Enhancing Password Security With Machine Learning-Based Strength Assessment Techniques

Introduction

In an era dominated by digital interconnectedness, the security of sensitive information has never been more critical. Ensuring the protection of personal and organizational data hinges on the strength of passwords—those digital keys that serve as the initial line of defence against potential security breaches. Passwords, being ubiquitous in today's digital landscape, have prompted a relentless battle between those seeking to safeguard their accounts and the ever-evolving tactics of malicious actors. The foundation of this battle is rooted in the concept of password strength. A strong password is an essential aspect of digital security, as it presents a formidable barrier against unauthorized access. Conversely, weak, or easily guessable passwords can expose individuals and organizations to the vulnerabilities of cyber threats.

In this context, machine learning techniques have emerged as a powerful tool for enhancing password security. The ability of machine learning models to analyse patterns, discern anomalies, and make data-driven predictions holds promise for improving the robustness of password systems. By implementing machine learning-based strength assessment techniques, we gain an opportunity to bolster digital security on a broad scale. This chapter delves into the importance of enhancing password security through the utilization of machine learning-based strength assessment techniques. We will explore the methods and strategies employed in this endeavour, focusing on algorithms like Support Vector Machines (SVM), k-Nearest Neighbours (KNN), AdaBoost, Decision Trees, and Random Forest. Through these techniques, we aim to not only evaluate password strength but also to understand their potential for real-world application. In a digital landscape where the stakes are higher than ever, the quest for a more secure future begins with an exploration of how machine learning can fortify the very first line of defence: the password.