NUS-ISS Learning Day 2019- AI and Cybersecurity – Solution or Threat?

1Proprietary and Confidential © 2019Dual headquarters
in Switzerland and Singapore
Dual headquarters
Aug 2nd , Singapore
AI and Cybersecurity -
Solution or Threat?
Sergey Ulasen, Director of AI/ML Development

1. How to securely employ AI to power businesses
2. How is AI used in the cybersecurity domain
3. Ransomware protection
4. AI-powered attacks
Agenda

Fake Recognition Example
What chat is fake
Chat 2
 current date, pls
 secound of August if I remember it right.
 what is the date today
 Do you think my name is Gregorian Calendar?;-)
 what is the capital of Finland
 Possibly, Helsinki?
Chat 1
 Today's date is 02-Aug-2019
 Today is 02-Aug-2019
 Helsinki
 what is the capital of France
 Paris

Fake Recognition Example
What chat is fake
Chat 2
 secound of August if I remember it right.
 Do you think my name is Gregorian Calendar?;-)
 Possibly, Helsinki?
Chat 1
 Today's date is 02-Aug-2019
 Today is 02-Aug-2019
 Helsinki
 what is the capital of France
 Paris
Test Chatbot Eugene Goostman Bot

Founded in Singapore
by a Singaporean
in 2003
Established its second HQ in
Switzerland for its global
expansion in 2008
Global company with more than
1,300 employees
in 30+ offices worldwide
80% of Fortune 1000 companies use Acronis products and services
50,000+ partners • 500,000+ businesses • 5,000,000+ prosumers in 150+ countries
Singapore Schaffhausen, Switzerland Solar system
Acronis is a Swiss-Singaporean Company, a Global
Business, and the World Leader in Cyber Protection

How to securely employ AI
to power businesses

Steps to Adopting AI in Your Business
 Get familiar with AI
 Identify the problems you want AI to solve and prioritize goals
 Bring in experts and set up a pilot project
 Form a taskforce to integrate data to avoid ”garbage in – garbage out”
 Start small
 Collect data, pay attention for data privacy
 Analyse and mitigate security issues
 Incorporate AI as part of your daily tasks

AI Business Problems
 Algorithms can be copied
• It’s possible to reverse-engineer machine learning algorithms with up to 99% accuracy with given
access to only an API without having it on your computer
 Algorithms are vulnerable
• Evasion and Poison Attacks
 Compliance and privacy
• New era of data ownership, and general data protection regulation (GDPR). Data is becoming a liability
as well as an asset
• Ask for access to use customer data or data actually owned by other companies
• Avoid private data in training datasets
• Storing data according to laws
• Check if models don’t include private data (model dictionaries, word2vec, etc.)

Attacks against AI based systems
• Evasion attacks or “fooling” ML models
i. Software attacks (black and white boxes)
ii.“Physical” attacks
• Poison Attacks or compromising the learning process of an algorithm

Adversarial Attacks
Adding strategic noise to an image can be used to fool neural networks. Source: Goodfellow et al., 2015.

“Physical” Attack Examples
https://towardsdatascience.com/hacking-neural-networks-2b9f461ffe0b

Adversarial Patches Against YOLOv2
Matteo Testi, https://www.linkedin.com/feed/update/urn:li:activity:6550391677951987712

Models Defence
 For any type of attacks:
• Limit information leakage
• Limit probing
• Ensemble learning
• Adversarial training
• Defensive distillation
 For poisoning attacks:
• Data cleaning
• Anomaly detection
• Secured data collection

How is AI used in the
cybersecurity domain

What AI Can Do for Cybersecurity
 Helps to detect malicious activity on different stages of execution
 Helps to automate process of malware analysis
 Makes it easier to respond to cybersecurity risks. Cybersecurity solutions that rely on ML
use data from prior cyber-attacks to respond to newer but somewhat similar risk
 Free up enormous amount of time for cybersecurity experts and increase their
effectiveness
 Intelligent automation - reduce human labour without loss of quality or reliability of the
optimized process

What AI Can Not Do for Cybersecurity
 Solve all of our cybersecurity problems
 Unable to detect new types of malware
 Replace for human experts — at least not in the immediate future
 Zero false positives
 Algorithms are only as good as the humans that designed them

AI in Cybersecurity
Not working The only solution
Attacker has adversarial behavior
World is complex and changing
Problem is undecidable
Manual work should be automated
Continuous fast adaptation is required
AI techniques can work efficiently and
automatically in specific use cases
or to augment human work

AI Applications in Cybersecurity
Threat Detection
 SPAM
 Phishing (emails, sites)
 Malware (files, sites)
 Malicious Behavior (processes)
 Network Attacks (DDoS, exploits)
User Behavior Analysis
 Fraud Detection
 User Authentication
 Account Takeover Detection
Application Security
 Automated fuzzing
 Automated findings prioritization
Tools to augment human work
 Clustering of malware samples
 Data classification for forensics
 Automated feature extraction

Features to use by AI models
 Accessed APIs
 Accessed fields on the disk
 Accessed environmental products (camera, keyboard etc)
 Consumed processor power
 Consumed bandwidth
 Amount of data transmitted over the internet
 Etc.

