Information and Big Data Security

1. 01Big data fundamentals: small vs big data, characteristics (3Vs/6Vs), sources and formats
2. 02Big data technology, platforms, components, and governance; using the cloud for big data
3. 03Operational and analytical big data; business intelligence vs big data vs data mining
4. 04System and management view of information and big data security
5. 05Requirements for information and big data security; systems-design process and lifecycle security management
6. 06Basic policies on information security and methodologies; security risk management
7. 07Laws related to information security and management of operational systems
8. 08Applying machine learning and analytical tools to big data; cloud-based big data analysis

1. 01Introduction to Big Data Security
2. 02Operational and Analytical Big Data
3. 03Big Data Skills
4. 04Concepts of Big Data Security
5. 05Artificial Intelligence and Big Data Security
6. 06The Cyber Attacker's Economy
7. 07Machine Learning for Data Security
8. 08Supervised Learning for Data Security
9. 09Practical Activities
10. 10Unsupervised, Reinforcement and Deep Learning
11. 11Detection and Mitigation of Threats in Big Data
12. 12Machine Learning for Threat Detection in Big Data
13. 13Exploratory Data Analysis (EDA) for Network Security
14. 14Model Evaluation
15. 15Different Libraries for Big Data Manipulation
16. 16Practice Exercise of Exploratory Data Analysis for IDS Data Set
17. 17Practical — SVM and KNN for Intrusion Detection
18. 18Intrusion Detection Using Decision Tree (DT)
19. 19Network Anomaly Detection (K-Means Clustering)
20. 20The k-Nearest Neighbors (k-NN) Algorithm
21. 21Anomaly Detection Using Pandas and Scikit-Learn Libraries
22. 22Practical Illustration of Anomaly Detection in Customer Credit Card
23. 23Security Management and Its Objectives
24. 24The Security Design Process and the Security Management Life Cycle
25. 25Information Security Policy, Principles and Methodologies
26. 26Big Data Stages, Technologies and Adoption

26 transcribed topics, taken from the lecturer’s own workbook. Readable in full once you sign in.

Definition

Big Data Security refers to the tools, policies, and measures used to protect large volumes of data from unauthorized access, breaches, and misuse throughout its lifecycle — from collection to processing and storage.

M2M (Machine to Machine)

data exchanged between connected devices

People to Machine

data generated through human interactions with technology

Organisational Data

data produced by businesses and institutions

Operational Big Data

Operational Big Data refers to real or nearly real-time data used to support daily business operations and decision making. It processes data immediately as it is generated, supports transactional systems, and requires fast processing.

Analytical Big Data

Analytical Big Data refers to historical and accumulated data used for analysis, forecasting, reporting, and strategic decision making. It focuses on past and current data, supports business intelligence, and is often stored in data warehouses.

Technical Skills

programming, database management, big data frameworks

Analytical Skills

data analysis, pattern recognition, statistical analysis

Business Skills

translating insights into actionable business strategies

Communication Skills

presenting findings clearly to non-technical audiences

Problem Solving Skills

using data-driven approaches to solve complex problems

Programming

ability to write code to manipulate and process data

Database Skills

MySQL, Oracle — querying and managing structured data

Big Data Frameworks

Hadoop, Spark, Kafka — large-scale data processing tools

Data sources

Records the university already holds: semester results and GPA history per course, lecture attendance registers, student-portal login and activity logs, library borrowing and e-resource access, JAMB and O-level entry scores, departmental and level enrolment figures, fee-payment timing, and student feedback or…

Collection and storage

Most of this starts on paper or in departmental spreadsheets, so the first tasks are digitising it and agreeing one student identifier that works across departments. Structured records go into a central data warehouse; feedback text and portal logs suit a data lake.

