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Observations on Social Engineering presentation by Warren Finch for LkNOG 6

  1. 1 Evolution of Social Engineering
  2. 4 4 Social Engineering Definition • “… uses psychological manipulation to trick users into making security mistakes or giving away sensitive information.” Imperva (Oct 2022) https://www.imperva.com/learn/application-security/social-engineering-attack/ • “… the art of manipulating people so they give up confidential information." Webroot (Oct 2022) https://www.webroot.com/us/en/resources/tips-articles/what-is-social-engineering • “… a manipulation technique that exploits human error to gain private information, access, or valuables ... Once an attacker understands what motivates a user’s actions, they can deceive and manipulate the user effectively." Kaspersky (Oct 2022) https://www.kaspersky.com/resource-center/definitions/what-is-social- engineering
  3. 6 https://www.newsfirst.lk/2022/10/28/massive-crypto-fraud-of-us- 37-mn-uncovered-in-sri-lanka/
  4. 8
  5. 9 Social Engineering Principles Social Engineering Principles (Reasons for Effectiveness) Authority and Trust Intimidation Consensus and Social Proof Scarcity Urgency Familiarity and Liking https://xmind.app/embed/ERb5/
  6. 10 Model for Social Engineering Attacks Wang, Z., Zhu, H., & Sun, L. (2021). Social Engineering in Cybersecurity: Effect Mechanisms, Human Vulnerabilities and Attack Methods. IEEE Access, 9, 11895–11910. https://doi.org/10.1109/ACCESS.2021.3051633
  7. 11 Model for Social Engineering Attacks Wang, Z., Zhu, H., & Sun, L. (2021). Social Engineering in Cybersecurity: Effect Mechanisms, Human Vulnerabilities and Attack Methods. IEEE Access, 9, 11895–11910. https://doi.org/10.1109/ACCESS.2021.3051633
  8. 12 12 Why does it work? Human Attributes Social Engineering Technique Trust – People are trustworthy where it is easy to gain trust with victims • Direct approach • Technical expert The desire to be ‘helpful’ – Most people are kind • Direct Approach • Technical expert • Voice of Authority The wish to get something for nothing • Chain email • SMS Curiosity • Open email attachments from unknown senders • Spam Fear of the unknown, or of losing something • Popup window Ignorance • Direct Approach • Dumpster diving https://www.academia.edu/8216745/Social_Engineering_it_s_impact_on_organization
  9. 13 13 Doesn’t matter who you are https://www.cert.gov.lk/2?lang=en&id=3
  10. 14 Doesn’t matter who you are Australian Statistics for 2022
  11. 15 Doesn’t matter who you are https://www.scamwatch.gov.au/scam-statistics Australian Statistics for 2022
  12. 16 Doesn’t matter who you are
  13. 18 The art of the con (Demo)
  14. 19 The Psychic Card Trick
  15. 21 Pick a card - any card
  16. 22 Is your card here?
  17. 24 24 Influence of technology https://www.dogana-project.eu/index.php/social-engineering-blog/11-social-engineering/98-se-evolution
  18. 25 25 Social Engineering Attack Framework Mouton, F., Leenen, L., & Venter, H. S. (2016). Social engineering attack examples, templates and scenarios. Computers & Security, 59, 186–209. https://doi.org/10.1016/j.cose.2016.03.004
  19. 26 26 Life cycle of attack https://www.imperva.com/learn/application-security/social-engineering-attack/
  20. 27 Type of attacks • Pre-texting • Baiting • Quid Pro Quo • Scareware • Phishing, Smishing, Vishing, Whaling • Telephone-oriented Attack Delivery (TOAD) • Tailgating Mouton, F., Leenen, L., & Venter, H. S. (2016). Social engineering attack examples, templates and scenarios. Computers & Security, 59, 186–209. https://doi.org/10.1016/j.cose.2016.03.004
  21. 28 28 Phishing statistics • 18-39yr old's average click rate of 29%, drops to 19% among older age groups. • 23% of male participants opened a phishing email compared to 10% for woman. • Public sector organizations were the most vulnerable to phishing attacks (with an average click rate of 36%) https://betanews.com/2022/10/13/older-generations-are- less-likely-to-click-phishing-emails/
  22. 29 Social Engineering Toolkit https://github.com/trustedsec/social-engineer-toolkit
  23. 30 30 Social Engineering Toolkit
  24. 31 31 Attack vectors / infection points • QRLJacking https://www.owasp.org/index.php/Qrljacking
  25. 32 Fake profiles
  26. 33 33 Real or Not?
  27. 34 34 Real or Not? https://this-person-does-not-exist.com/en
  28. 35 35 Real or Not?
  29. 36 36 Real or Not? https://drdavidhamilton.com/fake-social-media-profiles/
  30. 37 37 How to Detect a Fake Profile • Profile photo – Do a search using the image – https://support.google.com/websearch/answer/1325808 • Username • The Biography • Profile content • Number of followers
  31. 38 38 How to Report a Fake Profile • Twitter – https://help.twitter.com/en/forms/authenticity/impersonation • Instagram – https://help.instagram.com/contact/636276399721841 • Facebook (Meta) – https://www.facebook.com/help/306643639690823 • LinkedIn – Click the More icon on the member’s profile. – Click Report or block. • TikTok – Go to the profile of the account you want to report. – Tap the Settings icon – Tap “Report” and follow the steps in the app.
  32. 39 39 How to Report a Fake Profile https://www.cert.gov.lk/view?lang=en&articleID=267
  33. 40 Deep Fakes
  34. 41 41 Real or Not? https://youtu.be/l_6Tumd8EQI?t=70
  35. 42 Deep Fakes • Deepfake technology allows users to impersonate others with startling accuracy. – Deep Video Fakes (https://youtu.be/kOIMXt8KK8M) – Deep Voice Fakes (https://youtu.be/0ybLCfVeFL4) • Anyone can find deepfake software and services on the internet and have a relatively convincing representation of another person within minutes. – https://github.com/iperov/DeepFaceLab – https://github.com/sibozhang/Text2Video • Synthetic Identities are created by applying for credit using a combination of real and fake, or sometimes entirely fake, information.
  36. 43 43 Deep Fakes https://arxiv.org/abs/2005.05535
  37. 44 44 Deep Fakes https://youtu.be/0ybLCfVeFL4?t=83 Text-based Editing of Talking-head Video
  38. 45 Deep Fakes • … with the help of deepfakes, fraudsters can orchestrate social engineering attacks that appear to come from a friend or colleague, that is, someone we know and trust and whose motives do not need to be questioned.
  39. 46 Deep Fakes
  40. 47 Questions?
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