Static Portable Executable Analysis
 Create portrait of the file based on static characteristics
 Advantages vs signature approach:
• Allow blocking exe-files on pre-execution stage
• Doesn’t require regular update of ML model
• Much smaller size of the model compare to AV bases
• Fast response time
• Comparable detection rate
 Disadvantages – false detections
• Need to be mitigated by whitelists
 Can be applicable for scripts detection and malicious
URL detection

Behavior Analysis
Detect malware in execution based on functions calls and their parameters
09:33:21.86 LoadLibraryA
09:33:21.88 VirtualAllocEx
09:33:27.88 CreateFileW
09:33:27.88 ReadFile
09:33:27.88 OpenMutexW
09:33:27.88 RegOpenKeyExW
09:33:27.88 RegQueryValueExW
09:33:27.88 CreateProcessInternalW
09:33:27.88 WriteProcessMemory
09:33:27.89 WriteProcessMemory
Operations log
Log
preprocessor
Operation
Dictionary
Word
Embedding
Hierarchical
Attention
Network
Cleaning data Process classification
Clean or
malicious
process
Preparing data

Is it possible to detect cyber attacks
before they happen?
 This is not AI task directly, but AI can help. Scanning large portions of data across the
internet, AI systems can learn how cyberattacks originate and suggest solutions for decision
makers within the organization
 Scanning the information about your company, that can be accessed via internet
 Some of the information that can be accessed about your company are:
• Hacktivist posts that target your company in dark forums or social media
• Leaked information about your company’s customers and employees (e-mail, passwords,
credit card information etc.)
• Phish website, mobile and desktop applications about your company
 If you know your virtual existence well and can manage it, you can reduce the risk of being
affected from a cyber attack

Ransomware Protection

Ransomware gone mainstream
Ransomware-as-a-Service fueling the overall growth
AIDS
1989
Cryptowall
2013
WannaCry
2017
Major Ransomware
Families in 2018
• GandCrab ransomware (RaaS)
• Data Keeper ransomware (RaaS)
• Black Ruby ransomware
• SamSam ransomware
$11.5B
damage by
2019*
* https://cybersecurityventures.com/ransomware-damage-report-2017-part-2/

Security technologies are failing
Attacker sends
malicious e-mail
Spam Filter
failed
Inbox
Download
invoice
Web Filter
failed
Web browser
Webpage shows
password
Password
protected ZIP
Open ZIP
archive
Web Filter
failed
Enter password
Run binary
Negotiate
encryption (C&C)
Binary jumps into
trusted process
Web Filter
failed
Encryption of files
Web address not
blocked
User clicks on
malicious link
Automatically
downloaded
User opens
malicious archive
User enters
password
Web address not
blocked
User runs malicious
binary in archive
Communication is
not blocked
Anti-Virus
failed
Binary is unknown
and obfuscated
Explorer.exe
Anti-Virus
failed
Archive is new and
password protected

Why antiviruses fail
Main reason: AV focuses on malicious code
detection, not on data protection.
Use of malicious scripts
(e.g. PowerShell, python, etc.)
Not many AV have something like script
emulator, program that launches script is
whitelisted and clean by default, so often
won’t be checked.)
Use of other accepted file
extensions (e.g. .WSF, .WSH,
.HTA, .PUB files)
Bypasses filters that proactively block
known dangerous attachments
(containing e.g. .EXE, .PDF.EXE, .JS,
.DOCM as extension)
Use of a .DLL file instead of an
.EXE
In-memory attack via downloaded
payload with no files on the disk
Bypasses sandbox, signature and ‘math-
based, next-gen’ products
Use of other active content in
weaponized documents (no
macros)
e.g. RAA Ransomware
Use of only trusted
binaries,
part of the OS
Bypasses application
whitelisting, signature & ‘math-
based, next-gen’ products
Manipulate
timestamp, create
extension-less copy,
encrypt copy and
delete original
Cripple / shake off
behavior-based monitoring

Ransomware Protection
Email/Vulnerability
Ransomware starts On file encryption:
Copy-On-Write of
changed files
Behavior detection
Data recovery
Kill Ransomware
ProcessReal time backup

AI-powered attacks

Any type of attacks enhanced with AI
 Impersonation of trusted users
• Malwares able to learn the nuances of an individual’s behaviour and language by analysing email and social media
communications
• Use this knowledge to replicate a user’s writing style, crafting messages that appear highly credible
• Messages written by AI malware almost impossible to distinguish from genuine communications
 Blending into the background
• Sophisticated threat actors can often maintain a long-term presence in their target environments for months at a time,
without being detected.
• Move slowly and with caution, to evade traditional security controls
• Often targeted to specific individuals and organizations
 Faster attacks with more effective consequences
• Today’s most sophisticated attacks require skilled technicians to conduct research on their target and identify
individuals of interest, understand their social network and observe over time how they interact with digital platforms
https://towardsdatascience.com/hacking-neural-networks-2b9f461ffe0b
Attacks using AI algorithms

GANs and Deep Fakes
https://arxiv.org/abs/1710.10196v3
Tero Karras, Timo Aila, Samuli
Laine, Jaakko Lehtinen
Progressive Growing of GANs
for Improved Quality, Stability,
and Variation

Mitigations against AI-powered attacks
• Classic attacks
• Antivirus
• Check vulnerability
• Alertness
• Etc.
• Modern attacks
• Knowledge of ”How it works”
• Request “secret password”
• Probe questions
• Etc.

Summary

1. With AI, the world is changing. Data becomes important, and many tasks
become doable by a computer
2. AI can be used in many domains, including security. Technologies range from
ML to object/face detection
3. We cannot affect how NNs take decisions. They are vulnerable to adversarial
attacks, and create some new security concerns
Summary

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