Cyber Security Analysis by Dr. Kannimuthu from Karpagam College of Engineering, Coimbatore.

1. CYBER SECURITY ANALYTICS
Dr. S KANNIMUTHU,
Professor / CSE Department,
Karpagam College of Engineering,
Coimbatore.
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2. CYBER SECURITY
• Practice of
– protecting systems
– networks
– and programs from digital attacks
• Cyberattacks are usually aimed at accessing,
changing, or destroying sensitive information;
extorting money from users; or interrupting
normal business processes.
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3. Why CS Important?
• In today’s connected world, everyone benefits from
advanced cyberdefense programs.
• At an individual level, a cybersecurity attack can
result in everything from identity theft, to extortion
attempts, to the loss of important data like family
photos.
• Everyone relies on critical infrastructure like power
plants, hospitals, and financial service companies.
• Securing these and other organizations is essential to
keeping our society functioning.
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4. Why CS Important? (Contd..,)
• Everyone also benefits from the work of cyberthreat
researchers, like the team of 250 threat researchers at
Talos, who investigate new and emerging threats and
cyber attack strategies.
• They reveal new vulnerabilities, educate the public on
the importance of cybersecurity, and strengthen open
source tools. Their work makes the Internet safer for
everyone.
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5. Types of CS Threats
• Ransomware
– is a type of malicious software.
– it is designed to extort money by blocking access to files or
the computer system until the ransom is paid.
– paying the ransom does not guarantee that the files will be
recovered or the system restored.
• Malware
– is a type of software designed to gain unauthorized access
or to cause damage to a computer.
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6. Types of CS Threats (Contd..,)
• Social engineering
– is a tactic that adversaries use to trick you into revealing
sensitive information.
– they can solicit a monetary payment or gain access to your
confidential data.
– can be combined with any of the threats mentioned earlier
to make you more likely to click on links, download
malware, or trust a malicious source.
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7. Types of CS Threats (Contd..,)
• Phishing
– is the practice of sending fraudulent emails that resemble
emails from reputable sources.
– The aim is to steal sensitive data like credit card numbers
and login information.
– It’s the most common type of cyber attack.
– You can help protect yourself through education or a
technology solution that filters malicious emails.
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8. When Required
• Cyberterrorism
– is the disruptive use of information technology by
terrorist groups to further their ideological or
political agenda.
– This takes the form of attacks on networks,
computer systems and telecommunication
infrastructures.
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9. When Required
• Cyberwarfare
– involves nation-states using information technology to
penetrate another nation’s networks to cause damage or
disruption.
– Cyberwarfare attacks are primarily executed by hackers who are
well-trained in exploiting the intricacies of computer networks,
and operate under the auspices and support of nation-states.
– Rather than “shutting down” a target’s key networks, a
cyberwarfare attack may intrude into networks to compromise
valuable data, degrade communications, impair such
infrastructural services as transportation and medical services,
or interrupt commerce.
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10. When Required
• Cyberespionage
– is the practice of using information technology to obtain
secret information without permission from its owners or
holders.
– Cyberespionage is most often used to gain strategic,
economic, political or military advantage, and is conducted
using cracking techniques and malware.
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11. DATA ANALYTICS
• Data analytics is the science of drawing insights from raw
information sources.
• Many of the techniques and processes of data analytics
have been automated into mechanical processes
and algorithms that work over raw data for human
consumption.
• Data analytics techniques can reveal trends and metrics
that would otherwise be lost in the mass of information.
This information can then be used to optimize processes to
increase the overall efficiency of a business or system.
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12. APPLICATION AREAS
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13. CSA PROBLEMS
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• SMS SPAM DETECTION
• CREDIT CARE FRAUD DETECTION
• INTRUSION DETECTION

14. SMS SPAM DETECTION
• The mobile phone market has experienced a
substantial growth over recent years.
