SRT411 - Security Arts: Digital Data Analysis

Outline info
Last revision date Sep 24, 2018 12:41:02 AM
Last review date Dec 3, 2018 12:17:52 AM

Subject Title
Security Arts: Digital Data Analysis

Subject Description

This course is an introduction to the digital data analysis for security students. We will look at capturing data from appropriate data sources (such as packet captures, log files, IDS systems, and configuration files) then analyzing and displaying this data, using visualization techniques, in ways that will help detect/mitigate threats or help fulfill compliance requirements. Emphasis is placed on using the applicable tools for data analysis and statistical review.

Credit Status
1 credit in the IFS program.

Learning Outcomes
Upon successful completion of this subject the student will be able to:

  •     Identify and select appropriate digital data sources given a set of requirements
  •     Describe the basics of visualization theory
  •     Configure logging and other data sources to capture required data given a set of requirements
  •     Design and develop strategies for performing statistical analysis of digital data
  •     Analyze data streams using statistical reasoning and appropriate statistical tools to produce meaningful information
  •     Display information using various visualization techniques
  •     Determine and choose the most appropriate technique to display information

Essential Employability Skills
Communicate clearly, concisely and correctly in the written, spoken and visual form that fulfils the purpose and meets the needs of the audience.

Respond to written, spoken, or visual messages in a manner that ensures effective communication.

Execute mathematical operations accurately.

Apply a systematic approach to solve problems.

Use a variety of thinking skills to anticipate and solve problems.

Locate, select, organize, and document information using appropriate technology and information systems.

Analyze, evaluate, and apply relevant information from a variety of sources.

Manage the use of time and other resources to complete projects.

Take responsibility for one's own actions, decisions, and consequences.

Academic Integrity
Seneca upholds a learning community that values academic integrity, honesty, fairness, trust, respect, responsibility and courage. These values enhance Seneca's commitment to deliver high-quality education and teaching excellence, while supporting a positive learning environment. Ensure that you are aware of Seneca's Academic Integrity Policy which can be found at: Review section 2 of the policy for details regarding approaches to supporting integrity. Section 2.3 and Appendix B of the policy describe various sanctions that can be applied, if there is suspected academic misconduct (e.g., contract cheating, cheating, falsification, impersonation or plagiarism).

Please visit the Academic Integrity website to understand and learn more about how to prepare and submit work so that it supports academic integrity, and to avoid academic misconduct.

All students and employees have the right to study and work in an environment that is free from discrimination and/or harassment. Language or activities that defeat this objective violate the College Policy on Discrimination/Harassment and shall not be tolerated. Information and assistance are available from the Student Conduct Office at

Accommodation for Students with Disabilities
The College will provide reasonable accommodation to students with disabilities in order to promote academic success. If you require accommodation, contact the Counselling and Accessibility Services Office at ext. 22900 to initiate the process for documenting, assessing and implementing your individual accommodation needs.

SPR300, SRT311, RIS320

Topic Outline

  • Data Visualization 10%
    •     security vizualization
    •     vizualization theory
  • Data sources - 10%
    •     common sources of security data
    •     system logs
    •     network traffic flow
    •     firewalls
    •     IDS systems
    •     Passive network analysis
    •     operating systems
    •     applications
  • Graphing and Charting - 25%
    •     Graph properties
    •     Picturing distributions of data
    •     Graphics in the media
    •     Types of graphs: bar charts, pie charts, histograms, box plots, scatter plots, parallel coordinates, link graphs, maps, treemaps
    •     Choosing the right graph
  • Analyzing data - 25%
    •     Sampling methods
    •     Statistical modeling
    •     Statistical tools
    •     Data aggregation
  •     Data Distributions
  • Analysis scenarios - 20%
    •     Historical vs real-time
    •     Perimeter threat
    •     Intrusion detection
    •     Email server
    •     Social network
    •     Compliance
    •     risk management
    •     regulations, frameworks
    •     logging requirements
    •     Insider threats
    •     types
    •     detection
  • Data visualization tools - 10%
    •     open source tools
    •     commercial tools
    •     mitigation

Mode of Instruction

4 hours activity-based learning per week.

Prescribed Texts
Data-Driven Security: Analysis, Visualization and Dashboards
By Jay Jacobs, Bob Rudis
Publisher: Wiley
ISBN 978-1-118-79372-5

Network Security Through Data Analysis: Building Situational Awareness
By Michael S Collins
Publisher: O'Reilly Media

Reference Material

See class website

Required Supplies

  • Removable drive

Student Progression and Promotion Policy
To obtain a credit in this subject, a student must:

  •     Satisfactorily complete all assignments
  •     Pass the weighted average of all assessments
  •     Pass the final exam
  •     Pass the weighted average of the exam and tests

Grading Policy
A+ 90%  to  100%
A 80%  to  89%
B+ 75%  to  79%
B 70%  to  74%
C+ 65%  to  69%
C 60%  to  64%
D+ 55%  to  59%
D 50%  to  54%
F 0%    to  49% (Not a Pass)
EXC Excellent
SAT Satisfactory
UNSAT Unsatisfactory

For further information, see a copy of the Academic Policy, available online ( or at Seneca's Registrar's Offices.

Modes of Evaluation

Projects (minimum of 2) 30%
Labs (minimum of 5) 20%
Tests (minimum of 2) 20%
Final Exam 30%

Approved by: Mary-Lynn Manton