Artificial Intelligence in Cyber Security · Neural networks Natural Language Processing...

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Artificial Intelligence in Cyber SecurityPanacea, Pandora's Box or Nothing New under the Sun?

October 1st, 12:20-13:00

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3Photo by Hitesh Choudhary on Unsplash

4Photo by Louis Hansel on Unsplash

5Photo by Alex Knight on UnsplashPhoto by Jens Johnsson on Unsplash

6Photo by Todd Quackenbush on Unsplash

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Deep learning

Machine learning Random forest

K-nearest neighbors

Genetic algorithms

Linear regression

Logistic regression

Markov chainsNeural networks

Natural Language Processing

Reinforcement

learning

Techniques for narrow AI (AI Bingo!)

Machine learning

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UnsupervisedSupervised

Pre-labeled

‘ground truth’

Discover

commonalities &

outliers

Today’s services and products with narrow AI tools• Personal assistants

• Recommendation services

• Autonomous vehicles

• Data (image, audio, video, text) recognition/generation

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• Large datasets of adequate quality

• Algorithms to create a model or existing models to ‘learn’ and act

AI ingredients

• Large processing capacity

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11Photo by Maarten van den Heuvel on Unsplash

AI in cyber security

AI: Cyber security activities cheaper and at scale

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Marginal costs

Scale Scale

Performance

13 Defender has advantage

Attacker has advantage

Human

vulnerabilityTechnical

vulnerability

Titel | Datum14Photo by Olav Ahrens Røtne on Unsplash

Existing cyber security challenges

Automated vulnerability detection

15Photo by Chris Ried on Unsplash

Automated vulnerability detection with AI

• Examples:- Learning the patterns of security vulnerabilities directly from code using natural

language processing (NLP) (Russell et al. 2018)

- Automated software vulnerability detection with machine learning (Harer et al. 2018) - Machine Learning Methods for Software Vulnerability Detection (Chernis, Verma 2018)

- Pattern-Based Vulnerability Discovery (Yamaguchi 2015)

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Vulnerability dataset

Feature extraction

Train model Apply to code

Update classifierStatic

analysisDynamic analysis

Automated vulnerability detection with AI

• Benefits both attackers and defenders

• Reliable vulnerability datasets for training are a challenge

• AI is an addition to existing working methods

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18Photo by Marc A on Unsplash

Mass scale spear phishing

Mass scale spear phishing with AI

• Automatically acquire targets through social media mining

• Automatically create spear phishing message based on social media content

• Example:- Generative Models for Spear Phishing Posts on Social

Media (Seymour and Tully 2018)

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Mass scale spear phishing with AI

• Attackers benefits

• Targets human vulnerability

• Economies of scale for the attacker

• Detection of automatically generated text? (GLTR)

Titel | Datum20

Network and host based detection

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Network and host based detection with AI

• Detection usually based on profiles of ‘normal behavior’ given a certain context

• Defining outliers with unsupervised learning is still a challenge

• Useful to prioritize possible anomalies and to increase detection rates through human-computer cooperation (SIEM/SOC)

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23 Photo by Olav Ahrens Røtne on Unsplash

New cyber security challenges caused by AI

Deepfakes: social engineering on steroids

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https://ThisPersonDoesNotExist.com/

Social engineering on steroids

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Social engineering with AI

• Mostly used: generative adversarial networks

• Costs of model creation decreasing rapidly

• Deepfake detection is challenging

• Human authentication more important than ever!

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Automated hacking and patching

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Cyber Grand Challenge

AI as an attack vector

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AI as an attack vector (poisoning)

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AI as an attack vector

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Photo by Michael Sum on Unsplash

CAT

Photo by Berkay Gumustekin on Unsplash

Dog

AI as an attack vector

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Photo by Michael Sum on Unsplash

CAT

Photo by Michael Sum on Unsplash

DOG

Details: Explaining and harnessing adversarial examples (Goodfellow et al 2015)

Add specific noise

AI as an attack vector – options

• Evade AI detection

• Skew training models (poisoning)

• Steal models

• AI software vulnerabilities

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Key take aways

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•AI supplements and strengthens existing measures and

provides new opportunities for automation

•AI brings advantages for attack and defense

•AI isn’t a panacea or a Pandora’s box

Photo by Lodewijk Hertog on Unsplash

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