site Extracts visual elements of the site Images Visible text
43 Approach: PhishZoo Real site Extracts visual elements of the site Images Visible text Visual components match but the url, ssl don t match Profile Stored Fake site Extracts visual elements of the site Images Visible text
44 Approach: PhishZoo Real site Extracts visual elements of the site Images Visible text Visual components match but the url, ssl don t match Profile Stored Fake site Extracts visual elements of the site Images Visible text
45 Approach: PhishZoo Real site Extracts visual elements of the site Images Visible text Visual components match but the url, ssl don t match Profile Stored Phishing Alert Fake site Extracts visual elements of the site Images Visible text
46 Result Accuracy False PosiMve HTML Visible text in HTML Images Images & Screenshots Keywords visible texts 0.5 Images & Keywords
47 Result 96.4% accurate in detecting phishing Accuracy False PosiMve HTML Visible text in HTML Images Images & Screenshots Keywords visible texts 0.5 Images & Keywords
48 Future work: Deception in Blog Comment
49 Approach Spammers post same thing repeatedly. Use compression ratio (LZMA) Classifier: Latent logistic regression
50 Result
51 But spammers are smart! There are tools for spamming: Xrumer, SEnuke, Ultimate WordPress Comment Submitter (UWCS) That automatically Use proxy create new accounts Copy relevant words
52 Summary Deception in Writing Style: Distinguish regular writing from deceptive writings Deception in Website (Phishing) Detect website imitation Deception in Blog Comment Detect spam comments
53 Thanks! Sadia Afroz: Rachel Greenstadt: Michael Brennan: Ariel Stolerman: Andrew McDonald: Aylin Caliskan: Privacy, Security And Automation Lab ( Secure Computing Research for User Benefit (
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