Text-based passwords remain the dominant authentication method in computer systems, despite significant advancement in attackers' capabilities to perform password cracking. In response to this threat, password composition policies have grown increasingly complex. However, there is insufficient research defining metrics to characterize password strength and evaluating password-composition policies using these metrics. In this paper, we describe an analysis of 12,000 passwords collected under seven composition policies via an online study. We develop an efficient distributed method for calculating how effectively several heuristic password-guessing algorithms guess passwords. Leveraging this method, we investigate (a) the resistance of passwords created under different conditions to password guessing; (b) the performance of guessing algorithms under different training sets; (c) the relationship between passwords explicitly created under a given composition policy and other passwords that happen to meet the same requirements; and (d) the relationship between guessability, as measured with password-cracking algorithms, and entropy estimates. We believe our findings advance understanding of both password-composition policies and metrics for quantifying password security.
Despite considerable research on passwords, empirical studies of password strength have been limited by lack of access to plaintext passwords, small data sets, and password sets specifically collected for a research study or from low-value accounts. Properties of passwords used for high-value accounts thus remain poorly understood.We fill this gap by studying the single-sign-on passwords used by over 25,000 faculty, staff, and students at a research university with a complex password policy. Key aspects of our contributions rest on our (indirect) access to plaintext passwords. We describe our data collection methodology, particularly the many precautions we took to minimize risks to users. We then analyze how guessable the collected passwords would be during an offline attack by subjecting them to a state-of-the-art password cracking algorithm. We discover significant correlations between a number of demographic and behavioral factors and password strength. For example, we find that users associated with the computer science school make passwords more than 1.8 times as strong as those of users associated with the business school. In addition, we find that stronger passwords are correlated with a higher rate of errors entering them.We also compare the guessability and other characteristics of the passwords we analyzed to sets previously collected in controlled experiments or leaked from low-value accounts. We find more consistent similarities between the university passwords and passwords collected for research studies under similar composition policies than we do between the university passwords and subsets of passwords leaked from low-value accounts that happen to comply with the same policies.
CollectionAcquisition a b s t r a c tThe Android platform has been deployed across a wide range of devices, predominately mobile phones, bringing unprecedented common software features to a diverse set of devices independent of carrier and manufacturer. Modern digital forensics processes differentiate collection and analysis, with collection ideally only occurring once and the subsequent analysis relying upon proper collection. After exploring special device boot modes and Android's partitioning schema we detail the composition of an Android bootable image and discuss the creation of such an image designed for forensic collection. The major contribution of this paper is a general process for data collection of Android devices and related results of experiments carried out on several specific devices.
The large amounts of malware, and its diversity, have made it necessary for the security community to use automated dynamic analysis systems. These systems often rely on virtualization or emulation, and have recently started to be available to process mobile malware. Conversely, malware authors seek to detect such systems and evade analysis. In this paper, we present techniques for detecting Android runtime analysis systems. Our techniques are classified into four broad classes showing the ability to detect systems based on differences in behavior, performance, hardware and software components, and those resulting from analysis system design choices. We also evaluate our techniques against current publicly accessible systems, all of which are easily identified and can therefore be hindered by a motivated adversary. Our results show some fundamental limitations in the viability of dynamic mobile malware analysis platforms purely based on virtualization.
Abstract. The matrix barcodes known as Quick Response (QR) codes are rapidly becoming pervasive in urban environments around the world. QR codes are used to represent data, such as a web address, in a compact form that can be scanned readily and parsed by consumer mobile devices. They are popular with marketers because of their ease in deployment and use. However, this technology encourages mobile users to scan unauthenticated data from posters, billboards, stickers, and more, providing a new attack vector for miscreants. By positioning QR codes under false pretenses, attackers can entice users to scan the codes and subsequently visit malicious websites, install programs, or any other action the mobile device supports. We investigated the viability of QRcode-initiated phishing attacks, or QRishing, by conducting two experiments. In one experiment we visually monitored user interactions with QR codes; primarily to observe the proportion of users who scan a QR code but elect not to visit the associated website. In a second experiment, we distributed posters containing QR codes across 139 different locations to observe the broader application of QR codes for phishing. Over our four-week study, our disingenuous flyers were scanned by 225 individuals who subsequently visited the associated websites. Our survey results suggest that curiosity is the largest motivating factor for scanning QR codes. In our small surveillance experiment, we observed that 85% of those who scanned a QR code subsequently visited the associated URL.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.