Spam Filtering. Uma Sawant, Megha Pandey, Sambuddha Roy. LinkedIn

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Cordelia Floyd
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2 Spam Filtering Uma Sawant, Megha Pandey, Sambuddha Roy LinkedIn

3 Spam Irrelevant or unsolicited messages sent over the Internet, typically to large numbers of users, for the purposes of advertising, phishing, spreading malware, etc. Spam removal essential for good user experience 3

4 Outline of the talk Challenges in spam filtering Man + machine motif Spam LI 4

5 Outline of the talk Challenges in spam filtering Man + machine motif Spam LI 5

6 Fighting spam Small network Large network Growth Cold start : no training data for ML algorithms Manual curation is doable Small network is less lucrative, not many sophisticated attacks Need for automation at scale Manually curated data == training data for ML algorithms Highly lucrative for spammers, more and cleverer attacks 6

7 Types of spam 1. Offensive content Hate mail Nudity 7

8 Types of spam 2. Monetary gain Money scam Romance scam 8

9 Types of spam 3. Drive traffic to external link phishing Wrong sender Misdirection 9

10 Types of spam 4. Collection of personal data , phone, birthday Address book sale Hi XXX, How are you? I hope you are fine. I came across your profile and found it very impressive,so I just reach out to you regarding an offer for a online business. Can I ask for your active phone number and address? 10

11 Types of spam 5. Fake profiles with malicious intent Connect with someone that would not have otherwise accepted the connection Precursor to fraud 11

12 Types of spam 6. Unprofessional content Puzzles Memes Jokes Puzzles Memes Jokes 12

13 Outline of the talk Challenges in spam filtering Man + machine motif Spam LI 13

14 Typical ML pipeline Collect training data Train algorithms Labeled training data false Test data ML algorithms true 14

15 Spam filtering : Man + machine Spam data distribution changes as spammers change tactics Human intervention and classifier retraining is important Labeled training Feedback loop data spam User generated Spam detection FP Human content algorithms Review ham 15

16 Spam filtering : Man + machine Spam data distribution changes as spammers change tactics Human intervention and classifier retraining is important Labeled training Feedback loop data spam User generated Spam detection FP Human content algorithms Review FN ham Member flagging 16

17 Outline of the talk Challenges in spam filtering Man + machine motif Spam LI 17

18 LinkedIn User profile (photo, description, geo...) User updates (posts, comments, like, shares,...) s Ads Network connections Groups... 18

19 Spam filtering signals : content + context Goal : classify a post as spam or not spam Context : who is the author (Number and types of connections, previous posts etc) Content of the post: text and rich media (image, video) Context : how the post propagates over the network Context : which user segment does the post engage Context : nature and quality of comments the post generates 19

20 Spam detection pipeline author, geo, connections Classification of contextual signals User generated content text, rich media spam scores spam Content classification to detect spam spam Prioritize detected items for human review ranked Human review scores items for ham review 20

21 Spam detection pipeline author, geo, connections Classification of contextual signals User generated content text, rich media spam scores spam Content classification to detect spam spam Prioritize detected items for human review ranked Human review scores items for ham review 21

22 Content Classification Text Content Multimedia Content Profanity classifier for short text: Detection of abuse, profanity and personal attacks in text updates and comments Short to mid length text Prevalent use of non-dictionary words and internet slang 22

23 Content Classification Text Content Multimedia Content Profanity classifier for short text Blogs and articles classifier: Detection of objectionable content in blogs and articles Larger length text documents Understanding of context and overall semantic sense may be useful 23

24 Content Classification Text Content Multimedia Content Profanity classifier for short text Blogs and articles classifier Scams and promotions classifier: Posted with malicious intent Money scams, unsolicited promotions Can be detected based on text content of the post 24

25 Content Classification Text Content Multimedia Content Profanity classifier for short text Profanity classifier for user photos and video clips: Blogs and articles classifier Scams and promotions classifier Detection of objectionable content in images and videos 25

26 Content Classification Text Content Multimedia Content Profanity classifier for short text Profanity classifier for user photos and video clips Blogs and articles classifier Near de-duplication Scams and promotions classifier Detect close visual similarity to known bad content Uses image hashing techniques 26

27 Content Classification Text Content Multimedia Content Profanity classifier for short text Profanity classifier for user photos and video clips Blogs and articles classifier Near de-duplication Scams and promotions classifier Other ML Classifiers: Based on a variety of visuo-temporal and spatial features 27

28 Engineering constraints Latency Online Nearline Offline Respect member privacy Enhanced member experience (personalization) Precision vs recall (high cost of false positives) 28

Commtouch RPD Technology. Network Based Protection Against Email-Borne Threats

Commtouch RPD Technology. Network Based Protection Against Email-Borne Threats Network Based Protection Against Email-Borne Threats Fighting Spam, Phishing and Malware Spam, phishing and email-borne malware such as viruses and worms are most often released in large quantities in