Password Based a Generalize Robust Security System Design Using Neural Network

Among the various means of available resource protection including biometrics, password based system is most simple, user friendly, cost effective and commonly used. But this method having high sensitivity with attacks. Most of the advanced methods for authentication based on password encrypt the contents of password before storing or transmitting in physical domain. But all conventional cryptographic based encryption methods are having its own limitations, generally either in terms of complexity or in terms of efficiency. Multi-application usability of password today forcing users to have a proper memory aids. Which itself degrades the level of security. In this paper a method to exploit the artificial neural network to develop the more secure means of authentication, which is more efficient in providing the authentication, at the same time simple in design, has given. Apart from protection, a step toward perfect security has taken by adding the feature of intruder detection along with the protection system. This is possible by analysis of several logical parameters associated with the user activities. A new method of designing the security system centrally based on neural network with intrusion detection capability to handles the challenges available with present solutions, for any kind of resource has presented

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

PassGAN: A Deep Learning Approach for Password Guessing

State-of-the-art password guessing tools, such as HashCat and John the Ripper, enable users to check billions of passwords per second against password hashes. In addition to performing straightforward dictionary attacks, these tools can expand password dictionaries using password generation rules, such as concatenation of words (e.g., "password123456") and leet speak (e.g., "password" becomes "p4s5w0rd"). Although these rules work well in practice, expanding them to model further passwords is a laborious task that requires specialized expertise. To address this issue, in this paper we introduce PassGAN, a novel approach that replaces human-generated password rules with theory-grounded machine learning algorithms. Instead of relying on manual password analysis, PassGAN uses a Generative Adversarial Network (GAN) to autonomously learn the distribution of real passwords from actual password leaks, and to generate high-quality password guesses. Our experiments show that this approach is very promising. When we evaluated PassGAN on two large password datasets, we were able to surpass rule-based and state-of-the-art machine learning password guessing tools. However, in contrast with the other tools, PassGAN achieved this result without any a-priori knowledge on passwords or common password structures. Additionally, when we combined the output of PassGAN with the output of HashCat, we were able to match 51%-73% more passwords than with HashCat alone. This is remarkable, because it shows that PassGAN can autonomously extract a considerable number of password properties that current state-of-the art rules do not encode.Comment: This is an extended version of the paper which appeared in NeurIPS 2018 Workshop on Security in Machine Learning (SecML'18), see https://github.com/secml2018/secml2018.github.io/raw/master/PASSGAN_SECML2018.pd

Crime Scene Re-investigation: A Postmortem Analysis of Game Account Stealers' Behaviors

As item trading becomes more popular, users can change their game items or money into real money more easily. At the same time, hackers turn their eyes on stealing other users game items or money because it is much easier to earn money than traditional gold-farming by running game bots. Game companies provide various security measures to block account- theft attempts, but many security measures on the user-side are disregarded by users because of lack of usability. In this study, we propose a server-side account theft detection system base on action sequence analysis to protect game users from malicious hackers. We tested this system in the real Massively Multiplayer Online Role Playing Game (MMORPG). By analyzing users full game play log, our system can find the particular action sequences of hackers with high accuracy. Also, we can trace where the victim accounts stolen money goes.Comment: 7 pages, 8 figures, In Proceedings of the 15th Annual Workshop on Network and Systems Support for Games (NetGames 2017

User habitation in keystroke dynamics based authentication

Most computer systems use usernames and passwords for authentication and access control. For long, password security has been framed as a tradeoff between user experience and password security. Trading off one for the other appears to be an inevitable dilemma for single password based security applications. As a new biometric for authenticating access, keystroke dynamics offers great promises in hardening the password mechanism. Our research first investigate the keystroke dynamics based password security by conducting an incremental study on user\u27s habituation process for keystroke dynamics analysis using two distinct types of passwords. The study shows that (1) long and complex passwords are more efficient to be employed in keystroke dynamics systems; and (2) there is a habituation and acclimation process before the user obtains a stable keystroke pattern and the system collects enough training data. Then, based on our findings, we propose a two passwords mechanism that attempts to strike the right balance over user experience and password security by adopting a conventional easy-to-memorize password followed by a long-and-complex phrase for keystroke dynamics verification. Analysis and experimental studies successfully demonstrate the effectiveness of our proposed approach

