Secure Because Math: Understanding ML- based Security Products (#SecureBecauseMath)
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1 Secure Because Math: Understanding ML- based Security Products (#SecureBecauseMath) Alex Pinto Chief Data Scientist Niddel / @NiddelCorp
2 MLSec Project / Niddel MLSec Project research- focused branch of Niddel for open- source tools and community building Niddel builds Magnet, an Applied Threat Intelligence Platform, that leverages ML models to facilitate the usage of TI in a fully personalized and hassle- free way. Looking for beta testers and research collaboration More info at: niddel.com mlsecproject.org
3 Agenda Big Data Security Analytics Some History Anomaly Detection Classification Buyer s Guide
4 Big Data Security Analytics
5 Big Data Security Analytics
6 Security Singularity Approaches Machine learning / math / algorithms these terms are used interchangeably quite frequently. Is behavioral baselining and anomaly detection part of this? What about Big Data Security Analytics? (
7 Are we even trying? Hyper- dim Security Analytics 3 rd gen Artificial Intelligence Secure Because Math Math > Malware Lack of ability to differentiate hurts buyers, investors. Are we even funding the right things?
8 Is this a communication issue?
9 Guess the Year! ( ) behavior analysis system that enhances your network intelligence and security by auditing network flow data from existing infrastructure devices "Mathematical models ( ) that determine baseline behavior across users and machines, detecting (...) anomalous and risky activities (...) ( ) maintains historical profiles of usage per user and raises an alarm when observed activity departs from established patterns of usage for an individual.
10 A little history Dorothy E. Denning (professor at the Department of Defense Analysis at the Naval Postgraduate School) 1986 (SRI) - First research that led to IDS Intrusion Detection Expert System (IDES) Already had statistical anomaly detection built- in 1993: Her colleagues release the Next Generation (!) IDES
11 Enter the KDD After the release of Bro (1998) and Snort (1999), DARPA thought we were covered for this signature thing DARPA released datasets for user anomaly detection in 1998 and 1999 And then came the KDD- 99 dataset over 6200 citations on Google Scholar
12
13 Three Letter Acronyms
14 Three Letter Acronyms - KDD
15 Trolling, maybe?
16 Not here to bash academia
17 Objective Measurement of Success
18 Bringing some meaning to words
19 Anomaly Detection
20 Anomaly Detection Works wonders for well defined industrial- like processes. Looking at single, consistently measured variables Historical usage in financial fraud prevention.
21 Anomaly Detection
22 Anomaly Detection What fits this mold? Network / Netflow behavior analysis User behavior analysis Sommer, Robert and Paxson, Vern - Outside the Closed World: On Using Machine Learning For Network Intrusion Detection (2010)
23 AD: Curse of Dimensionality We need distances to measure the features/variables Usually Manhattan or Euclidian For high- dimensional data, the distribution of distances between all pairwise points in the space becomes concentrated around an average distance.
24 A Practical Example NetFlow data, company with n internal nodes. 2(nˆ2 - n) communication directions 2*2*2*65535(nˆ2 - n) measures of network activity 1000 nodes - > Half a trillion possible dimensions
25 Breaking the Curse Different / creative distance metrics Organizing the space into sub- manifolds where Euclidean distances make more sense. Aggressive feature removal A few (very few) interesting results available
26 AD: Normality- poisoning attacks LESS IMPACT Algorithm Feature Selection MORE IMPACT Ground Truth
27 AD: Normality- poisoning attacks
28 AD: Hanlon s Razor (aka Semantic Gap) Never attribute to malice that which is adequately explained by stupidity.
29 What about User Behavior? Behavior analysis!= Fraud Detection / Product Security Works as supervised Specific implementations for specific solutions Well defined scope and labeling Can user behavior (AD analysis, VPN connections) be generalized enough? Should I average out user behaviors in different applications?
30 Classification! VS
31 Why Supervised? Why not? If you can establish ground truth for your security data, by all means, use it Good sources of malicious data (mostly paid) Vetted non- malicious is still a challenge (Irena Damsky s CheckPoint - talk on whitelist data) Timeliness is also a big challenge. Your predictions affect the world, and you have to keep updating the models. When you create a classification model, you are forced to think about what you are modeling.
32 Lots of Malware Research GROUND TRUTH (PBs of malware)!! Lots of available academic research around this Classification and clustering of malware samples More success into classifying artifacts you already know to be malware then to actually detect it. (Lineage) State of the art? My guess is AV companies! All of them have an absurd amount of samples Have been researching and consolidating data on them for decades.
33 Lots of Malware Research Can we do better than AV Heuristics? Lots and lots of available data that has been made public Some of the papers also suffer from bad ground truth. VS
34 Network Data Classification IP reputation was the first step in this direction Lots of published papers about DGA classification and domain classification in general Terry Nelms Damballa - talk has good examples Greatest change in the last 2 years Threat Intelligence Network indicators can be used as malicious labels Feature extraction from internet topology, pdns and WHOIS infrastructure This is what Niddel s technology is based on
35 Can we fix security with ML?
36 Security ML Buyer s Guide 1) What are you trying to achieve with adding Machine Learning to the solution? 2) What are the sources of Ground Truth for your models? 3) How can you protect the features and ground truth from adversaries? 4) How does the solution and processes around it handle false positives?
37 Thanks! Q&A? @NiddelCorp We are drowning on information and starved for knowledge" - John Naisbitt
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