CYB 2222 units200 LevelSecond Semester

Cybersecurity Innovation and New Technologies

B.Sc. Cybersecurity, University of Uyo

CYB 222 (Cybersecurity Innovation and New Technologies) is a 2-unit, 200 Level course taught in the Second Semester at the University of Uyo, Akwa Ibom State, Nigeria.

Emerging technologies and their cybersecurity implications: AI in security, blockchain, quantum computing threats to cryptography, zero-trust architecture, and the innovation lifecycle in the security industry.

On Areté, CYB 222 comes with

  • 12 lecture-note topics
  • 132 practice questions
  • 81 past-paper questions
  • 2 recommended textbooks
  • an AI tutor that has read this course’s outline and notes
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Course outline

  1. 01AI and machine learning in threat detection
  2. 02Blockchain technology and its security properties
  3. 03Quantum computing and the threat to current cryptography
  4. 04Zero-trust architecture: principles and implementation
  5. 05Security automation: SOAR platforms overview
  6. 06Emerging threat vectors: deepfakes, AI-generated attacks
  7. 07Security innovation lifecycle and technology adoption
  8. 08Open-source intelligence (OSINT) modern tools

What the CYB 222 lecture notes cover

  1. 01AI & Machine Learning in Threat Detection
  2. 02Zero Trust Architecture
  3. 03Post-Quantum Cryptography & Migration Frameworks
  4. 04Behavioral Biometrics & Continuous Authentication
  5. 05Blockchain Technology
  6. 06Cloud Native Security
  7. 07Privacy Enhancing Technologies (PET)
  8. 08Automated Vulnerability Research
  9. 09Agentic AI
  10. 10Deepfake Detection
  11. 11Secure Access Service Edge (SASE)
  12. 12Extended Detection and Response (XDR)

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

CYB 222 key concepts

Abstract
Traditional security relied on signature-based detection, matching known attack patterns against incoming data — an approach that fails against the rapid evolution of cyberattacks. AI and ML fundamentally change threat detection by letting systems learn from data, recognise behavioural anomalies, and adapt to emerging…
Supervised Learning
Trained on labelled normal/malicious data — spam filtering, malware classification, IDS, fraud. Algorithms: Decision Trees, Random Forest, SVM, Logistic Regression, Naive Bayes.
Unsupervised Learning
Finds hidden patterns in unlabelled data for anomaly detection and user-behaviour analytics. Techniques: K-Means, PCA, Autoencoders, density-based clustering.
Deep Learning
Neural networks for large, complex datasets — malware detection, traffic classification, advanced IDS. Models: CNN, RNN, LSTM, Transformers.
— Marsh
Stephen Paul Marsh formalises trust as a computational concept.
— Jericho Forum
Forms to address "de-perimeterisation" of the enterprise network.
— Kindervag
Coins "Zero Trust" at Forrester: trust itself is a vulnerability.
/ 2014 — Google BeyondCorp
Implements then publishes Zero Trust after Operation Aurora; becomes the enterprise blueprint.
— NIST SP 800-207
First comprehensive government-endorsed ZTA framework.
— Executive Order 14028
Mandates Zero Trust for all US federal agencies.
— CISA ZTMM v2.0
Zero Trust Maturity Model across five pillars.
Never Trust, Always Verify
Every request is authenticated and continuously, contextually re-verified — not a one-time login.
Least Privilege Access
Access is scoped, time-limited, and contextual, shrinking the blast radius of any compromise.
Assume Breach
Design as if attackers are already inside: encrypt everything, segment, monitor, and plan recovery.
Micro-Segmentation
Divide the network into isolated segments — down to individual workloads — to contain lateral movement.
Identity & Access Management (IAM)
Identity — not network location — is the primary security boundary; SSO, RBAC, ABAC, risk-based auth.
Multi-Factor Authentication (MFA)
Two or more factors (know / have / are); mandatory for sensitive access; adaptive to risk.
Zero Trust Network Access (ZTNA)
Replaces VPNs — grants access to specific resources, not the whole network.
Device Trust & EDR
Verifies device health and compliance before access; EDR/MDM monitor and can quarantine endpoints.
Data Security & Encryption
Classify data; encrypt in transit (TLS/SSL) and at rest; DLP and attribute-based access control.
SIEM & UEBA
Aggregate and correlate logs; ML baselines behaviour and flags anomalies — the monitoring backbone.
What Is a Quantum Computer?
A quantum computer is a different computational paradigm, exploiting superposition, entanglement and interference. Where a classical bit is 0 or 1, a qubit can be both at once, so n qubits represent 2ⁿ states simultaneously.
Analogy — The Maze
A classical computer searches a maze one path at a time: hit a dead end, backtrack, try again. A quantum computer, through superposition, effectively evaluates many paths at once and converges exponentially faster.
Mosca's Theorem — Quantifying the Urgency
If (Migration Time) + (Data Secrecy Lifetime) > (Years until Q-Day), the organisation is already at risk. Applied to UNIUYO: a 7-year migration plus a 20-year record-confidentiality requirement = 27 years of protection needed against an ~8-year window.
ML-KEM (FIPS 203)
Primary KEM replacing RSA/Diffie-Hellman; from CRYSTALS-Kyber; ML-KEM-768 recommended; ~17× faster than ECDH on a Cortex-M0+ with 94% less energy; live by default in Google Chrome TLS 1.3
ML-DSA (FIPS 204)
Primary signature replacing ECDSA/RSA; from CRYSTALS-Dilithium; signatures 2,420–4,627 bytes (38–72× larger than ECDSA); rejection sampling causes variable signing latency
SLH-DSA (FIPS 205)
Stateless hash-based signature from SPHINCS+; the suite's insurance policy; very large signatures (7,856–49,856 bytes); for low-frequency, high-security signing
Falcon (FIPS 206)
Compact lattice (NTRU) signature ~666 bytes — best for DNS; hard to implement (constant-time floating-point) and the highest side-channel risk