Universal Neural-Cracking-Machines: Self-Configurable Password Models from Auxiliary Data

We develop the first universal password model -- a password model that, once pre-trained, can automatically adapt to any password distribution. To achieve this result, the model does not need to access any plaintext passwords from the target set. Instead, it exploits users' auxiliary information, such as email addresses, as a proxy signal to predict the underlying target password distribution. The model uses deep learning to capture the correlation between the auxiliary data of a group of users (e.g., users of a web application) and their passwords. It then exploits those patterns to create a tailored password model for the target community at inference time. No further training steps, targeted data collection, or prior knowledge of the community's password distribution is required. Besides defining a new state-of-the-art for password strength estimation, our model enables any end-user (e.g., system administrators) to autonomously generate tailored password models for their systems without the often unworkable requirement of collecting suitable training data and fitting the underlying password model. Ultimately, our framework enables the democratization of well-calibrated password models to the community, addressing a major challenge in the deployment of password security solutions on a large scale.Comment: v0.0

The Future of Cybercrime: AI and Emerging Technologies Are Creating a Cybercrime Tsunami

This paper reviews the impact of AI and emerging technologies on the future of cybercrime and the necessary strategies to combat it effectively. Society faces a pressing challenge as cybercrime proliferates through AI and emerging technologies. At the same time, law enforcement and regulators struggle to keep it up. Our primary challenge is raising awareness as cybercrime operates within a distinct criminal ecosystem. We explore the hijacking of emerging technologies by criminals (CrimeTech) and their use in illicit activities, along with the tools and processes (InfoSec) to protect against future cybercrime. We also explore the role of AI and emerging technologies (DeepTech) in supporting law enforcement, regulation, and legal services (LawTech)

Tiresias: Predicting Security Events Through Deep Learning

With the increased complexity of modern computer attacks, there is a need for defenders not only to detect malicious activity as it happens, but also to predict the specific steps that will be taken by an adversary when performing an attack. However this is still an open research problem, and previous research in predicting malicious events only looked at binary outcomes (e.g., whether an attack would happen or not), but not at the specific steps that an attacker would undertake. To fill this gap we present Tiresias, a system that leverages Recurrent Neural Networks (RNNs) to predict future events on a machine, based on previous observations. We test Tiresias on a dataset of 3.4 billion security events collected from a commercial intrusion prevention system, and show that our approach is effective in predicting the next event that will occur on a machine with a precision of up to 0.93. We also show that the models learned by Tiresias are reasonably stable over time, and provide a mechanism that can identify sudden drops in precision and trigger a retraining of the system. Finally, we show that the long-term memory typical of RNNs is key in performing event prediction, rendering simpler methods not up to the task

The effective combating of intrusion attacks through fuzzy logic and neural networks

The importance of properly securing an organization’s information and computing resources has become paramount in modern business. Since the advent of the Internet, securing this organizational information has become increasingly difficult. Organizations deploy many security mechanisms in the protection of their data, intrusion detection systems in particular have an increasingly valuable role to play, and as networks grow, administrators need better ways to monitor their systems. Currently, many intrusion detection systems lack the means to accurately monitor and report on wireless segments within the corporate network. This dissertation proposes an extension to the NeGPAIM model, known as NeGPAIM-W, which allows for the accurate detection of attacks originating on wireless network segments. The NeGPAIM-W model is able to detect both wired and wireless based attacks, and with the extensions to the original model mentioned previously, also provide for correlation of intrusion attacks sourced on both wired and wireless network segments. This provides for a holistic detection strategy for an organization. This has been accomplished with the use of Fuzzy logic and neural networks utilized in the detection of attacks. The model works on the assumption that each user has, and leaves, a unique footprint on a computer system. Thus, all intrusive behaviour on the system and networks which support it, can be traced back to the user account which was used to perform the intrusive behavior