Dictionary Attacks Research Articles

A new mathematical model to improve encryption process based on Split-Radix Fast Fourier Transform algorithm

This paper introduces a new encryption method aimed at improving the cryptography process through the use of splitting radix Fourier Transform technique called Split-Radix Fast Fourier Transforms (SRFFT). The proposed method is based on splitting the FFT radix-2 and radix-4 algorithms to achieve improved information assurance by SRFFT two phases. The first phase applies direct computation of SRFFT algorithm on input plaintext to produce a ciphertext and the second phase applies the reversing SRFFT algorithm to decipher. Several types of cryptoanalysis attacks such as brute-forcing, autocorrelation and dictionary attacks are comparatively evaluated and the end result of SRFFT evaluation indicates that SRFFT is preferable in many practical encryption applications since SRFFT complexity increases with the range of split-radix computations thus eliminating the potential chances of cryptanalysis attacks.

GDPR-anonymous population overlap estimation

The feasibility of many collaborative, data-driven research projects in Public Health and Social Sciences depends on the size of the overlap between the populations of contributing data partners, that is, the number of individuals for whom all parties can provide data. Where the identity of respective population members is subject to privacy protections, the overlap is traditionally estimated using de-identified cryptographic tokens to represent individuals. This does however not meet the strict standard of anonymity under GDPR and potentially runs counter to emerging privacy laws of US States, and may thus impose significant regulatory and governance burdens on research institutions, even at the early scoping stage. We present a novel method for overlap estimation, where two data partners construct a single, anonymous Bloom filter for their populations from which the overlap can be estimated. Two new techniques -- (a) the randomization of the contribution of individual population members to the Bloom filter and (b) an interactive bit-flipping protocol between data partners -- are presented which ensure that the Bloom filters are safe against dictionary attack by one party against the other party. We discuss the security aspects of this method, present the required mathematical formulas for calculating the overlap estimate, and report on the practical performance in terms of computational effort and resulting accuracy. The goal of the method is to facilitate new research by reducing the cost and complexity of initial planning and by making the overlap estimate GDPR-anonymous, and thus safer and subject to less onerous compliance requirements.

A Comparative Study Between Two Cybersecurity Attacks: Brute Force and Dictionary Attacks

In today's world, information drives not only business but nearly every aspect of human life. Therefore, safeguarding valuable information from malicious activities like attacks has become essential. Brute force search and dictionary attacks are prevalent cybersecurity threats, in which an attacker systematically attempts all possible passwords and passphrases to gain access to a user's account. These types of attacks are common due to the widespread reuse of simple password variations. The goal of this study is to discover as many passwords as possible and demonstrate their predictability and susceptibility. Our focus was on comparing two cracking methods, such as brute force search attacks and dictionary attacks, assessing their effectiveness and time requirements.

Multi-Level Encryption System using AES and RSA Algorithms

Abstract: As the volume of sensitive data entrusted to the cloud explodes, from healthcare records to financial transactions, the need for robust security solutions intensifies. Traditional encryption techniques, while effective, remain vulnerable to key compromise, leaving data exposed to malicious factors. This study proposes a multi-level encryption strategy that leverages the power of AES for efficient data encryption, RSA for secure key protection with public-key cryptography, and role-based encryption for granular access control. This layered approach significantly increases the computational complexity for unauthorized access, making brute-force or dictionary attacks exponentially more difficult. Our research demonstrates successful integration of these algorithms, achieving a 50% reduction in key compromise risk and enhanced data integrity through MAC verification. Moving forward, we envision seamless integration with multiple cloud platforms and exploring advanced access control mechanisms based on user context and behavior. This multi-level encryption strategy holds immense potential to reshape the landscape of cloud data security, fostering a more secure and trustworthy digital future.

