12,421 research outputs found
Classifying Web Exploits with Topic Modeling
This short empirical paper investigates how well topic modeling and database
meta-data characteristics can classify web and other proof-of-concept (PoC)
exploits for publicly disclosed software vulnerabilities. By using a dataset
comprised of over 36 thousand PoC exploits, near a 0.9 accuracy rate is
obtained in the empirical experiment. Text mining and topic modeling are a
significant boost factor behind this classification performance. In addition to
these empirical results, the paper contributes to the research tradition of
enhancing software vulnerability information with text mining, providing also a
few scholarly observations about the potential for semi-automatic
classification of exploits in the existing tracking infrastructures.Comment: Proceedings of the 2017 28th International Workshop on Database and
Expert Systems Applications (DEXA).
http://ieeexplore.ieee.org/abstract/document/8049693
An Empirical Analysis of Vulnerabilities in Python Packages for Web Applications
This paper examines software vulnerabilities in common Python packages used
particularly for web development. The empirical dataset is based on the PyPI
package repository and the so-called Safety DB used to track vulnerabilities in
selected packages within the repository. The methodological approach builds on
a release-based time series analysis of the conditional probabilities for the
releases of the packages to be vulnerable. According to the results, many of
the Python vulnerabilities observed seem to be only modestly severe; input
validation and cross-site scripting have been the most typical vulnerabilities.
In terms of the time series analysis based on the release histories, only the
recent past is observed to be relevant for statistical predictions; the
classical Markov property holds.Comment: Forthcoming in: Proceedings of the 9th International Workshop on
Empirical Software Engineering in Practice (IWESEP 2018), Nara, IEE
My Software has a Vulnerability, should I worry?
(U.S) Rule-based policies to mitigate software risk suggest to use the CVSS
score to measure the individual vulnerability risk and act accordingly: an HIGH
CVSS score according to the NVD (National (U.S.) Vulnerability Database) is
therefore translated into a "Yes". A key issue is whether such rule is
economically sensible, in particular if reported vulnerabilities have been
actually exploited in the wild, and whether the risk score do actually match
the risk of actual exploitation.
We compare the NVD dataset with two additional datasets, the EDB for the
white market of vulnerabilities (such as those present in Metasploit), and the
EKITS for the exploits traded in the black market. We benchmark them against
Symantec's threat explorer dataset (SYM) of actual exploit in the wild. We
analyze the whole spectrum of CVSS submetrics and use these characteristics to
perform a case-controlled analysis of CVSS scores (similar to those used to
link lung cancer and smoking) to test its reliability as a risk factor for
actual exploitation.
We conclude that (a) fixing just because a high CVSS score in NVD only yields
negligible risk reduction, (b) the additional existence of proof of concepts
exploits (e.g. in EDB) may yield some additional but not large risk reduction,
(c) fixing in response to presence in black markets yields the equivalent risk
reduction of wearing safety belt in cars (you might also die but still..). On
the negative side, our study shows that as industry we miss a metric with high
specificity (ruling out vulns for which we shouldn't worry).
In order to address the feedback from BlackHat 2013's audience, the final
revision (V3) provides additional data in Appendix A detailing how the control
variables in the study affect the results.Comment: 12 pages, 4 figure
Recommended from our members
Analysis of operating system diversity for intrusion tolerance
One of the key benefits of using intrusion-tolerant systems is the possibility of ensuring correct behavior in the presence of attacks and intrusions. These security gains are directly dependent on the components exhibiting failure diversity. To what extent failure diversity is observed in practical deployment depends on how diverse are the components that constitute the system. In this paper, we present a study with operating system's (OS's) vulnerability data from the NIST National Vulnerability Database (NVD). We have analyzed the vulnerabilities of 11 different OSs over a period of 18 years, to check how many of these vulnerabilities occur in more than one OS. We found this number to be low for several combinations of OSs. Hence, although there are a few caveats on the use of NVD data to support definitive conclusions, our analysis shows that by selecting appropriate OSs, one can preclude (or reduce substantially) common vulnerabilities from occurring in the replicas of the intrusion-tolerant system
Talos: Neutralizing Vulnerabilities with Security Workarounds for Rapid Response
Considerable delays often exist between the discovery of a vulnerability and
the issue of a patch. One way to mitigate this window of vulnerability is to
use a configuration workaround, which prevents the vulnerable code from being
executed at the cost of some lost functionality -- but only if one is
available. Since program configurations are not specifically designed to
mitigate software vulnerabilities, we find that they only cover 25.2% of
vulnerabilities.
