This document discusses how artificial intelligence and machine learning technologies like neural networks, deep learning, natural language processing, and intelligent agents can be applied to analyze data from the dark web. It provides definitions for these AI concepts and explains how dark web intelligence tools using AI could work by continuously scanning the dark web from a desktop to search for stolen data and other information. The document also discusses what the dark web is, how data on it can be accessed, and how dark web intelligence could be part of an organization's overall cybersecurity strategy.
2. Artificial Intelligence is the science of
emulating human intelligence
￭ Intelligence is generally viewed as the ability to:
▶ Sense and perceive
▶ Respond to situations flexibly
▶ Make sense out of ambiguous and contradictory information
▶ Recognize relative importance of different elements of situations
▶ Find similarities between situations despite differences
▶ Make rational decisions based on observations
￭ We can apply a variety of AI techniques in a Dark Web strategy
3. Deep Neural Networks are a machine learning
technology
￭ Neural Network objective is to solve a problem and
learn over time with increasing data input…
￭ …by mimicking the brain function – specifically the
neuron/synapse model
￭ Google’s face recognition capability is an example
Neural
Networks
DEFINITION:
An AI system that uses neuron paradigm of the brain to learn, find and
recognize patterns
4. Neural Networks are based on the neuron and
synapse structure of the human brain…
￭ General brain architecture:
▶ Many (relatively) slow neurons, interconnected
▶ Dendrites serve as input devices (receive electrical impulses from other neurons)
▶ Cell body "sums" inputs from the dendrites (possibly inhibiting or exciting)
▶ If sum exceeds some threshold, the neuron fires an output impulse along axon
▶ Synapses connect axons and therefore connects neurons
￭ Though brain neurons can only “fire” or “not fire” – representing a
“1” or a “0” – it is capable of processing amazing complexity
Neural
Networks
5. Layered neural networks are the basis for Deep
Learning
￭ Some patterns cannot be recognized by single
perceptron layer
￭ However, by adding additional layers of perceptrons, network can
develop complex feature detectors
￭ For example, consider Optical character recognition (OCR)
Neural
Networks
6. Natural Language Processing is a kind of
machine learning
￭ Deep understanding is still out of reach
￭ However, NLP does enable us to
▶ Recognize entities – persons, places, things, organizations
▶ Recognize topics
▶ Perform sentiment analysis, include trends, changes in trends,
etc.
Natural
Language
Processing
DEFINITION:
NLP systems extract meaning from text
7. What about higher-level reasoning?
￭ Intelligent Agents continuously perform several
functions:
▶ Perceive dynamic conditions in an environment
▶ Evaluate those conditions in light of a belief system
▶ When belief system reasons action must be taken…
▶ …either propose an action or initiate action in pursuit of a goal
(bounded authority)
Intelligent
Agent
DEFINITION:
An artificial intelligence system that can perceive its environment, interpret the
situation and make decisions in pursuit of accomplishing a goal
8. John Boyd’s OODA loop models dynamic
problem solving
￭ Represents how an aviator can and should think about
situations
Intelligent
Agents
￭ Continuous adaptation to continuously changing situations is the key
principle in Boyd’s OODA loop
9. What is the Dark Web?
￭ Surface vs. Deep web
￭ Dark web access methods
￭ Botnets vs. Active Sites
￭ Criminal Sites
10. Using the Dark Web for Defense
￭ Crawlers
￭ Offense informs Defense
￭ Special talents on the Dark Web
￭ Business Intelligence
11. What are We Searching For?
￭ Markers
￭ Hashing
￭ Successive searches
12. The Dark Web as Part of a Strategy
￭ The Dark Web can only do so much
￭ Work with other groups and actors
￭ Make sure you’re ready for bad news
DarkWebAI.com is developing a new tool for
leveraging the Dark Web
