Optimization of Kernel-Level Security Mechanisms Using the Grey Wolf Optimizer (GWO) Algorithm

Introduction

This project focuses on optimizing kernel-level security mechanisms using the Grey Wolf Optimizer (GWO) algorithm. Kernel-level security mechanisms, such as Access Control Lists (ACLs), Security-Enhanced Linux (SELinux) policies, and AppArmor profiles, are critical for protecting the core of operating systems from various types of attacks. Efficient security mechanisms can significantly enhance system security and performance.

Problem Statement

Traditional kernel-level security mechanisms can be challenging to configure optimally. The complexity of these configurations often leads to inefficiencies and vulnerabilities. There is a need for an advanced optimization method to improve the configuration of these security mechanisms, balancing security and performance.

Objective

The main objective of this project is to develop and implement an optimization model for kernel-level security mechanisms using the Grey Wolf Optimizer (GWO) algorithm. The project aims to evaluate the performance improvements of the GWO-optimized security mechanisms in terms of security effectiveness and system performance.

Solution

System Description

• Kernel-Level Security Mechanisms: Includes mechanisms like Access Control Lists (ACLs), Security-Enhanced Linux (SELinux) policies, and AppArmor profiles.
• Components: Kernel modules, security policies, access control rules.

GWO Algorithm

The GWO algorithm is inspired by the leadership hierarchy and hunting mechanism of grey wolves. It involves three main phases:

1. Initialization: Randomly generate initial configurations for the security mechanisms (wolves).
2. Hunting: Evaluate the configurations using the objective function (security effectiveness, performance overhead).
3. Encircling and Attacking Prey: Update and refine the configurations iteratively to converge on the optimal setup.

Objective Function

The objective function combines security effectiveness and performance overhead:

def objective_function(config):
    apply_configuration(config)
    security_score = measure_security_effectiveness()
    performance_score = measure_performance_overhead()
    combined_score = performance_score - security_score
    return combined_score