6,534 research outputs found
Mal-Netminer: Malware Classification Approach based on Social Network Analysis of System Call Graph
As the security landscape evolves over time, where thousands of species of
malicious codes are seen every day, antivirus vendors strive to detect and
classify malware families for efficient and effective responses against malware
campaigns. To enrich this effort, and by capitalizing on ideas from the social
network analysis domain, we build a tool that can help classify malware
families using features driven from the graph structure of their system calls.
To achieve that, we first construct a system call graph that consists of system
calls found in the execution of the individual malware families. To explore
distinguishing features of various malware species, we study social network
properties as applied to the call graph, including the degree distribution,
degree centrality, average distance, clustering coefficient, network density,
and component ratio. We utilize features driven from those properties to build
a classifier for malware families. Our experimental results show that
influence-based graph metrics such as the degree centrality are effective for
classifying malware, whereas the general structural metrics of malware are less
effective for classifying malware. Our experiments demonstrate that the
proposed system performs well in detecting and classifying malware families
within each malware class with accuracy greater than 96%.Comment: Mathematical Problems in Engineering, Vol 201
Community Self Help
This paper advocates controlling crime through a greater emphasis on precautions taken not by individuals, but by communities. The dominant battles in the literature today posit two central competing models of crime control. In one, the standard policing model, the government is responsible for the variety of acts that are necessary to deter and prosecute criminal acts. In the other, private self-help, public law enforcement is largely supplanted by providing incentives to individuals to self-protect against crime. There are any number of nuances and complications in each of these competing stories, but the literature buys into this binary matrix
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
A nonuniform popularity-similarity optimization (nPSO) model to efficiently generate realistic complex networks with communities
The hidden metric space behind complex network topologies is a fervid topic
in current network science and the hyperbolic space is one of the most studied,
because it seems associated to the structural organization of many real complex
systems. The Popularity-Similarity-Optimization (PSO) model simulates how
random geometric graphs grow in the hyperbolic space, reproducing strong
clustering and scale-free degree distribution, however it misses to reproduce
an important feature of real complex networks, which is the community
organization. The Geometrical-Preferential-Attachment (GPA) model was recently
developed to confer to the PSO also a community structure, which is obtained by
forcing different angular regions of the hyperbolic disk to have variable level
of attractiveness. However, the number and size of the communities cannot be
explicitly controlled in the GPA, which is a clear limitation for real
applications. Here, we introduce the nonuniform PSO (nPSO) model that,
differently from GPA, forces heterogeneous angular node attractiveness by
sampling the angular coordinates from a tailored nonuniform probability
distribution, for instance a mixture of Gaussians. The nPSO differs from GPA in
other three aspects: it allows to explicitly fix the number and size of
communities; it allows to tune their mixing property through the network
temperature; it is efficient to generate networks with high clustering. After
several tests we propose the nPSO as a valid and efficient model to generate
networks with communities in the hyperbolic space, which can be adopted as a
realistic benchmark for different tasks such as community detection and link
prediction
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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