Security awareness and affective feedback:categorical behaviour vs. reported behaviour

A lack of awareness surrounding secure online behaviour can lead to end-users, and their personal details becoming vulnerable to compromise. This paper describes an ongoing research project in the field of usable security, examining the relationship between end-user-security behaviour, and the use of affective feedback to educate end-users. Part of the aforementioned research project considers the link between categorical information users reveal about themselves online, and the information users believe, or report that they have revealed online. The experimental results confirm a disparity between information revealed, and what users think they have revealed, highlighting a deficit in security awareness. Results gained in relation to the affective feedback delivered are mixed, indicating limited short-term impact. Future work seeks to perform a long-term study, with the view that positive behavioural changes may be reflected in the results as end-users become more knowledgeable about security awareness

Supervised Machine Learning Techniques, Cybersecurity Habits and Human Generated Password Entropy for Hacking Prediction

Attempts to steal information through the hacking of online accounts or passwords violations are becoming more common. The human factor is involved in most cyber-attacks. A way to solve these human oversights is to start using artificial intelligence, delegating some human decisions in the machines, but these innovations also have much to improve. Human judgment is still necessary to fill the gap between the capabilities of technology and our needs. This is where conscious security habits play a differentiating role between being or not the victim of a cyber-attack. This study describes how machine learning techniques can be used to model predictions that allow the anticipation of a hacking event taking into account password entropy and security habits. Prediction models are created and trained using decision tree techniques, multilayer perceptron, and Naïve Bayes. The efficiency of these models is contrasted to determine which of the models is more efficient for the case under study

Lost in Disclosure: On the Inference of Password Composition Policies

Large-scale password data breaches are becoming increasingly commonplace, which has enabled researchers to produce a substantial body of password security research utilising real-world password datasets, which often contain numbers of records in the tens or even hundreds of millions. While much study has been conducted on how password composition policies (sets of rules that a user must abide by when creating a password) influence the distribution of user-chosen passwords on a system, much less research has been done on inferring the password composition policy that a given set of user-chosen passwords was created under. In this paper, we state the problem with the naive approach to this challenge, and suggest a simple approach that produces more reliable results. We also present pol-infer, a tool that implements this approach, and demonstrates its use in inferring password composition policies.Comment: 6 pages, 8 figures, 7 table

Nudging folks towards stronger password choices:providing certainty is the key

Persuading people to choose strong passwords is challenging. One way to influence password strength, as and when people are making the choice, is to tweak the choice architecture to encourage stronger choice. A variety of choice architecture manipulations i.e. “nudges”, have been trialled by researchers with a view to strengthening the overall password profile. None has made much of a difference so far. Here we report on our design of an influential behavioural intervention tailored to the password choice context: a hybrid nudge that significantly prompted stronger passwords.We carried out three longitudinal studies to analyse the efficacy of a range of “nudges” by manipulating the password choice architecture of an actual university web application. The first and second studies tested the efficacy of several simple visual framing “nudges”. Password strength did not budge. The third study tested expiration dates directly linked to password strength. This manipulation delivered a positive result: significantly longer and stronger passwords. Our main conclusion was that the final successful nudge provided participants with absolute certainty as to the benefit of a stronger password, and that it was this certainty that made the difference

TOWARDS ASSESSING PASSWORD WORKAROUNDS AND PERCEIVED RISK TO DATA BREACHES FOR ORGANIZATIONAL CYBERSECURITY RISK MANAGEMENT TAXONOMY

Cybersecurity involves a broad range of techniques, including cyber-physical, managerial, and technical, while authentication provides a layer of protection for Information Systems (IS) against data breaches. The recent COVID-19 pandemic brought a tsunami of data breach incidents worldwide. Authentication serves as a mechanism for IS against unauthorized access utilizing various defense techniques, with the most popular and frequently used technique being passwords. However, the dramatic increase of user accounts over the past few decades has exposed the realization that technological measures alone cannot ensure high level of IS security; this leaves the end-users holding a critical role in protecting their organization and personal information. Despite users being more aware of password entropy, users still often participate in deviant password behaviors also known as ‘password workarounds’ or ‘shadow security’. These deviant password behaviors can put individuals and organizations at risk resulting in data privacy issues, data loss, and ultimately a data breach incident. In this paper, we outline a research-in-progress study to build a risk taxonomy for organizations based on the to identify the risks associated with deviant password behaviors technique based on the constructs of users’ perceived cybersecurity risk of data breaches resulting from PassWord WorkArounds (PWWA) techniques. Additionally, this study aims to empirically assess significant mean difference between Subject Matter Experts (SMEs) and employees on their perceived cybersecurity risk of data breaches resulting from the deviant password behaviors and frequency of PWWA techniques usage

Why Do People Adopt, or Reject, Smartphone Password Managers?

People use weak passwords for a variety of reasons, the most prescient of these being memory load and inconvenience. The motivation to choose weak passwords is even more compelling on Smartphones because entering complex passwords is particularly time consuming and arduous on small devices. Many of the memory- and inconvenience-related issues can be ameliorated by using a password manager app. Such an app can generate, remember and automatically supply passwords to websites and other apps on the phone. Given this potential, it is unfortunate that these applications have not enjoyed widespread adoption. We carried out a study to find out why this was so, to investigate factors that impeded or encouraged password manager adoption. We found that a number of factors mediated during all three phases of adoption: searching, deciding and trialling. The study’s findings will help us to market these tools more effectively in order to encourage future adoption of password managers

Interpretable Probabilistic Password Strength Meters via Deep Learning

Probabilistic password strength meters have been proved to be the most accurate tools to measure password strength. Unfortunately, by construction, they are limited to solely produce an opaque security estimation that fails to fully support the user during the password composition. In the present work, we move the first steps towards cracking the intelligibility barrier of this compelling class of meters. We show that probabilistic password meters inherently own the capability of describing the latent relation occurring between password strength and password structure. In our approach, the security contribution of each character composing a password is disentangled and used to provide explicit fine-grained feedback for the user. Furthermore, unlike existing heuristic constructions, our method is free from any human bias, and, more importantly, its feedback has a clear probabilistic interpretation. In our contribution: (1) we formulate the theoretical foundations of interpretable probabilistic password strength meters; (2) we describe how they can be implemented via an efficient and lightweight deep learning framework suitable for client-side operability.Comment: An abridged version of this paper appears in the proceedings of the 25th European Symposium on Research in Computer Security (ESORICS) 202