1. The Use of Neural Networks In Cybersecurity
Ian Wolff
September 19, 2018

2. History of Artificial Intelligence(AI)
An idea that goes back to early Chinese and Greek cultures with Automatons.
Alan Turing, 1950 paper on “Computing Machinery and Intelligence”, “suggested that humans use available information as well as reason in order to solve problems and make decisions, so why can’t machines do the same thing?”
AI was officially coined in 1956, during a conference at Dartmouth College, by Allen Newell, Cliff Shaw, and Herbert Simon.
Program called Logic Theorist funded by the Research and Development Corporation (RAND).
The scientific community had an off again, on again love of the AI field until
IBM’s Deep Blue beat Chess grandmaster Garry Kasparov.
IBM’s Watson won Jeopardy in 2011.
Becominghuman’s definition
Chatbots

3. History of Artificial Intelligence(AI) Cont.

4. The Different Fields within Artificial Intelligence(AI)

5. Overview of Artificial Intelligence
A major thrust of AI is in the development of computer functions associated with human intelligence, such as reasoning, learning, and problem solving.
Artificial intelligence is a science and technology based on disciplines such as Computer Science, Biology, Psychology, Linguistics, Mathematics, and Engineering.
Artificial intelligence, sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals.
Unsupervised vs Supervised
Supervised learning are datasets that we have a reasonable idea of what the output should look like.
Contains labels
Unsupervised learning allows for a function to classify and group data without knowing what the structure should look like.
Learns similarities without names, can identify what’s different.
Reinforcement Learning
Goal oriented algorithms
How to obtain max number of points in a videogame?
DeepMind uses this method to create a neural network that functions nearly the same a human’s short term memory.
Narrow AI vs General AI
Siri or other chatbots
Can be biased based on information provided within the datasets.
Depressed teen AI
Racist Microsoft twitter bot
Facebook and IBM now offer bias identification services for datasets

6. What are Neural Networks?
Inspired by the animal brain’s processing power and composition.
Each artificial neuron cab transmit a number between 0 and 1.
Leverages computational inputs, Sigmoids (Logistic Curve) and weights.
Comes in different flavors.
Also referred to in some cases as Deep Learning, or Deep Learning Networks.
Image recognition
Speech recognition

7. What are Neural Networks, cont.
Convolutional Classifier – labelled input
Convolutional agent – can rank actions to perform in state
Replika Chatbot uses a neural network to respond via application
Can be used for language translation, but has limit on which languages
Of the 7,000 estimated languages only 100 have enough transcript data to do speech recognition.
MIT system that can describe a picture.

8. Brief Example of Neural Networks
But what *is* a Neural Network? | Deep learning, chapter 1
By 3BLUE1BROWN

9. Applications of Neural Networks within Cybersecurity
Network based Misuse and Anomaly Detection
HIDS and NIDS Detection and Response
MIT Research using neural Network IDS in 2011
30 keywords used to detect attacks
Cat, uuedecode, root shell commands
17 of 20 attacks detected with 1 false positive during attacks using shell code commands.
68 of 73 attacks detected with 4 false positives during attacks that executed C source code.
Endpoint Detection and Response
Cylance
2.7 Quadrillion “turns” or combinations used to detect anomalous behavior
99% True Positive Rate (Their words, not mine)
What AV should have been 5 years ago
Darpa
Georgia University
How is this relevant to us today?
How could it be relevant?

10. Applications of Neural Networks within Cybersecurity cont.
Threat Intelligence Platforms
Leverage deep learning and Machine Learning to categorize and report on:
Pattern recognition
Natural Language Processing
Adds structure to unstructured text (slang, jargon, multi-language support)
Predictive Analytics

11. Applications of Neural Networks within Cybersecurity, cont.
SignalSciences
Next Gen WAF and Runtime Application Self Protection (RASP)
Leverages neural networks to detect attack patterns and create response signatures.
Also able to analyze traffic and notify customers on anomalous activity.

12. The Downside to Neural Networks and AI within Cybersecurity
Explainability
Why did this detection and response event occur?
Complex algorithms are difficult to explain
Supervised Datasets
Currently, limited to human created datasets, what did we miss?
Unfinished Products Hitting the Market
The current hype cycle means a released product that may not have been completely vetted.
Too much training data and not enough real-world data used.

13. Wrapping up Investing in AI is a good thing for our industry.
Almost certainly behind the bad guys.
As always, properly vet your products to ensure they fit you environment, with an emphasis on what’s under the hood.
Neural Networks and Deep Learning will allow us to leverage all manner of data, with the hopes of being sent actionable high fidelity data.

14. References
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