Weak Points

Question 1

Gantt charts two strengths and weaknesses

Answer 1

Benefits include visualisation, planning, scheduling, and tracking progress.

drawbacks include limited flexibility, complexity, and error-prone manual updates.

Question 2

National Intelligence Model

Answer 2

Intelligence-led policing
Used by law enforcement
Created in early 2000s

Question 3

Probably Yardstick

Answer 3

Remote chance 5% or less
Highly Unlikely 10-20%
Unlikely 25%-35%
Realistic Possibility 40%- 50%
Likely or Probable 55%-75%
High Likely 80%-90%
Almost Certain 95% or higher

Question 4

Traffic Light Protocol

Answer 4

RED
No disclosure,
No dissemination,
Only participating groups have access,
AMBER
Limited Disclosure
shared with some members or participants of org or community
Additional restrictions can be made
GREEN
Community-Wide Disclosure
It cannot be published publicly on the internet
WHITE
Unlimited Disclosure
Shared with everyone
Copyright laws are applicable

Question 5

Hidden Markov Model

Answer 5

A class of probabilistic graphical model

allow us to predict a sequence of unknown (hidden) variables from a set of observed variables

Question 6

P-value

Answer 6

The p-value is a number, calculated from a statistical test, that describes how likely you are to have found a particular set of observations if the null hypothesis were true. P values are used in hypothesis testing to help decide whether to reject the null hypothesis.

Question 7

P-value statistics

Answer 7

A p-value less than 0.05 is typically considered to be statistically significant, in which case the null hypothesis should be rejected.

A p-value greater than 0.05 means that deviation from the null hypothesis is not statistically significant, and the null hypothesis is not rejected.

Question 8

Bayesian Model

Answer 8

Used for hypothesis testing
Used to represent and reason about uncertainty,
and to incorporate prior knowledge

Question 9

Unsupervised Learning examples (3)

Answer 9

Exploratory analysis- discovering hidden data patterns
Clustering- grouping unlabelled data based on similarities or differences (Social Network Analysis)
Association- Identifying dependencies and occurrences
Dimensionality reduction- Reducing data, image/video processing

Question 10

Supervised Machine Learning (5)

Answer 10

Classification using predefined classes or categories
Email spam detection
Language detection
Recognition
Fraud detection

Question 11

Pyramid of Pain- top to bottom

Answer 11

TTPs
Tools
Network/hosts artifacts
Domain names
IP address
Hash values