Type of analytics

All four, in order. Descriptive answers what happened — pass rates by course and level. Diagnostic asks why — which factors track with failure. Predictive identifies which students are likely to carry over a course while there is still time to act. Prescriptive recommends which intervention to offer whom

Tools and technology

Python with pandas for the analysis, PostgreSQL or MySQL for the warehouse, and Power BI or Excel for dashboards that deans will actually open. Hadoop and Spark are the fashionable answer and the wrong one here: a single university produces gigabytes, not petabytes, and one relational database server handles that…

Benefits and challenges

Benefits: at-risk students identified before they carry over a course, better lecturer and timetable allocation, and evidence to support accreditation. Challenges: incomplete and inconsistent records, no single student identifier across departments, staff with no analytics training, unreliable power and connectivity…

Volume

Protecting petabytes or exabytes of data requires security tools that can scale dynamically without creating processing bottlenecks.

Velocity

Data streams into systems in real-time (e.g., IoT sensors, network logs). Security mechanisms like encryption and threat detection must happen on the fly without causing latency.

Variety

Big Data includes structured SQL databases, semi-structured JSON files, and unstructured data like video, audio, and text. A one-size-fits-all security policy does not work here.

Encryption in Transit

Protecting data as it moves between nodes, clusters, or users (typically using TLS/SSL).

Encryption at Rest

Safeguarding data stored on disks using strong encryption standards (like AES-256).

Data Masking & Tokenization

Replacing sensitive data (like credit card numbers or PII) with realistic but fake data or tokens before it enters the analytics pipeline so data scientists can work with it safely.

Role-Based Access Control (RBAC)

Assigning permissions based on organizational roles.

Attribute-Based Access Control (ABAC)

A finer-grained approach where access is determined by contextual attributes (e.g., "Allow access only if the user is a Data Analyst AND the request comes from an internal IP during working hours").

Node Authentication

Ensuring that only authorized servers/nodes can join the computing cluster (often managed via Kerberos).

The 26 topics define many more terms than these — the full set, with flashcards, is in the notes.

• Big Data Security — Bhavani Thuraisingham

  Comprehensive coverage of the field

• Database Security and Auditing — Hassan Afyouni

  Practical focus on database security mechanisms

• Read the Nigeria Data Protection Act — it is publicly available and short; exam questions reference it
• SQL injection from this course connects directly to web security in UUY-CYB 222 — study them together
• The cloud shared responsibility model is exam-critical: know clearly what the provider vs customer secures
• Use OWASP resources on SQL injection and data security — they are free and authoritative

What is CYB 224 at the University of Uyo?

CYB 224 (Information and Big Data Security) is a 2-unit, 200 Level course taught in the Second Semester at the University of Uyo, Akwa Ibom State, Nigeria. Big data fundamentals — characteristics, sources, platforms, and analytics — paired with the security side: information and big data security requirements, lifecycle security management, risk management, and cloud-based big data security.

What topics are covered in CYB 224?

The CYB 224 outline covers 8 topics: Big data fundamentals: small vs big data, characteristics (3Vs/6Vs), sources and formats; Big data technology, platforms, components, and governance; using the cloud for big data; Operational and analytical big data; business intelligence vs big data vs data mining; System and management view of information and big data security; Requirements for information and big data security; systems-design process and lifecycle security management; Basic policies on information security and methodologies; security risk management; Laws related to information security and management of operational systems; Applying machine learning and analytical tools to big data; cloud-based big data analysis.

What textbooks are recommended for CYB 224?

Recommended reading for CYB 224: Big Data Security by Bhavani Thuraisingham; Database Security and Auditing by Hassan Afyouni.

How do I pass CYB 224?

Read the Nigeria Data Protection Act — it is publicly available and short; exam questions reference it SQL injection from this course connects directly to web security in UUY-CYB 222 — study them together The cloud shared responsibility model is exam-critical: know clearly what the provider vs customer secures Use OWASP resources on SQL injection and data security — they are free and authoritative

Where can I find CYB 224 lecture notes and past questions?

Areté (https://www.aretecyb.tech/courses/cyb-224) carries 26 lecture-note topics, 187 practice questions, 102 past-paper questions, 2 recommended textbooks for CYB 224, plus an AI tutor that has read the outline. It is free for University of Uyo students — sign in with your email, no password.

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