Difference between revisions of "Fall 2025: Cyber Forensics"

From MKWiki
Jump to navigation Jump to search
Line 16: Line 16:
 
!Readings
 
!Readings
 
|-
 
|-
−
| style="width: 12%; " | Unit 1 | style="width: 60%" |  '''''Introduction''''': 1.1 - What Is Data Mining? 1.2 Challenges 1.3 Data Mining Origins 1.4 Data Mining Tasks
+
| style="width: 12%; " | Unit 1 | style="width: 60%" |  '''''Digital Forensics''''':  
 
| style="width: 15%" | [http://mkbhandari.com/mkwiki/data/spring2023/DM/1Intro.pdf '''1Intro.pdf''']   
 
| style="width: 15%" | [http://mkbhandari.com/mkwiki/data/spring2023/DM/1Intro.pdf '''1Intro.pdf''']   
 
| Chapter 1 (CB1)
 
| Chapter 1 (CB1)
 
|-
 
|-
 
| Unit 2  
 
| Unit 2  
−
|  '''''Data mining techniques''''': 2.1- Types of data, 2.2 – Data Quality, 2.3.1 Aggregation, 2.3.2 Sampling, 2.3.3 Dimensionality reduction – upto pg 51, 2.3.4 Feature subset selection upto pg 52, 2.4.5 Feature creation upto pg 55, 2.3.6 Discretization upto pg 59, 2.3.7 variable transformations 2.4.3 Dissimilarity among data objects 2.4.4 similarity among data objects
+
|  '''''Windows OS Forensics:''''':  
 
|  [http://mkbhandari.com/mkwiki/data/spring2023/DM/2DMT.pdf '''2DMT.pdf''']   
 
|  [http://mkbhandari.com/mkwiki/data/spring2023/DM/2DMT.pdf '''2DMT.pdf''']   
 
| Chapter 2 (CB1)
 
| Chapter 2 (CB1)
 
|-
 
|-
 
| Unit 3  
 
| Unit 3  
−
|  '''''Association Rules''''': 6.1-Problem definition, 6.2-Frequent itemset generation, 6.3-Rule generation till Pg 351
+
|  '''''Evidence Recovery:''''':  
 
|  [http://mkbhandari.com/mkwiki/data/spring2023/DM/3AR.pdf '''3AR.pdf''']   
 
|  [http://mkbhandari.com/mkwiki/data/spring2023/DM/3AR.pdf '''3AR.pdf''']   
 
| Chapter 6 (CB1)
 
| Chapter 6 (CB1)
 
|-
 
|-
 
| Unit 4
 
| Unit 4
−
|  '''''Classification: Basic Concepts and Techniques''''': 4.1 – Preliminaries, 4.2 – General Approach to Solving a Classification Problem, 4.3 Decision Tree Induction (Till Pg. 165), 4.5 –Evaluating the Performance of a Classifier
+
|  '''''Investigation:''''':  
 
|  [http://mkbhandari.com/mkwiki/data/spring2023/DM/4CL.pdf '''4Classification.pdf''']   
 
|  [http://mkbhandari.com/mkwiki/data/spring2023/DM/4CL.pdf '''4Classification.pdf''']   
 
| Chapter 4 (CB1)
 
| Chapter 4 (CB1)
 
|-
 
|-
 
| Unit 5
 
| Unit 5
−
|  '''''Classification: Alternative Techniques''''': 5.1 – Rule Based Classifier (upto page 212),5.2 – Nearest Neighbor Classifiers, 5.3–Bayesian Classifiers (Complete for discrete data and only introduction of Bayes classifier for continuous attributes) till pg. 233, 5.7.1 –Alternative Metrics
+
|  '''''Cyber Crimes and Cyber Laws:''''':  
 
|  [https://www-users.cse.umn.edu/~kumar001/dmbook/index.php#item4 Read from Authors' web page]   
 
|  [https://www-users.cse.umn.edu/~kumar001/dmbook/index.php#item4 Read from Authors' web page]   
 
| Chapter 5 (CB1)
 
| Chapter 5 (CB1)
−
|-
 
−
| Unit 6 / Chapter 8
 
−
|  '''''Clustering''''': 8.1 Basic concepts of clustering analysis, 8.2 K-Means (8.2.1-8.2.5 except 8.2.3), 8.3 Agglomerative Hierarchical Clustering (except pg 522-524), 8.4 DBSCAN
 
−
|  [http://mkbhandari.com/mkwiki/data/spring2023/DM/5CA.pdf '''5CA.pdf'''] 
 
−
| Chapter 8 (CB1)
 
 
|}
 
|}
  

Revision as of 21:09, 17 August 2025

Logistics

  • Class Timings: Wednesdays 1:00 pm - 3:00 pm (5th and 6th slot)and Thursdays 10:45 am - 12:45 pm (3rd and 4th slot)
  • Classroom: R33
  • Lab Timings: Mondays 8:45 am - 12:45 pm (1st - 4thslots)
  • Labs: CS Lab 5

Course Overview

  • As per the Delhi University Course Guidelines

Lectures

Lecture Topic Lecture Slides Readings
Unit 1 | style="width: 60%" | Digital Forensics: 1Intro.pdf Chapter 1 (CB1)
Unit 2 Windows OS Forensics:: 2DMT.pdf Chapter 2 (CB1)
Unit 3 Evidence Recovery:: 3AR.pdf Chapter 6 (CB1)
Unit 4 Investigation:: 4Classification.pdf Chapter 4 (CB1)
Unit 5 Cyber Crimes and Cyber Laws:: Read from Authors' web page Chapter 5 (CB1)

Assignments and Tests

Class Assignments

  • Assignment No. 1,
  • Assignment No. 2,

Tests and Quizzes

  • Test 1 :
  • Test 2 :

Resources

Course Books:

  • CB1: Introduction to Data Mining, Pang-Ning Tan, Michael Steinbach, Vipin Kumar, Pearson Education.

References:

  • R2: Data Mining: Concepts and Techniques, 3nd edition,Jiawei Han and Micheline Kamber.
  • R3: Data Mining: A Tutorial Based Primer, Richard Roiger, Michael Geatz, Pearson Education 2003.
  • R4: Introduction to Data Mining with Case Studies, G.K. Gupta, PHI 2006.
  • R5: Insight into Data mining: Theory and Practice, Soman K. P., DiwakarShyam, Ajay V., PHI 2006
Retrieved from "http://www.mkbhandari.com/mkwiki/index.php?title=Fall_2025:_Cyber_Forensics&oldid=2596"