SRT521 - Advanced Data Analysis

Outline info
Semester
School
Last revision date 2020-10-30 11:38:37.899
Last review date 2020-10-30 11:38:37.899


Subject Title
Advanced Data Analysis

Subject Description
An information security professional has the responsibility to assure that the world is a safe space. This can only be accomplished with an understanding of what is happening in the increasingly complex systems upon which business and society have come to depend. This understanding requires the analysis of a vast amount of data in order to derive meaning from these systems. Fortunately, the increasing power of distributed technology also provides us with the tools to do so. Distributed systems allow us to create scalable tools which leverage the power of the cloud, machine learning, and artificial intelligence to collect, parse and analyze previously inaccessible amounts of data to find patterns and meaning in the complex systems which control our world. This course introduces the student to these technologies and teaches them how to reduce risk and make our systems more secure.

Credit Status
one credit

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

Upon successful completion of the course the student will be able to apply the concepts of data driven security to:

  • Examine security events to discover breaches and incidents
  • Inspect large datasets to gain insight to the data
  • Inspect networked systems to discover security vulnerabilities
  • Analyze user behaviour to identify malicious actors
  • As software runtime performance metrics to discover malicious behaviour
  • Inspect data generated by complex systems to obtain situational awareness
 

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.

    •  Show respect for diverse opinions, values, belief systems, and contributions of others.

    •  Interact with others in groups or teams in ways that contribute to effective working relationships and the achievement of goals.

    •  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: http://www.senecapolytechnic.ca/about/policies/academic-integrity-policy.html 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 http://open2.senecac.on.ca/sites/academic-integrity/for-students to understand and learn more about how to prepare and submit work so that it supports academic integrity, and to avoid academic misconduct.

Discrimination/Harassment
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 student.conduct@senecapolytechnic.ca.

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.

Camera Use and Recordings - Synchronous (Live) Classes
Synchronous (live) classes may be delivered in person, in a Flexible Learning space, or online through a Seneca web conferencing platform such as MS Teams or Zoom. Flexible Learning spaces are equipped with cameras, microphones, monitors and speakers that capture and stream instructor and student interactions, providing an in-person experience for students choosing to study online.

Students joining a live class online may be required to have a working camera in order to participate, or for certain activities (e.g. group work, assessments), and high-speed broadband access (e.g. Cable, DSL) is highly recommended. In the event students encounter circumstances that impact their ability to join the platform with their camera on, they should reach out to the professor to discuss. Live classes may be recorded and made available to students to support access to course content and promote student learning and success.

By attending live classes, students are consenting to the collection and use of their personal information for the purposes of administering the class and associated coursework. To learn more about Seneca's privacy practices, visit Privacy Notice.

Prerequisite(s)
n/a

Topic Outline

  1. Introduction to Big Data Analytics
    1. distributed everything
    2. Netflix, a use case
  2. distributed NoSQL databases
    1. Realizing the Container Has Constraints
      1. Constrained by Schema
      2. Constrained by Storage
      3. Constrained by RAM
      4. Constrained by Data
    2. Alternative Data Stores
      1. BerkeleyDB
      2. Redis
      3. Hive
      4. MongoDB
      5. Special Purpose Databases
  3. the ELK stack
    1. elastic search
    2. logstash
    3. kibana
    4. putting it together
  4. Machine Learning
    1. Intro
    2. Algorithms
      1. Developing a Machine Learning Algorithm
      2. Validating the Algorithm
      3. Implementing the Algorithm
    3. Benefiting from Machine Learning
      1. Answering Questions with Machine Learning
      2. Measuring Good Performance
      3. Selecting Features
      4. Validating Your Model
    4. Specific Learning Methods
      1. Supervised
      2. Unsupervised
    5. Hands On: Clustering Breach Data
      1. Multidimensional Scaling on Victim Industries
      2. Hierarchical Clustering on Victim Industries
  5. Artificial Intelligence
  6. Threat Intelligence

Mode of Instruction
Classroom and Lab

Prescribed Texts
Data Driven Security: Analysis, Visualization and Dashboards
by Jay Jacobs, Bob Rudis
Publisher: Wiley (2014)
ISBN-13: 978-1-118-79372-5
 
Malware Data Science
By Joshua Saxe, Hillary Sanders
Publisher: No Starch Press (2018)
ISBN 978-1-59327-859-5
 
Machine Learning and Security: Protecting Systems with Data and Algorithms 1st Edition
by Clarence Chio, David Freeman
Publisher: O'Reilly Media; 1 edition (February 17, 2018)
ISBN-13: 978-1491979907

Reference Material
n/a

Required Supplies
n/a

Student Progression and Promotion Policy
http://www.senecapolytechnic.ca/about/policies/student-progression-and-promotion-policy.html

Grading Policyhttp://www.senecapolytechnic.ca/about/policies/grading-policy.html)

A+90%  to  100%
A80%  to  89%
B+75%  to  79%
B70%  to  74%
C+65%  to  69%
C60%  to  64%
D+55%  to  59%
D50%  to  54%
F0%    to  49% (Not a Pass)
OR
EXCExcellent
SATSatisfactory
UNSATUnsatisfactory

For further information, see a copy of the Academic Policy, available online (http://www.senecapolytechnic.ca/about/policies/academics-and-student-services.html) or at Seneca's Registrar's Offices.(https://www.senecapolytechnic.ca/registrar.html)


Modes of Evaluation
Labs (minimum of 8)                               25%
Assignments (minimum of 2)                  35%
Project                                                    40%

 

Approved by: Suzanne Abraham