Secure Computing Systems Assignment

Module Information

Module Code: 7018SCN Module Title: Secure Computing Systems Assignment Type: Coursework University: Coventry University

Project Overview

This project demonstrates the practical implementation of core cybersecurity concepts through the development of three Python-based command-line tools. The aim is to address real-world security challenges including secure authentication, threat detection through log analysis, and malware identification with digital forensics.

The tools are designed to simulate how security engineers protect systems, detect intrusions, and respond to potential threats in a controlled and safe environment.

Implemented Tasks

Task 1: Secure Authentication System

This tool provides a secure mechanism for user authentication by addressing common vulnerabilities found in poorly designed systems.

Key Features:

Passwords are securely stored using SHA-256 hashing
A unique 16-byte random salt is generated for each password using os.urandom()
Password strength is enforced using regular expressions
Minimum requirements:
- At least 12 characters
- Must include at least one number
- Must include at least one special character
Implements brute-force attack protection by introducing a 2-second delay (time.sleep) after failed login attempts

Security Benefit: This approach ensures that even if the database is compromised, attackers cannot easily reverse the stored passwords, significantly improving system security.

Task 2: SIEM Lite (Security Information and Event Management)

This tool simulates a basic SIEM system used in enterprise environments to monitor and detect suspicious activities in log files.

Key Features:

Parses Linux-style log files (e.g., SSH authentication logs)
Uses regular expressions (re.findall) to extract IP addresses
Detects suspicious patterns such as:
- "Failed password"
- "Invalid user"
Counts occurrences of each IP address
Uses dynamic thresholding via command-line arguments (sys.argv)
Exports detected malicious IP addresses into a structured JSON file

Security Benefit: This tool enables proactive detection of brute-force attacks and unauthorized access attempts, allowing faster incident response.

Task 3: Malware Analysis & Digital Forensics Tool

This tool safely scans files to identify malicious content without executing them, ensuring system safety.

Key Features:

Generates SHA-256 hash of files using chunk-based reading (4096 bytes) to prevent memory overload
Compares computed hashes with a predefined list of known malicious signatures
If a match is found:
- The file is securely moved to a QUARANTINE_VAULT directory using shutil.move()
Supports directory traversal using os.walk() to scan multiple files
Extracts EXIF metadata (such as GPS coordinates) from image files using the Pillow library

Security Benefit: This ensures safe malware detection and containment while also supporting forensic investigation through metadata analysis.

Requirements

Python 3.x installed
Required Python library:

pip install pillow

How to Run the Project

Task 1: Authentication

python task1.py

Task 2: SIEM Lite

python task2.py 3

(Replace 3 with your desired threshold value)

Task 3: Malware Analysis

python task3.py

Security Concepts Demonstrated

Cryptographic hashing and salting
Password policy enforcement
Brute-force attack mitigation
Log monitoring and anomaly detection
File hashing and signature-based malware detection
Digital forensics (EXIF metadata extraction)

Legal and Ethical Considerations

This project is developed strictly for educational purposes. The use of cybersecurity tools must comply with legal frameworks such as the UK Computer Misuse Act 1990, which prohibits unauthorized access to computer systems. Tools like Nmap or Metasploit must only be used with explicit permission and within defined rules of engagement.

Compliance and Standards

The authentication system aligns with:

GDPR (General Data Protection Regulation) by ensuring secure storage and processing of user credentials
ISO27001 by implementing access control, risk mitigation, and data protection practices

Author

Student Name: [Karmudi Rishith Reddy] Student ID: [16718535] Course: Secure Computing Systems University: Coventry University

karmudir/secure-computing-systems---assignement

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