Group 8 Social Coding

README.md

@@ -2,6 +2,16 @@
 
 This repository contains starter code and resources for Social Coding workshops covering 1-off fun subjects like generative art, coding competitions, code reading sessions or pseudocoding.
 
+**Group name: Salle 8**
+**Group members:** - Dadi Ishimwe
+              - Robel Mengsteab
+              - Safa Saber
+              - Louis Kervens Hubert
+              - M Jawid Mohseni
+              - Mohamed El - nageeb
+              - Franklyn Abanihe
+              - Mariia Ermishina
+
 ## Running Python Files in this Repository
 
 - **Running a plain Python script**:

challenges/challenge1.py

@@ -2,6 +2,16 @@
 """
 Created on Thu Jan 30 15:30:30 2025
 
+Group name: Salle 8
+Group members: Dadi Ishimwe
+              Robel Mengsteab
+              Safa Saber
+              Louis Kervens Hubert
+              M Jawid Mohseni
+              Mohamed El - nageeb
+              Franklyn Abanihe
+              Mariia Ermishina
+
 @author: somai
 Challenge 1: Detecting Suspicious Login Attempts
 Objective:
@@ -26,3 +36,22 @@
 Interpret what the final expression means in the context of allowing or blocking a login attempt.
 
 """
+#¬(A∧(B∨¬B))
+#¬(A∧(True))
+#¬(A∧(1))
+#¬(A)
+
+# the authentication system is blocked by incorrect login credentials and not whether the device is trusted or not.
+def check_login(A, B):
+    """
+    A: Correct credentials
+    B: Trusted device
+    Returns: Whether login should be blocked
+    """
+    return not A  # Simplified from ¬(A∧(B∨¬B))
+
+# Test cases
+print("Credentials correct, trusted device:", check_login(True, True))
+print("Credentials correct, untrusted device:", check_login(True, False))
+print("Credentials wrong, trusted device:", check_login(False, True))
+print("Credentials wrong, untrusted device:", check_login(False, False))

challenges/challenge2.py

@@ -2,6 +2,17 @@
 """
 Created on Thu Jan 30 15:30:30 2025
 
+Group name: Salle 8
+Group members: Dadi Ishimwe
+              Robel Mengsteab
+              Safa Saber
+              Louis Kervens Hubert
+              M Jawid Mohseni
+              Mohamed El - nageeb
+              Franklyn Abanihe
+              Mariia Ermishina
+
+
 @author: somai
 Challenge 2: Digital Access Control
 Objective
@@ -29,3 +40,26 @@
 # -*- coding: utf-8 -*-
 
 """
+finance_access = {"E0435", "E1021", "E3098", "E7642", "E8873", "E6590"}
+tech_access = {"E7642", "E8873", "E6590", "E9812", "E4520"}
+
+# 1 - finance_access OR tech_access
+at_least_one = finance_access | tech_access
+
+# 2 - Team = finance_access AND tech_access 
+both_access = finance_access & tech_access
+
+# 3 - finance_access_exclusive {"E0435", "E1021", "E3098"}  
+exclusive_finance = finance_access - tech_access
+
+# 3 - tech_access_exclusive {"E9812", "E4520"}
+exclusive_tech = tech_access - finance_access
+
+# 4 - no_access = new_employee E9999
+all_employees = {"E0001", "E9999", "E0435", "E1021", "E3098", "E7642", "E8873", "E6590", "E9812", "E4520"}
+no_access = all_employees - (finance_access | tech_access)
+
+# 5 - overlap_risk = finance_access AND tech_access
+overlap = finance_access & tech_access
+
+# The should not share common dates and Grant employees only the access they absolutely need to perform their job duties.

challenges/challenge3.py

@@ -3,6 +3,17 @@
 Created on Thu Jan 30 15:30:30 2025
 
 @author: somai
+
+Group name: Salle 8
+Group members: Dadi Ishimwe
+              Robel Mengsteab
+              Safa Saber
+              Louis Kervens Hubert
+              M Jawid Mohseni
+              Mohamed El - nageeb
+              Franklyn Abanihe
+              Mariia Ermishina
+
 Challenge 3: Access Visualization
 Objective
 Develop a visual representation of the company's access control system to identify patterns, overlaps, and security risks.
@@ -20,3 +31,23 @@
 Are there risks? Indicate employees who might be exposed to unnecessary data.
 Your output should visually highlight these relationships without explicitly listing them in a simple table or list. Think beyond just printing data—use a format that helps detect patterns at a glance.
 """
+import matplotlib.pyplot as plt
+from matplotlib_venn import venn2
+
+# Employee access data
+finance_access = {"E0435", "E1021", "E3098", "E7642", "E8873", "E6590"}
+tech_access = {"E7642", "E8873", "E6590", "E9812", "E4520"}
+all_employees = {"E0001", "E9999", "E0435", "E1021", "E3098", "E7642", "E8873", "E6590", "E9812", "E4520"}
+
+# Determine employee groups
+only_finance = finance_access - tech_access
+only_tech = tech_access - finance_access
+both_access = finance_access & tech_access
+no_access = all_employees - (finance_access | tech_access)
+
+# Create Venn diagram
+plt.figure(figsize=(6, 6))
+venn = venn2(subsets=(len(only_finance), len(only_tech), len(both_access)), set_labels=("Finance Access", "Tech Access"))
+plt.title("Employee Access Visualization")
+plt.annotate(f"Employees with no access: {len(no_access)}", xy=(0.5, 0.1), xycoords="axes fraction", ha="center", fontsize=12)
+plt.show()