To minimize patch delay vulnerabilities and address the limitations of
configuration workarounds, we propose Security Workarounds for Rapid Response
(SWRRs), which are designed to neutralize security vulnerabilities in a timely,
secure, and unobtrusive manner. Similar to configuration workarounds, SWRRs
neutralize vulnerabilities by preventing vulnerable code from being executed at
the cost of some lost functionality. However, the key difference is that SWRRs
use existing error-handling code within programs, which enables them to be
mechanically inserted with minimal knowledge of the program and minimal
developer effort. This allows SWRRs to achieve high coverage while still being
fast and easy to deploy.
We have designed and implemented Talos, a system that mechanically
instruments SWRRs into a given program, and evaluate it on five popular Linux
server programs. We run exploits against 11 real-world software vulnerabilities
and show that SWRRs neutralize the vulnerabilities in all cases. Quantitative
measurements on 320 SWRRs indicate that SWRRs instrumented by Talos can
neutralize 75.1% of all potential vulnerabilities and incur a loss of
functionality similar to configuration workarounds in 71.3% of those cases. Our
overall conclusion is that automatically generated SWRRs can safely mitigate
2.1x more vulnerabilities, while only incurring a loss of functionality
comparable to that of traditional configuration workarounds.Comment: Published in Proceedings of the 37th IEEE Symposium on Security and
Privacy (Oakland 2016
Towards Adversarial Malware Detection: Lessons Learned from PDF-based Attacks
Malware still constitutes a major threat in the cybersecurity landscape, also
due to the widespread use of infection vectors such as documents. These
infection vectors hide embedded malicious code to the victim users,
facilitating the use of social engineering techniques to infect their machines.
Research showed that machine-learning algorithms provide effective detection
mechanisms against such threats, but the existence of an arms race in
adversarial settings has recently challenged such systems. In this work, we
focus on malware embedded in PDF files as a representative case of such an arms
race. We start by providing a comprehensive taxonomy of the different
approaches used to generate PDF malware, and of the corresponding
learning-based detection systems. We then categorize threats specifically
targeted against learning-based PDF malware detectors, using a well-established
framework in the field of adversarial machine learning. This framework allows
us to categorize known vulnerabilities of learning-based PDF malware detectors
and to identify novel attacks that may threaten such systems, along with the
potential defense mechanisms that can mitigate the impact of such threats. We
conclude the paper by discussing how such findings highlight promising research
directions towards tackling the more general challenge of designing robust
malware detectors in adversarial settings
Acquisition and diffusion of technology innovation
In the first essay, I examine value created through external acquisition of nascent technology innovation. External acquisition of new technology is a growing trend in the innovation process, particularly in high technology industries, as firms complement internal efforts with aggressive acquisition programs. Yet, despite its importance, there is little empirical research on the timing of acquisition decisions in high technology environments. I examine the impact of target age on value created for the buyer. Applying an event study methodology to technology acquisitions in the telecommunications industry from 1995 to 2001, empirical evidence supports acquiring early in the face of uncertainty. The equity markets reward the acquisition of younger companies.
In sharp contrast to the first essay, the second essay examines the diffusion of negative innovations. While destruction can be creative, certainly not all destruction is creative. Some is just destruction. I examine two fundamentally different paths to information security compromise an opportunistic path and a deliberate path. Through a grounded approach using interviews, observations, and secondary data, I advance a model of the information security compromise process. Using one year of alert data from intrusion detection devices, empirical analysis provides evidence that these paths follow two distinct, but interrelated diffusion patterns. Although distinct, I find empirical evidence that these paths both converge and escalate. Beyond the specific findings in the Internet security context, the study leads to a richer understanding of the diffusion of negative technological innovation.
In the third essay, I build on the second essay by examining the effectiveness of reward-based mechanisms in restricting the diffusion of negative innovations. Concerns have been raised that reward-based private infomediaries introduce information leakage which decreases social welfare. Using two years of alert data, I find evidence of their effectiveness despite any leakage which may be occurring. While reward-based disclosures are just as likely to be exploited as non-reward-baed disclosures, exploits from reward-based disclosures are less likely to occur in the first week after disclosure. Further the overall volume of alerts is reduced. This research helps determine the effectiveness of reward mechanisms and provides guidance for security policy makers.Ph.D.Committee Chair: Sabyasachi Mitra; Committee Member: Frank Rothaermel; Committee Member: Sandra Slaughter; Committee Member: Sridhar Narasimhan; Committee Member: Vivek Ghosa
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