• As the utilization of mobile phone devices
has become commonplace, Short
Message Service (SMS) has grown into a
multi-billion dollars commercial industry
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15. SMS SPAM DETECTION
• As the popularity of the platform has
increased, we have seen a surge in the
number of unsolicited commercial
advertisements sent to mobile phones using
text messaging
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16. SMS SPAM DETECTION
• SMS Spam is particularly more irritating than
email spams, since in some countries they
contribute to a cost for the receiver as well.
• These factors along with limited availability of
mobile phone spam-filtering software makes
spam detection for text messages an
interesting problem to look into.
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17. SMS SPAM DETECTION
• Hence, we are in a position to implement
models for classifying SMS spam and ham
messages based on message text.
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18. Data Collection
• SMS Spam Collection Data Set available in UCI
Repository
• https://archive.ics.uci.edu/ml/datasets/SMS+S
pam+Collection
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19. Screenshot
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20. Data Preprocessing
• Unstructured data is converted into structured
form using IF-IDF vectorization
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21. Machine Learning Approaches
• We can use
– Logistic Regression
– Support Vector Machines
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22. Credit Card Fraud Detection
• Credit card fraud has emerged as major
problem in the electronic payment sector.
• From the moment the payment systems
came to existence, there have always
been people who will find new ways to
access someone’s finances illegally.
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23. Credit Card Fraud Detection
• This has become a major problem in the
modern era, as all transactions can easily
be completed online by only entering your
credit card information.
• Even in the 2010s, many American retail
website users were the victims of online
transaction fraud right before two-step
verification was used for shopping online.
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24. Credit Card Fraud Detection
• Organizations, consumers, banks, and
merchants are put at risk when a data
breach leads to monetary theft and
ultimately the loss of customers’ loyalty
along with the company’s reputation.
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25. Credit Card Fraud Detection
• We are in a position to study data-driven
credit card fraud detection particularities and
several machine learning methods to address
each of its intricate challenges with the goal to
identify fraudulent transactions that have
been issued illegitimately on behalf of the
rightful card owner.
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26. Data Collection
• Credit Card Fraud Detection Data Set available
in UCI Repository
• https://archive.ics.uci.edu/ml/datasets/defaul
t+of+credit+card+clients
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27. Screenshot
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28. Attribute Information
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29. Data Preprocessing
• Selecting the Relevant Features
• Replace Missing Values
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30. Machine Learning Approaches
• We can use
– Logistic Regression
– Bayesian Classification
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31. Intrusion Detection
• The rapid advances in the internet and
communication fields have resulted in a
huge increase in the network size and the
corresponding data.
• As a result, many novel attacks are being
generated and have posed challenges for
network security to accurately detect
intrusions.
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32. Intrusion Detection
• Furthermore, the presence of the intruders
with the aim to launch various attacks
within the network cannot be ignored.
• An intrusion detection system (IDS) is one
such tool that prevents the network from
possible intrusions by inspecting the
network traffic, to ensure its confidentiality,
integrity, and availability.
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33. Intrusion Detection
• Despite enormous efforts by the
researchers, IDS still faces challenges in
improving detection accuracy while
reducing false alarm rates and in detecting
novel intrusions.
• Machine Learning can be used to detect
intruders in an effective way.
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34. Data Collection
• Intrusion Detection Data Set available in UCI
Repository
• http://kdd.ics.uci.edu/databases/kddcup99/kd
dcup99.html
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35. Screenshot
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36. Attribute Information
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37. Data Preprocessing
• Selecting the Relevant Features
• Replace Missing Values
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38. Machine Learning Approaches
• We can use
– Logistic Regression
– Bayesian Classification
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39. THANK YOU
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40. Dr. S Kannimuthu
(Department of CSE)
Karpagam College of Engineering
Email- kannimuthu.me@gmail.com
Blog: http://skannimuthu.blogspot.in
Publon
Profile:https://publons.com/researcher/1686169/kannimuth
u-subramanian/
Google Scholar Profile:
https://scholar.google.co.in/citations?user=eSdX5S0AAAAJ&hl=en
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