The 12 topics define many more terms than these — the full set, with flashcards, is in the notes.

Read the start of “AI & Machine Learning in Threat Detection”

Group 0 research project — by Amasi Rhema. An assigned project topic for CYB 222, examining how Artificial Intelligence (AI) and Machine Learning (ML) are transforming cybersecurity threat detection.

In brief

  • Signature-based detection only catches attacks it has already seen — zero-days, polymorphic malware and insider abuse walk straight past it
  • AI and ML detect by behaviour instead: baseline what normal looks like, flag the deviation, adapt in real time
  • Three ML families do the work — supervised (spam, malware classification), unsupervised (anomaly detection), and deep learning (CNN, RNN, LSTM)
  • Applied across IDS, malware and phishing detection, fraud, IoT monitoring and automated incident response
  • The cost: false positives, model bias, adversarial attacks, privacy exposure and heavy compute — which is why human oversight and Explainable AI remain essential

Abstract

Traditional security relied on signature-based detection, matching known attack patterns against incoming data — an approach that fails against the rapid evolution of cyberattacks. AI and ML fundamentally change threat detection by letting systems learn from data, recognise behavioural anomalies, and adapt to emerging threats in real time. The trade-off is a new set of challenges — false positives, adversarial attacks, privacy concerns, computational cost, and a lack of interpretability — which is why human oversight and ethical frameworks remain essential.

Why Traditional Detection Falls Short

  • Signature-based systems only catch previously known attacks
  • Zero-day attacks have no existing signature to match
  • Polymorphic malware constantly changes its signature
  • Credential abuse and insider threats look like legitimate activity
  • Malicious traffic hidden inside encryption evades inspection

Think of a hostel gateman holding a photo album of known troublemakers. Signature-based detection is him checking every face against the album — anyone not pictured strolls straight in. AI-based detection is the gateman who has watched the hostel for years: he doesn't need your photo to notice you carrying someone else's mattress out at 2 a.m. He knows what normal looks like, so the unusual stands out — even on a face he has never seen before.

That is the opening of topic 1 of 12. Sign in — free, no password — to read the rest, take the practice questions, and ask the AI tutor about any of it.

Read all 12 CYB 222 topics

Recommended textbooks

  • AI and Machine Learning for Coders — Laurence Moroney

    Accessible entry point to AI for non-ML specialists

  • Blockchain Basics — Daniel Drescher

    Clear, non-mathematical introduction to blockchain

How to pass CYB 222

  • Stay current — this course changes rapidly; subscribe to a cybersecurity newsletter (SANS, Dark Reading)
  • Zero trust ("never trust, always verify") is a major industry direction — understand it thoroughly
  • Quantum threats to RSA and AES connect directly to your Cryptography course in 300L
  • For exam essays, structure answers around: what the technology is, its security benefit, and its risk

CYB 222 — questions and answers

What is CYB 222 at the University of Uyo?
CYB 222 (Cybersecurity Innovation and New Technologies) is a 2-unit, 200 Level course taught in the Second Semester at the University of Uyo, Akwa Ibom State, Nigeria. Emerging technologies and their cybersecurity implications: AI in security, blockchain, quantum computing threats to cryptography, zero-trust architecture, and the innovation lifecycle in the security industry.
What topics are covered in CYB 222?
The CYB 222 outline covers 8 topics: AI and machine learning in threat detection; Blockchain technology and its security properties; Quantum computing and the threat to current cryptography; Zero-trust architecture: principles and implementation; Security automation: SOAR platforms overview; Emerging threat vectors: deepfakes, AI-generated attacks; Security innovation lifecycle and technology adoption; Open-source intelligence (OSINT) modern tools.
What textbooks are recommended for CYB 222?
Recommended reading for CYB 222: AI and Machine Learning for Coders by Laurence Moroney; Blockchain Basics by Daniel Drescher.
How do I pass CYB 222?
Stay current — this course changes rapidly; subscribe to a cybersecurity newsletter (SANS, Dark Reading) Zero trust ("never trust, always verify") is a major industry direction — understand it thoroughly Quantum threats to RSA and AES connect directly to your Cryptography course in 300L For exam essays, structure answers around: what the technology is, its security benefit, and its risk
Where can I find CYB 222 lecture notes and past questions?
Areté (https://www.aretecyb.tech/courses/cyb-222) carries 12 lecture-note topics, 132 practice questions, 81 past-paper questions, 2 recommended textbooks for CYB 222, 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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