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What is Artificial Intelligence?
AI broadly refers to the ability of machines
to "think" like humans and perform tasks
considered "smart," without explicitly being
programmed to do so.
ML is a subset of AI. ML algorithms build a
mathematical model based on training
data, and they leverage the model to make
predictions when new data is provided
Source: pwc
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Unsupervised
Supervised
Reinforcement
SUPERVISED
Machine learns by using labelled
data
Regressions & Classification
Labelled data
External supervision
Maps labelled inputs to known
outputs
UNSUPERVISED
Machine is trained on an unlabelled
data without any guidance
Association & Clustering
Unlabelled data
No supervision
Understands patterns & discovers the
outputs
REINFORCEMENT
Agent interacts with its environment by
performing actions & learning from errors or
reward
Reward-based
No predefined data
Follows the trial-and-error method
RN
LEA
ING
Machine Learning Algorithms – overview of ML underlaying technologies
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Deep Learning
Data Requirement – Requires large data
Accuracy – Provider higher accuracy
Training Time – Takes longer to train
Hardware Dependency – Requires GPU to training
properly
Hyperparameter Tuning – Can be tuned in various
different ways
Learning
Machine Learning
Data Requirement – can train on lesser data
Accuracy – Gives lesser accuracy
Training Time – Takes less time to train
Hardware Dependency – Train on CPU
Hyperparameter Tuning – Limited tuning capabilities
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Supervised learning is when a model learns from a labeled dataset with
guidance. And, Unsupervised learning is where the machine is given
training based on unlabeled data without any guidance. Whereas
Reinforcement learning is when a machine or an agent interacts with its
environment, performs actions, and learns by a trial-and-error method.
.
AI Learning Model – in a nutshell
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AI is Math
Advanced and new
application of Statistics
Artificial General
Intelligence
Intelligence of a machine that
could successfully perform any
intellectual task
Machine Learning
Ability for (machines) to learn
without being explicitly
programmed – predictions
based on data sets
Deep Learning
“New ML” that recognizes
patterns - wider range of data
resources, less data pre-
processing by humans,
produce more accurate
results
Swarm technology
Collective behaviour of
decentralized, self-
organized systems,
natural or artificial -
Already used in drones
and fledgling robotics
devices
Overview of AI
Source : researchgate.com
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Network security
Application security
Information security
Operational security
Disaster recovery &
Business Continuity
End-user education
Cyber Security is the practice of
defending computers, servers,
mobile devices, electronic
systems, networks and data from
malicious attacks
What is Cyber Security?
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Virus, Trojan,
Spyware,
Ransomware,
Adware, Botnets
Malware
Email phishing,
Spear phishing,
Whaling, Vishing &
Smishing
Phishing
Application-layer
flood, Distributed
Denial of Service
Attacks (DDoS)
Denial-of-service
attack
SQL injection
In-band SQLi
(Classic), Inferential
SQLi (Blind) and Out-
of-band SQLi
Man-in-the middle
attack
Man-in-the browser
(MitB)
01 02 03 04 05
Types of cyber threats
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02 | Manual
Manual threat hunting—can be
expensive and time-consuming,
resulting in more unnoticed attacks.
04 | Hackers
Hackers often hide and change their
IP addresses—hackers use different
programs like Virtual Private Networks
(VPN), Proxy servers, Tor browsers, and
more. These programs help hackers
stay anonymous and undetected.
01 | Defence Paradigm
Defence paradigm based on
previous knowledge of attacks—
inefficient against zero-day and
variations
03 | Reactive
Reactive nature of cybersecurity—
companies can resolve problems
only after they have already
happened. Predicting threats before
they occur is a great challenge for
security experts.
Key flaws of Cyber Security
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The next generation of cyber security products are increasingly incorporating Artificial
Intelligence (AI) and Machine Learning (ML) technologies. By training AI software on large
datasets of cyber security, network, and even physical information, cyber security solutions
providers aim to detect and block abnormal behaviour.
There are different approaches to using AI for cyber security, and it is important first to
determine which is appropriate for the organization. Some software applications analyse
raw network data to spot an irregularity, while others focus on user/asset/entity behaviour to
detect patterns that deviate from normal. The types of data streams, how they are collected,
and the level of effort needed by analysts all vary by approach.
Artificial Intelligence for Cyber Security
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Offensive security – is a proactive and adversarial approach to protecting
computer systems, networks and individuals from attacks
Defensive security – offensive security measures are focused on seeking
out the perpetrators and in some cases attempting to disable or at least
disrupt their operations.
AI in Cyber Security techniques
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04 Conditional attacks 05 Classifying victims
CA CV
01 Malware creations 02 Smart botnets
MC SB
06 Adversarial AI
AAI
03 Spear phishing
SP
Offensive AI (Red Team)
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AI for Network
Threat Identification
AI-based Antivirus
Software
AI for Fighting AI
Threats
AI for Email
Monitoring
AI-based User
Behaviour
Modelling
01 02 03 04 05
AI Cyber Security Use Case
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What Resources Does the
System Require?
What About Recurring and
Additive Costs?
Can the System Adapt as
Your Network Evolves and
Expands?
How does the AI learn?
What volume of data is
required?
How often is retraining
needed?
The considerations you should make
when choosing an AI-based platform
How to Choose an AI-based
Cyber Security Platform
Is the solution efficient? Is It Really Unsupervised?
What are the error rates? Is the error rate acceptable
to achieve detection?
Wale Omolere Role of Ai in cyber security