Efficient and fine-grained access control with fully-hidden policies for cloud-enabled IoT

Ciphertext-Policy Attribute-Based Encryption (CP-ABE) enables fine-grained access control on ciphertexts, making it a promising approach for managing data stored in the cloud-enabled Internet of Things. But existing schemes often suffer from privacy breaches due to explicit attachment of access policies or partial hiding of critical attribute content. Additionally, resource-constrained IoT devices, especially those adopting wireless communication, frequently encounter affordability issues regarding decryption costs. In this paper, we propose an efficient and fine-grained access control scheme with fully hidden policies (named FHAC). FHAC conceals all attributes in the policy and utilizes bloom filters to efficiently locate them. A test phase before decryption is applied to assist authorized users in finding matches between their attributes and the access policy. Dictionary attacks are thwarted by providing unauthorized users with invalid values. The heavy computational overhead of both the test phase and most of the decryption phase is outsourced to two cloud servers. Additionally, users can verify the correctness of multiple outsourced decryption results simultaneously. Security analysis and performance comparisons demonstrate FHAC's effectiveness in protecting policy privacy and achieving efficient decryption.

Password authenticated key exchange-based on Kyber for mobile devices.

In this article, a password-authenticated key exchange (PAKE) version of the National Institute of Standards and Technology (NIST) post-quantum cryptography (PQC) public-key encryption and key-establishment standard is constructed. We mainly focused on how the PAKE version of PQC standard Kyber with mobile compatibility can be obtained by using simple structured password components. In the design process, the conventional password-based authenticated key exchange (PAK) approach is updated under the module learning with errors (MLWE) assumptions to add password-based authentication. Thanks to the following PAK model, the proposed Kyber.PAKE provides explicit authentication and perfect forward secrecy (PFS). The resistance analysis against the password dictionary attack of Kyber.PAKE is examined by using random oracle model (ROM) assumptions. In the security analysis, the cumulative distribution function (CDF) Zipf (CDF-Zipf) model is also followed to provide realistic security examinations. According to the implementation results, Kyber.PAKE presents better run-time than lattice-based PAKE schemes with similar features, even if it contains complex key encapsulation mechanism (KEM) components. The comparison results show that the proposed PAKE scheme will come to the fore for the future security of mobile environments and other areas.

A Multilayer Data Security Using Matrix Transformation and RSA For Public Cloud Storage

Cloud computing is an internet-based system that allows clients to access computers, software, infrastructure, devices, and other resources through a subscription model. Data security is a major concern for the cloud computing model. Data security is a research challenge in any cloud environment. When cloud users upload their confidential and secret data through the cloud, the security of this data must be ensured. In order to improve data security, a new method is presented in this paper. The approach uses ten random prime integers to compute the public and safe private keys after transforming user data into a matrix format. The encrypted content is then decrypted using the secured private key, improving total data security. The Hackman tool is used to analyse the encryption and decryption times as well as the security level. The OPNET tool measures the power of encryption and decryption. The findings indicate that all five potential file sizes for the proposed algorithm's parameters have the highest value. The cypher text was analysed using this suggested technique using brute force and dictionary attacks. This method is more effective, more secure, and impenetrable.

Flaw and amendment of Dharminder et al.'s authentication protocol for satellite communication

SummarySatellite communication is becoming an increasingly important component of the communication process, as all forms of communication are sent over open networks. As a result, there is an increase in the number of security concerns, and several protocols have been established with this consideration in mind. Recently, Dharminder et al. proposed a post‐quantum secure authentication protocol for satellite communication. The protocol is an improved version of Kumar and Garg's protocol. The authors claimed that the protocol resists the vulnerability of Kumar and Garg's protocol and provides post‐quantum security for satellite communication. Despite their claims, we have found that the protocol is vulnerable to a key mismatch attack and an offline dictionary attack. Further, we have improved Dharminder et al.'s proposed protocol that resists the key mismatch attack and offline dictionary attack and have provided the condition for the correctness of the improved protocol. Our formal security proof and implementation results demonstrate that the improved protocol is secure against quantum attacks.

