HPBigData2015Predicting Cyber Security Industry-JohnPark
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This document discusses using natural language processing (NLP) to analyze cybersecurity industry data and make predictions. Key findings from analyzing 2013-2014 data include the most frequent words ("malware", "security", "attack"), word pairs ("operating system", "targeted attack", "exploit kits"), mentions of security conferences, and national mentions. Predictions for 2015 extrapolate the top words will be "security", "attacks", "malware", "system", "data" and the top word pairs will be "operating system", "malware family", "exploit kits", "targeted attacks", "cyber security". Competitive analysis of other companies shows their most mentioned words. The document concludes security is about defending systems from attacks started
HPBigData2015Predicting Cyber Security Industry-JohnPark
- 1. SEIZE THE DATA. 2015 PredictingCyberSecurityIndustry HP Security Briefing 21 John Park/ August 11th, 2015
- 2. It all starts with a simple question.
- 3. “What is happening with computer security ?”
- 6. After 5000 hours of reading…
- 8. Lesson #1: There is more info than one can read in a lifetime.
- 9. Lesson #2: The more one reads, the more it sounds the same.
- 11. Natural Language Processing (NLP)
- 12. Word-smithing
- 13. Mass Media 101: Journalism The more important it is, the more it’s talked about.
- 14. Mass Media 102: Advertising The more it’s talked about, the more important it becomes.
- 16. Game Plan: Count the number of words and sequences of words (n-grams).
- 17. Data tip #1: Start with a high-quality dataset.
- 19. HPSR Cyber Risk Report 2015
- 20. NLP tip #1: Word (1-gram) frequency shows the ingredients.
- 21. NLP tip #2: Word pair (2-grams) are intersections. More niche.
- 22. NLP tip #3: 3+ grams are super-niche. May be too unique.
- 23. NLP tip #4: Stemming lowercase + substr(word,0,7)
- 24. NLP tip #5: Stop-words (is, the, ...) Normalize against general text. (go over the top 300 manually)
- 25. NLP tip #6: If timeline analysis, balance the “before” and the “after”.
- 26. NLP tip #7: It’s always best to have final human verification. Use the common sense. Find similar exploiters.
- 27. Without further ado, let’s see some results.
- 28. Result #1: Top 5 words (2013+2014) 1. Malware 2. Security 3. Attack 4. System 5. Exploits
- 30. Result #2: Top 5 n-grams (2013+2014) 1. Operating System 2. Targeted Attack 3. Exploit Kits 4. United States 5. Social Engineering
- 31. Result #3: Security conferences Black Hat 1. attack 2. security 3. presentation 4. system 5. talk Def Con 1. security 2. talk 3. attack 4. network 5. hackers Virus Bulletin 1. malware 2. system 3. security 4. app 5. detection
- 32. Result #4: National mentions 1. United States 2. Russia 3. China 4. Germany 5. Brazil
- 33. One more thing…
- 34. What we really want: Predictions
- 35. Prediction #1: Word frequency 2015 (extrapolated from 2013 + 2014) 1. Security (2) 2. Attacks (3) 3. Malware (1) 4. System (4) 5. Data (6)
- 36. Prediction #1: n-gram frequency 2015 (extrapolated from 2013 + 2014) 1. Operating System (1) 2. Malware Family (9) 3. Exploit Kits (3) 4. Targeted Attacks (2) 5. Cyber Security (19)
- 38. What we really want: Competitive Analysis
- 39. What are other companies in the industry working on? Microsoft 1. computers 2. detected 3. exploits 4. Microsoft 5. malware FireEye 1. attack 2. malware 3. targeted 4. FireEye 5. verticals Verizon 1. data 2. incidents 3. breaches 4. attacks 5. organizations
- 40. More data on HP Security Briefing 21:
- 41. Security is about defending the System against Attacks, that start with Exploits and controlled by Malware.
- 42. SEIZE THE DATA. 2015