Hacker Halted 2018: Don't Panic! Big Data Analytics vs. Law Enforcement
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A man was discovered murdered on a street late at night. Police were called to the scene and began their investigation. They found limited clues but saw footage of a potential suspect. Detectives then used big data analytics to identify individuals near the crime scene around the time of the murder. This led them to three male suspects. Further investigation of these suspects focused on building profiles of their patterns of life in hopes of identifying the killer.
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Hacker Halted 2018: Don't Panic! Big Data Analytics vs. Law Enforcement
- 1. Don’t Panic! Big Data Analytics vs. Law Enforcement. Who can Solve it Faster?
- 2. There has been a murder… www.hackerhalted.com 2
- 4. At this time: www.hackerhalted.com 4 A body is discovered.
- 5. www.hackerhalted.com 5 White male, age 38
- 6. A woman discovers the body at 1:50AM after her shift ends at Tonio’s restaurant. She immediately calls the police. www.hackerhalted.com 6
- 7. Police arrive at 1:53AM. The officers cordon off the area and check for clues. www.hackerhalted.com 7
- 8. www.hackerhalted.com 8 Detective looks up and sees a security camera on the corner building… Time of death –recent Type – knife wound Wallet is intact – license, credit cards, photos, and cash Witnesses – no one is around besides the woman who called Side street
- 9. • Detective finally gets access to the owner of the building and gets a copy of the footage; they discover limited traffic but at 1:38AM they catch a partial image of a white male in a blue hooded sweatshirt, face unrecognizable. www.hackerhalted.com 9 Evidence
- 10. Investigation • Determine victim’s identity • Make/model of victim’s car • Time of death • Suspected method • Physical evidence • Begin witness canvassing • Victim’s entire geo coordinate information • Pattern of life • Social life • Work life • Shopping, interests, politics, health • Predictive, accurate measures of behavior www.hackerhalted.com 10 Detectives Big Data
- 11. Investigation • Interview witnesses • Talk to victim’s family, friends, co-workers • Look through social media • Profile on victim • Profile of suspects • Process evidence • Facts • No useable DNA • Facial recognition not useable • No grudges or beef • Knife was not victim’s • No witnesses saw anything • Location of all other people nearby at the time of the incident • Pattern of life on every one of those people • Google locations • GPS locations / frequent places • Social media data collection • Vehicle identification • Family history • Behavioral tendencies • Smart home sensor data • Ambient noise recordings • Buying and purchase history based on tailored ad content www.hackerhalted.com 11 Detectives Big Data
- 12. Putting the Two Together Based on ISP records and social media post location, detectives were able to retrieve the names of everyone with in a 1 km radius, 10 minutes before and after the crime took place. 11 individuals were identified • 8 individuals were Female • 3 individuals were male • Target recon conducted on the 3 males identified www.hackerhalted.com 12
- 13. Story Wrap-Up The Suspect • Acted out of anger / Road rage • Victim parked too close to suspect’s car • No connection to the victims life • Law enforcement can not find any direct link from the suspect to the victim and will need to use data analytic tools to help solve this crime. • Data Analysts and Targeters used data driven OSINT to build a profile and a pattern of life for the suspect. www.hackerhalted.com 13
- 14. Thank you. Tyler Cohen Wood Executive Director Cyber Workforce Development CyberVista Tyler.CohenWood@cybervista.net Twitter: @TylerCohenWood LinkedIn: linkedin.com/in/tylercohen78 Matt Salmon Technical Cybersecurity Manager CyberVista Matt.Salmon@cybervista.net LinkedIn: linkedin.com/in/matthew-salmon-129155a4 www.hackerhalted.com 14
Editor's Notes
- It’s not just each separate detail, it’s how the details are put together to portray a frighteningly accurate picture of a person’s entire life including things they might not even know about themselves. Think of it like a private detective following, cataloging every move you’ve ever made and questioning every person you’ve ever contacted to put together your life profile. Driving history, speeding, all locations Friends, family Explain detail and depth on each topic Exact genetic profile Work history Pattern of typing, walking Hobbies, drinking/eating patterns Exercise Shopping—likely to buy ALL LOCATIONS Type of person Home address Dating apps Uber patent Personal assistant devices recording all speech Android and app permission creep Posting Facial recognition Photo metadata
- Detectives—show image to friends, family Look through social media for clues
- Talking points to tell story: By querying Waze, frequent speeder, drives erratically Frequently posts on social media about idiot drivers Recently fired—LinkedIn shows job histories lasting no more than 3 months Posted a photo of the victim’s car who parked so close to him, showing his car and victim’s car Recent credit card purchases – bought a fixed blade camping knife Last thing they got, Alexa—recorded him talking about the incident
- OSINT hasn’t just solved one crime. It has solved dozens over the years.