Evaluating the security of CAPTCHAs utilized on Bangladeshi websites

A number of attacks such as spamming, email-address harvesting, dictionary attacks, brute-force attacks, credential surfing attacks, DOS attacks and others can be executed relatively easily if the target system cannot differentiate between requests coming from a real human user and those generated artificially by bots. In order to distinguish between human users and bots, the Completely Automatic Public Turing Test (CAPTCHA) has been used for almost 20 years. Nevertheless, CAPTCHA can be easily broken if it lacks security features. Consequently, the motivation for utilizing CAPTCHA is undermined. This article proposes a framework that can evaluate the security aspects of text CAPTCHAs. The effectiveness of the framework is validated by using a generalized attack method consisting of pre-processing and utilizing an Optical Character Recognition (OCR) library such as Tesseract OCR. We have carried out a systematic study of existing CAPTCHAs utilized on Bangladeshi websites. We have studied 238 Bangladeshi websites and found that only 44 websites used a CAPTCHA mechanism. Then, we have assessed the security levels of the selected text CAPTCHAs by the proposed framework. As per our analysis, we have found that there are 29 different CAPTCHA schemes being adopted on Bangladeshi websites and among them 23 are vulnerable to automated attacks. In order to address these issues, we also present a number of recommendations.

A new lattice-based password authenticated key exchange scheme with anonymity and reusable key.

In this article, we propose a novel bilateral generalization inhomogenous short integer solution (BiGISIS)-based password-authenticated key exchange (PAKE) scheme for post-quantum era security. The hardness assumption of the constructed PAKE is based on newly proposed hard lattice problem, BiGISIS. The main aim of this article is to provide a solution for the post-quantum secure PAKE scheme, which is one of the open problems in the literature. The proposed PAKE is the first BiGISIS-based PAKE that satisfies anonymity and reusable key features. The bilateral-pasteurization (BiP) approach is used to obtain the reusable key, and anonymity is achieved thanks to the additional identity components and hash functions. The reusable key structure reduces the time in the key generation, and anonymity prevents illegal user login attempts. The security analysis is done by following the real-or-random (RoR) model assumptions. As a result of security examinations, perfect forward secrecy (PFS) and integrity are satisfied, and the resistance against eavesdropping, manipulation-based attack (MBA), hash function simulation, impersonation, signal leakage attack (SLA), man-in-the-middle (MitM), known-key security (KKS), and offline password dictionary attack (PDA) is captured. According to the comparison analysis, the proposed PAKE is the first SLA-resistant lattice-based PAKE with reusable key and anonymity properties.

Process of Fingerprint Authentication using Cancelable Biohashed Template

Template protection using cancelable biometrics prevents data loss and hacking stored templates, by providing considerable privacy and security. Hashing and salting techniques are used to build resilient systems. Salted password method is employed to protect passwords against different types of attacks namely brute-force attack, dictionary attack, rainbow table attacks. Salting claims that random data can be added to input of hash function to ensure unique output. Hashing salts are speed bumps in an attacker’s road to breach user’s data. Research proposes a contemporary two factor authenticator called Biohashing. Biohashing procedure is implemented by recapitulated inner product over a pseudo random number generator key, as well as fingerprint features that are a network of minutiae. Cancelable template authentication used in fingerprint-based sales counter accelerates payment process. Fingerhash is code produced after applying biohashing on fingerprint. Fingerhash is a binary string procured by choosing individual bit of sign depending on a preset threshold. Experiment is carried using benchmark FVC 2002 DB1 dataset. Authentication accuracy is found to be nearly 97\%. Results compared with state-of art approaches finds promising.

RoseCliff Algorithm: Making Passwords Dynamic

Authentication in the digital landscape faces persistent challenges due to evolving cyber threats. Traditional text-based passwords, which are vulnerable to various attacks, necessitate innovative solutions to fortify user systems. This paper introduces the RoseCliff Algorithm, which is a dual authentication mechanism designed to enhance resilience against sophisticated hacking attempts and to continuously evolve stored passwords. The study explores encryption techniques, including symmetric, asymmetric, and hybrid encryption, thereby addressing the emerging threats posed by quantum computers. The RoseCliff Algorithm introduces introduces dynamism into passwords that allows for more secured communication across multiple platforms. To assess the algorithm’s robustness, potential attacks such as brute force, dictionary attacks, man-in-the-middle attacks, and machine learning-based attacks are examined. The RoseCliff Algorithm, through its dynamic password generation and encryption methodology, proves effective against these threats. Usability evaluation encompasses the implementation and management phase, focusing on seamless integration, and the user experience, emphasizing clarity and satisfaction. Limitations are acknowledged, thus urging further research into encryption technique resilience, robustness against breaches, and the integration of emerging technologies. In conclusion, the RoseCliff Algorithm emerges as a promising solution, thereby effectively addressing the complexities of modern authentication challenges and providing a foundation for future research and enhancements in digital security.

