277 research outputs found
A study on the false positive rate of Stegdetect
In this paper we analyse Stegdetect, one of the well-known image steganalysis tools, to study its false positive rate. In doing so, we process more than 40,000 images randomly downloaded from the Internet using Google images, together with 25,000 images from the ASIRRA (Animal Species Image Recognition for Restricting Access) public corpus. The aim of this study is to help digital forensic analysts, aiming to study a large number of image files during an investigation, to better understand the capabilities and the limitations of steganalysis tools like Stegdetect. The results obtained show that the rate of false positives generated by Stegdetect depends highly on the chosen sensitivity value, and it is generally quite high. This should support the forensic expert to have better interpretation in their results, and taking the false positive rates into consideration. Additionally, we have provided a detailed statistical analysis for the obtained results to study the difference in detection between selected groups, close groups and different groups of images. This method can be applied to any steganalysis tool, which gives the analyst a better understanding of the detection results, especially when he has no prior information about the false positive rate of the tool
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
Scottish Nonprofit Mental Health Organizational Strategies to Commercialize Innovative Products and Services
Many nonprofit organization (NPO) leaders lack strategies to commercialize innovativeproducts and services to secure funding and ensure financial stability. NPO leaders who do not overcome financial challenges could face organizational failure and the inability to attain their mission. Grounded in stakeholder theory, the purpose of this qualitative, single case study was to explore strategies that Scottish nonprofit mental health organization leaders use to commercialize innovative products and services to secure funding and ensure financial stability. The study participants were 4 leaders at a single mental health organization in Scotland. Semistructured interviews, organizational documents, government data, public data, and information from the participant organization’s website comprised the data collected for this study. Identification of themes occurred using Yin’s 5-step thematic analysis process, whereas data assessment and organization scoring occurred using the Baldrige Performance Excellence Program criteria. Key themes that emerged from the analysis included effective strategic planning, effective financial management, ineffective commercialization strategy, positive work environment, and partially effective workforce development. A key recommendation for NPO leaders is to incorporate robust marketing as part of their strategy to commercialize products and services. Commercialization of mental health services and tools could result in positive social change by expanding available resources within Scotland’s mental health community and creating a revenue source that will allow the NPO to continue providing vital services to the community
Structural Learning of Attack Vectors for Generating Mutated XSS Attacks
Web applications suffer from cross-site scripting (XSS) attacks that
resulting from incomplete or incorrect input sanitization. Learning the
structure of attack vectors could enrich the variety of manifestations in
generated XSS attacks. In this study, we focus on generating more threatening
XSS attacks for the state-of-the-art detection approaches that can find
potential XSS vulnerabilities in Web applications, and propose a mechanism for
structural learning of attack vectors with the aim of generating mutated XSS
attacks in a fully automatic way. Mutated XSS attack generation depends on the
analysis of attack vectors and the structural learning mechanism. For the
kernel of the learning mechanism, we use a Hidden Markov model (HMM) as the
structure of the attack vector model to capture the implicit manner of the
attack vector, and this manner is benefited from the syntax meanings that are
labeled by the proposed tokenizing mechanism. Bayes theorem is used to
determine the number of hidden states in the model for generalizing the
structure model. The paper has the contributions as following: (1)
automatically learn the structure of attack vectors from practical data
analysis to modeling a structure model of attack vectors, (2) mimic the manners
and the elements of attack vectors to extend the ability of testing tool for
identifying XSS vulnerabilities, (3) be helpful to verify the flaws of
blacklist sanitization procedures of Web applications. We evaluated the
proposed mechanism by Burp Intruder with a dataset collected from public XSS
archives. The results show that mutated XSS attack generation can identify
potential vulnerabilities.Comment: In Proceedings TAV-WEB 2010, arXiv:1009.330
Using Global Honeypot Networks to Detect Targeted ICS Attacks
Defending industrial control systems (ICS) in the cyber domain is both helped and hindered by bespoke systems integrating heterogeneous devices for unique purposes. Because of this fragmentation, observed attacks against ICS have been targeted and skilled, making them difficult to identify prior to initiation. Furthermore, organisations may be hesitant to share business-sensitive details of an intrusion that would otherwise assist the security community.
In this work, we present the largest study of high-interaction ICS honeypots to date and demonstrate that a network of internet-connected honeypots can be used to identify and profile targeted ICS attacks. Our study relies on a network of 120 high-interaction honeypots in 22 countries that mimic programmable logic controllers and remote terminal units. We provide a detailed analysis of 80,000 interactions over 13 months, of which only nine made malicious use of an industrial protocol. Malicious interactions included denial of service and replay attacks that manipulated logic, leveraged protocol implementation gaps and exploited buffer overflows. While the yield was small, the impact was high, as these were skilled, targeted exploits previously unknown to the ICS community.
By comparison with other ICS honeypot studies, we demonstrate that high-quality deception over long periods is necessary for such a honeypot network to be effective. As part of this argument, we discuss the accidental and intentional reasons why an internet-connected honeypot might be targeted. We also provide recommendations for effective, strategic use of such networks.Gates Cambridge Trus
Why Do Developers Get Password Storage Wrong? A Qualitative Usability Study
Passwords are still a mainstay of various security systems, as well as the
cause of many usability issues. For end-users, many of these issues have been
studied extensively, highlighting problems and informing design decisions for
better policies and motivating research into alternatives. However, end-users
are not the only ones who have usability problems with passwords! Developers
who are tasked with writing the code by which passwords are stored must do so
securely. Yet history has shown that this complex task often fails due to human
error with catastrophic results. While an end-user who selects a bad password
can have dire consequences, the consequences of a developer who forgets to hash
and salt a password database can lead to far larger problems. In this paper we
present a first qualitative usability study with 20 computer science students
to discover how developers deal with password storage and to inform research
into aiding developers in the creation of secure password systems
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 (eg. 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 xspace, a system that leverages Recurrent Neural Networks (RNNs) to predict future events on a machine, based on previous observations. We test Tiresias xspace 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 xspace 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
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