16,406 research outputs found
Reviewer Integration and Performance Measurement for Malware Detection
We present and evaluate a large-scale malware detection system integrating
machine learning with expert reviewers, treating reviewers as a limited
labeling resource. We demonstrate that even in small numbers, reviewers can
vastly improve the system's ability to keep pace with evolving threats. We
conduct our evaluation on a sample of VirusTotal submissions spanning 2.5 years
and containing 1.1 million binaries with 778GB of raw feature data. Without
reviewer assistance, we achieve 72% detection at a 0.5% false positive rate,
performing comparable to the best vendors on VirusTotal. Given a budget of 80
accurate reviews daily, we improve detection to 89% and are able to detect 42%
of malicious binaries undetected upon initial submission to VirusTotal.
Additionally, we identify a previously unnoticed temporal inconsistency in the
labeling of training datasets. We compare the impact of training labels
obtained at the same time training data is first seen with training labels
obtained months later. We find that using training labels obtained well after
samples appear, and thus unavailable in practice for current training data,
inflates measured detection by almost 20 percentage points. We release our
cluster-based implementation, as well as a list of all hashes in our evaluation
and 3% of our entire dataset.Comment: 20 papers, 11 figures, accepted at the 13th Conference on Detection
of Intrusions and Malware & Vulnerability Assessment (DIMVA 2016
Command & Control: Understanding, Denying and Detecting - A review of malware C2 techniques, detection and defences
In this survey, we first briefly review the current state of cyber attacks,
highlighting significant recent changes in how and why such attacks are
performed. We then investigate the mechanics of malware command and control
(C2) establishment: we provide a comprehensive review of the techniques used by
attackers to set up such a channel and to hide its presence from the attacked
parties and the security tools they use. We then switch to the defensive side
of the problem, and review approaches that have been proposed for the detection
and disruption of C2 channels. We also map such techniques to widely-adopted
security controls, emphasizing gaps or limitations (and success stories) in
current best practices.Comment: Work commissioned by CPNI, available at c2report.org. 38 pages.
Listing abstract compressed from version appearing in repor
Hiding in Plain Sight: A Longitudinal Study of Combosquatting Abuse
Domain squatting is a common adversarial practice where attackers register
domain names that are purposefully similar to popular domains. In this work, we
study a specific type of domain squatting called "combosquatting," in which
attackers register domains that combine a popular trademark with one or more
phrases (e.g., betterfacebook[.]com, youtube-live[.]com). We perform the first
large-scale, empirical study of combosquatting by analyzing more than 468
billion DNS records---collected from passive and active DNS data sources over
almost six years. We find that almost 60% of abusive combosquatting domains
live for more than 1,000 days, and even worse, we observe increased activity
associated with combosquatting year over year. Moreover, we show that
combosquatting is used to perform a spectrum of different types of abuse
including phishing, social engineering, affiliate abuse, trademark abuse, and
even advanced persistent threats. Our results suggest that combosquatting is a
real problem that requires increased scrutiny by the security community.Comment: ACM CCS 1
Eight years of rider measurement in the Android malware ecosystem: evolution and lessons learned
Despite the growing threat posed by Android malware,
the research community is still lacking a comprehensive
view of common behaviors and trends exposed by malware families
active on the platform. Without such view, the researchers
incur the risk of developing systems that only detect outdated
threats, missing the most recent ones. In this paper, we conduct
the largest measurement of Android malware behavior to date,
analyzing over 1.2 million malware samples that belong to 1.2K
families over a period of eight years (from 2010 to 2017). We
aim at understanding how the behavior of Android malware
has evolved over time, focusing on repackaging malware. In
this type of threats different innocuous apps are piggybacked
with a malicious payload (rider), allowing inexpensive malware
manufacturing.
One of the main challenges posed when studying repackaged
malware is slicing the app to split benign components apart from
the malicious ones. To address this problem, we use differential
analysis to isolate software components that are irrelevant to the
campaign and study the behavior of malicious riders alone. Our
analysis framework relies on collective repositories and recent
advances on the systematization of intelligence extracted from
multiple anti-virus vendors. We find that since its infancy in
2010, the Android malware ecosystem has changed significantly,
both in the type of malicious activity performed by the malicious
samples and in the level of obfuscation used by malware to avoid
detection. We then show that our framework can aid analysts
who attempt to study unknown malware families. Finally, we
discuss what our findings mean for Android malware detection
research, highlighting areas that need further attention by the
research community.Accepted manuscrip
POISED: Spotting Twitter Spam Off the Beaten Paths
Cybercriminals have found in online social networks a propitious medium to
spread spam and malicious content. Existing techniques for detecting spam
include predicting the trustworthiness of accounts and analyzing the content of
these messages. However, advanced attackers can still successfully evade these
defenses.
Online social networks bring people who have personal connections or share
common interests to form communities. In this paper, we first show that users
within a networked community share some topics of interest. Moreover, content
shared on these social network tend to propagate according to the interests of
people. Dissemination paths may emerge where some communities post similar
messages, based on the interests of those communities. Spam and other malicious
content, on the other hand, follow different spreading patterns.
In this paper, we follow this insight and present POISED, a system that
leverages the differences in propagation between benign and malicious messages
on social networks to identify spam and other unwanted content. We test our
system on a dataset of 1.3M tweets collected from 64K users, and we show that
our approach is effective in detecting malicious messages, reaching 91%
precision and 93% recall. We also show that POISED's detection is more
comprehensive than previous systems, by comparing it to three state-of-the-art
spam detection systems that have been proposed by the research community in the
past. POISED significantly outperforms each of these systems. Moreover, through
simulations, we show how POISED is effective in the early detection of spam
messages and how it is resilient against two well-known adversarial machine
learning attacks
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