Graphical Password Scheme Based on Color Hint Approach

Generally, textual password is the most used kind of authentication technique. However, this authentication type is vulnerable to a lot of attacks. Users using this kind of authentication techniques are at the risk of exposing vital organizational data or even password to attacks such as phishing, shoulder surfing, brute force and dictionary attacks etc. This is the major reason why a new authentication type is required, in other to minimize these threats or attacks, graphical authentication techniques were developed. But this authentication also faces attacks such as shoulder surfing and guessing attacks etc. In this paper, a graphical password scheme based on color hint approach has been developed and analyzed. The scheme authenticates users in two different ways which are: using an image to select different password click points and using secret color hint and transparent shapes as a way for the user to know the position of the click points after it had been reshuffled on the selected image. The project will be limited to the reliability, efficiency, ease of use and friendliness of graphical user interface during the user authentication. The system has been able to minimize or reduce the problem of shoulder surfing and guessing attacks.

Multiple Approaches Towards Authentication Using Keystroke Dynamics

In the realm of global computer security, safeguarding sensitive data and computer systems stands as an ongoing challenge. Striking the delicate balance between convenient access for legitimate users and thwarting imposter attacks is imperative. Traditional authentication methods, primarily reliant on username and password schemes, have been the longstanding cornerstone of cyber system authentication. Yet, this conventional approach is marred by inherent vulnerabilities, including password exchange, shoulder surfng, brute force attacks, dictionary attacks, speculation, phishing, and emerging threats. In response to these shortcomings, biometrics technology has emerged as a reliable alternative for authentication and verification, with Keystroke Dynamics being a well-established method. This research advances Keystroke Dynamics-based authentication through cutting-edge technologies, including machine learning, deep learning, and neural networks. A quantitative assessment demonstrates remarkable results—Machine Learning achieves 99.9% accuracy with the Random Forest model, Deep Learning combines CNN with GRU to reach 99.31% accuracy, and the DBN model registers at 98.01%. In the realm of neural networks, Bi-CNN and FFM-NN excel, with accuracy rates of 96.8% and 94.7%, respectively. These findings lay the foundation for robust, secure authentication systems, ensuring the protection of sensitive data and computer assets in our interconnected world.

Exploring the Frontier of Password Cracking: Methods, Effectiveness, and Defense Strategies

The manner of attacker's behavior cannot be underestimated; hackers use simple traditional attacks such as brute-force and dictionary attacks as well as sophisticated algorithms including: Markov models, probabilistic context-free grammars (PCFG), and generative adversarial networks (GANs). These are one of among the most advanced approaches which utilize artificial intelligence and machine learning to identify the patterns in passwords, guess them and crack them. Markov models calculate transitions from one character state to another, so they estimate password guesses as a probability which is sampled from the distribution. PCFGs further advance the concept by making use of context-specific inputs for producing the passwords, and as a result it is possible to come up with the candidates who are balanced and have the contextually valid characters. Years ago, the CPA attack was considered the ultimate approach for password cracking. Today, GANs have taken their place, implementing adversarial networks that use them as generators to generate valid password examples. It is evident from the research that abusing users' habits and context during password cracking has been proved that it can lead to a tremendous speed gain of the cracking process. The crackers exploit here patterns in the behavior and environmental features and tailor the cracking strategies. Besides that, recurrent neural networks (RNNs) and convolutional neural networks (CNNs) are considered to be good options in password modeling as well, where the ongoing researches are devoted to the structures of neural network so that the guessing powers of networks can improve. These new approaches have demonstrated an improvement of at least 10-15% over the outdated ones, thus they are credible in forming the password cracking paradigm shift. Thus, for defeating such sophisticated threats, companies should take into account such robust passphrase policies, teach the user about safety of passwords and implementation of rigid access mechanisms. Educating people on cyber threats basics and development of the reasonable cybersecurity culture are the factors that provide the impact reduction of attacks based on users’ behavior and contextual information.

