A Survey on Password Guessing

Text password has served as the most popular method for user authentication so far, and is not likely to be totally replaced in foreseeable future. Password authentication offers several desirable properties (e.g., low-cost, highly available, easy-to-implement, reusable). However, it suffers from a critical security issue mainly caused by the inability to memorize complicated strings of humans. Users tend to choose easy-to-remember passwords which are not uniformly distributed in the key space. Thus, user-selected passwords are susceptible to guessing attacks. In order to encourage and support users to use strong passwords, it is necessary to simulate automated password guessing methods to determine the passwords' strength and identify weak passwords. A large number of password guessing models have been proposed in the literature. However, little attention was paid to the task of providing a systematic survey which is necessary to review the state-of-the-art approaches, identify gaps, and avoid duplicate studies. Motivated by that, we conduct a comprehensive survey on all password guessing studies presented in the literature from 1979 to 2022. We propose a generic methodology map to present an overview of existing methods. Then, we explain each representative approach in detail. The experimental procedures and available datasets used to evaluate password guessing models are summarized, and the reported performances of representative studies are compared. Finally, the current limitations and the open problems as future research directions are discussed. We believe that this survey is helpful to both experts and newcomers who are interested in password securityComment: 35 pages, 5 figures, 5 table

Detecting protein and post-translational modifications in single cells with iDentification and qUantification sEparaTion (DUET)

While technologies for measuring transcriptomes in single cells have matured, methods for measuring proteins and their post-translational modification (PTM) states in single cells are still being actively developed. Unlike nucleic acids, proteins cannot be amplified, making detection of minute quantities from single cells difficult. Here, we develop a strategy to detect targeted protein and its PTM isoforms in single cells. We barcode the proteins from single cells by tagging them with oligonucleotides, pool barcoded cells together, run bulk gel electrophoresis to separate protein and its PTM isoform and quantify their abundances by sequencing the oligonucleotides associated with each protein species. We used this strategy, iDentification and qUantification sEparaTion (DUET), to measure histone protein H2B and its monoubiquitination isoform, H2Bub, in single yeast cells. Our results revealed the heterogeneities of H2B ubiquitination levels in single cells from different cell-cycle stages, which is obscured in ensemble measurements

Detecting protein and post-translational modifications in single cells with iDentification and qUantification sEparaTion (DUET)

While technologies for measuring transcriptomes in single cells have matured, methods for measuring proteins and their post-translational modification (PTM) states in single cells are still being actively developed. Unlike nucleic acids, proteins cannot be amplified, making detection of minute quantities from single cells difficult. Here, we develop a strategy to detect targeted protein and its PTM isoforms in single cells. We barcode the proteins from single cells by tagging them with oligonucleotides, pool barcoded cells together, run bulk gel electrophoresis to separate protein and its PTM isoform and quantify their abundances by sequencing the oligonucleotides associated with each protein species. We used this strategy, iDentification and qUantification sEparaTion (DUET), to measure histone protein H2B and its monoubiquitination isoform, H2Bub, in single yeast cells. Our results revealed the heterogeneities of H2B ubiquitination levels in single cells from different cell-cycle stages, which is obscured in ensemble measurements

Spyware Resistant Smartphone User Authentication Scheme

As smart phones are becoming widely used, a variety of services to store and use important information such as photos and financial information are now provided. User authentication to protect this information is increasingly important. The commonly used 4-digit PIN, however, is vulnerable to the Brute Force Attack, Shoulder-Surfing Attack, and Recording Attack. Various authentication techniques are being developed in order to solve these problems. However, the technique that provides perfect protection, even from the Recording Attack, is not yet known, and in most cases, a password can be exposed by multiple Recording Attacks. This paper proposes a new user authentication method that protects against a Recording Attack from spyware on the user's smart phone. The proposed method prevents password exposure by multiple Recording Attacks, is implemented on a real Android phone, and has been evaluated for usability

No Resistive Normal Electrons in Beginning Superconducting States

We investigate why normal electrons in superconductors have no resistance. Under the same conditions, the band gap is reduced to zero as well, but normal electrons at superconducting states are condensed into this virtual energy band gap

Forged Signature Distinction Using Convolutional Neural Network for Feature Extraction

This paper proposes a dynamic verification scheme for finger-drawn signatures in smartphones. As a dynamic feature, the movement of a smartphone is recorded with accelerometer sensors in the smartphone, in addition to the moving coordinates of the signature. To extract high-level longitudinal and topological features, the proposed scheme uses a convolution neural network (CNN) for feature extraction, and not as a conventional classifier. We assume that a CNN trained with forged signatures can extract effective features (called S-vector), which are common in forging activities such as hesitation and delay before drawing the complicated part. The proposed scheme also exploits an autoencoder (AE) as a classifier, and the S-vector is used as the input vector to the AE. An AE has high accuracy for the one-class distinction problem such as signature verification, and is also greatly dependent on the accuracy of input data. S-vector is valuable as the input of AE, and, consequently, could lead to improved verification accuracy especially for distinguishing forged signatures. Compared to the previous work, i.e., the MLP-based finger-drawn signature verification scheme, the proposed scheme decreases the equal error rate by 13.7%, specifically, from 18.1% to 4.4%, for discriminating forged signatures

A Symmetric Key Based Deduplicatable Proof of Storage for Encrypted Data in Cloud Storage Environments

Over the recent years, cloud storage services have become increasingly popular, where users can outsource data and access the outsourced data anywhere, anytime. Accordingly, the data in the cloud is growing explosively. Among the outsourced data, most of them are duplicated. Cloud storage service providers can save huge amounts of resources via client-side deduplication. On the other hand, for safe outsourcing, clients who use the cloud storage service desire data integrity and confidentiality of the outsourced data. However, ensuring confidentiality and integrity in the cloud storage environment can be difficult. Recently, in order to achieve integrity with deduplication, the notion of deduplicatable proof of storage has emerged, and various schemes have been proposed. However, previous schemes are still inefficient and insecure. In this paper, we propose a symmetric key based deduplicatable proof of storage scheme, which ensures confidentiality with dictionary attack resilience and supports integrity auditing based on symmetric key cryptography. In our proposal, we introduce a bit-level challenge in a deduplicatable proof of storage protocol to minimize data access. In addition, we prove the security of our proposal in the random oracle model with information theory. Implementation results show that our scheme has the best performance

Bioinspired Garra Rufa Optimization-Assisted Deep Learning Model for Object Classification on Pedestrian Walkways

Object detection in pedestrian walkways is a crucial area of research that is widely used to improve the safety of pedestrians. It is not only challenging but also a tedious process to manually examine the labeling of abnormal actions, owing to its broad applications in video surveillance systems and the larger number of videos captured. Thus, an automatic surveillance system that identifies the anomalies has become indispensable for computer vision (CV) researcher workers. The recent advancements in deep learning (DL) algorithms have attracted wide attention for CV processes such as object detection and object classification based on supervised learning that requires labels. The current research study designs the bioinspired Garra rufa optimization-assisted deep learning model for object classification (BGRODL-OC) technique on pedestrian walkways. The objective of the BGRODL-OC technique is to recognize the presence of pedestrians and objects in the surveillance video. To achieve this goal, the BGRODL-OC technique primarily applies the GhostNet feature extractors to produce a set of feature vectors. In addition to this, the BGRODL-OC technique makes use of the GRO algorithm for hyperparameter tuning process. Finally, the object classification is performed via the attention-based long short-term memory (ALSTM) network. A wide range of experimental analysis was conducted to validate the superior performance of the BGRODL-OC technique. The experimental values established the superior performance of the BGRODL-OC algorithm over other existing approaches