Beaf:BD – A Blockchain Enabled Authentication Framework for Big Data

The widespread utilization of Internet-based applications in our daily routines has resulted in enormous amounts of data being generated every minute. This data is not only produced by humans but also by various machines such as sensors, satellites, CCTV, etc. For many organizations, Apache Hadoop is the solution for handling big data. Big data refers to the extensive set of dissimilar data that can be processed to derive meaningful insights. For its security needs, Hadoop relies on trusted third-party security providers such as Kerberos. Kerberos has several security vulnerabilities. The focus of this paper is to eliminate security issues, particularly dictionary attacks and single points of failure, by proposing a model based on blockchain technology and threshold cryptography.In comparison to other existing schemes, the proposed approach offers superior computational overhead and storage requirements while maintaining the system's security level.

Inspired Symbol-based Authentication against Dictionary attacks Using ML Algorithm

Verifying an individual's identity before granting them access to a linked device, an internet service, or another resource is known as user authentication. The significance of authentication lies in its ability to safeguard data, apps, and networks for companies by limiting access to their protected resources to only authorized individuals or processes. This paper used the popular Big data technology Apache Spark for storing, and processing large data and proposed a novel authentication framework. A viable replacement for conventional alphanumeric passwords, bio-metric and facial authentications with dynamic symbol selection as an authentication. This authentication is tested in the Apache spark cluster which is the most distributed system. In these methods, SHA512 cryptography is used in several ways and comparison is done using existing authentication and machine learning algorithms. The straightforward authentication scheme and applied Apache Spark distributed system with 10 nodes produced the best results.

Exploring Wi-Fi Security Challenges and Proposing Solutions: The case of Afghanistan

Wireless networks are the most developed progress than other technologies because of cost-effective, flexibility, mobility, and easy deployment. Despite these benefits, wireless networks suffer from different types of challenges and vulnerabilities. During past decades, IEEE, IETF and, Wi-Fi Alliance Institute developed a lot of wireless security protocols including WEP, WPA, WPA2, WPA3, 802.1x standards. Besides, these protocols bring the most solutions for wireless security challenges, still, these protocols are vulnerable against the different types of attacks and threats (Dictionary attacks and Rogue access points). Thus, in this paper after doing a survey and finding the challenges in the case of Afghanistan, I propose solutions regarding specific challenges. The proposed solutions are Kismet and Snort for rogue-AP and dictionary attack detection and prevention. Since these proposed solutions are implemented in the least cost due to open source and free attitude, the proposed solutions are implementable in every Wi-Fi environment in Afghanistan.

Analysis of two-factor authentication plugins for WordPress

The purpose of this article is to analyze existing two-factor authentication plugins to assess their effectiveness. Due to the lack of substantial protection against unauthorized access in the WordPress system, it becomes vulnerable to types of attacks such as dictionary attacks and brute force attacks. To address this issue, plugins for two-factor authentication are used. The article examines the most popular two-factor authentication plugins to evaluate the level of security they provide.
 This article will be beneficial for website owners and administrators using WordPress who need to protect their resources against unauthorized access.

Password strength verification based on machine learning algorithms and LSTM recurrent neural networks

Objectives. One of the most commonly used authentication methods in computer systems, password authentication is susceptible to various attacks including brute-force and dictionary attacks. This susceptibility requires not only the strict protection of user credentials, but also the definition of criteria for increasing a password’s strength to minimize the possibility of its exploitation by an attacker. Thus, an important task is the development of a verifier for checking passwords for strength and prohibiting the user from setting passwords that are susceptible to cracking. The use of machine learning methods to construct a verifier involves algorithms for formulating requirements for password complexity based on lists of known passwords available for each strength category.Methods. The proposed supervised machine learning algorithms comprise support vector machines, random forest, boosting, and long short-term memory (LSTM) recurrent neural network types. Embedding and term frequency–inverse document frequency (TF-IDF) methods are used for data preprocessing, while cross-validation is used for selecting hyperparameters.Results. Password strength recommendations and requirements from international and Russian standards are described. The existing methods of password strength verification in various operating systems are analyzed. The experimental results based on existing datasets comprising passwords having an associated level of strength are presented.Conclusions. A LSTM recurrent neural network is highlighted as one of the most promising areas for building a password strength verifier.