24,994 research outputs found
Graph Summarization
The continuous and rapid growth of highly interconnected datasets, which are
both voluminous and complex, calls for the development of adequate processing
and analytical techniques. One method for condensing and simplifying such
datasets is graph summarization. It denotes a series of application-specific
algorithms designed to transform graphs into more compact representations while
preserving structural patterns, query answers, or specific property
distributions. As this problem is common to several areas studying graph
topologies, different approaches, such as clustering, compression, sampling, or
influence detection, have been proposed, primarily based on statistical and
optimization methods. The focus of our chapter is to pinpoint the main graph
summarization methods, but especially to focus on the most recent approaches
and novel research trends on this topic, not yet covered by previous surveys.Comment: To appear in the Encyclopedia of Big Data Technologie
Fluid Communities: A Competitive, Scalable and Diverse Community Detection Algorithm
We introduce a community detection algorithm (Fluid Communities) based on the
idea of fluids interacting in an environment, expanding and contracting as a
result of that interaction. Fluid Communities is based on the propagation
methodology, which represents the state-of-the-art in terms of computational
cost and scalability. While being highly efficient, Fluid Communities is able
to find communities in synthetic graphs with an accuracy close to the current
best alternatives. Additionally, Fluid Communities is the first
propagation-based algorithm capable of identifying a variable number of
communities in network. To illustrate the relevance of the algorithm, we
evaluate the diversity of the communities found by Fluid Communities, and find
them to be significantly different from the ones found by alternative methods.Comment: Accepted at the 6th International Conference on Complex Networks and
Their Application
A Multi-view Context-aware Approach to Android Malware Detection and Malicious Code Localization
Existing Android malware detection approaches use a variety of features such
as security sensitive APIs, system calls, control-flow structures and
information flows in conjunction with Machine Learning classifiers to achieve
accurate detection. Each of these feature sets provides a unique semantic
perspective (or view) of apps' behaviours with inherent strengths and
limitations. Meaning, some views are more amenable to detect certain attacks
but may not be suitable to characterise several other attacks. Most of the
existing malware detection approaches use only one (or a selected few) of the
aforementioned feature sets which prevent them from detecting a vast majority
of attacks. Addressing this limitation, we propose MKLDroid, a unified
framework that systematically integrates multiple views of apps for performing
comprehensive malware detection and malicious code localisation. The rationale
is that, while a malware app can disguise itself in some views, disguising in
every view while maintaining malicious intent will be much harder.
MKLDroid uses a graph kernel to capture structural and contextual information
from apps' dependency graphs and identify malice code patterns in each view.
Subsequently, it employs Multiple Kernel Learning (MKL) to find a weighted
combination of the views which yields the best detection accuracy. Besides
multi-view learning, MKLDroid's unique and salient trait is its ability to
locate fine-grained malice code portions in dependency graphs (e.g.,
methods/classes). Through our large-scale experiments on several datasets
(incl. wild apps), we demonstrate that MKLDroid outperforms three
state-of-the-art techniques consistently, in terms of accuracy while
maintaining comparable efficiency. In our malicious code localisation
experiments on a dataset of repackaged malware, MKLDroid was able to identify
all the malice classes with 94% average recall
PeerHunter: Detecting Peer-to-Peer Botnets through Community Behavior Analysis
Peer-to-peer (P2P) botnets have become one of the major threats in network
security for serving as the infrastructure that responsible for various of
cyber-crimes. Though a few existing work claimed to detect traditional botnets
effectively, the problem of detecting P2P botnets involves more challenges. In
this paper, we present PeerHunter, a community behavior analysis based method,
which is capable of detecting botnets that communicate via a P2P structure.
PeerHunter starts from a P2P hosts detection component. Then, it uses mutual
contacts as the main feature to cluster bots into communities. Finally, it uses
community behavior analysis to detect potential botnet communities and further
identify bot candidates. Through extensive experiments with real and simulated
network traces, PeerHunter can achieve very high detection rate and low false
positives.Comment: 8 pages, 2 figures, 11 tables, 2017 IEEE Conference on Dependable and
Secure Computin
Scalable Approach to Uncertainty Quantification and Robust Design of Interconnected Dynamical Systems
Development of robust dynamical systems and networks such as autonomous
aircraft systems capable of accomplishing complex missions faces challenges due
to the dynamically evolving uncertainties coming from model uncertainties,
necessity to operate in a hostile cluttered urban environment, and the
distributed and dynamic nature of the communication and computation resources.
Model-based robust design is difficult because of the complexity of the hybrid
dynamic models including continuous vehicle dynamics, the discrete models of
computations and communications, and the size of the problem. We will overview
recent advances in methodology and tools to model, analyze, and design robust
autonomous aerospace systems operating in uncertain environment, with stress on
efficient uncertainty quantification and robust design using the case studies
of the mission including model-based target tracking and search, and trajectory
planning in uncertain urban environment. To show that the methodology is
generally applicable to uncertain dynamical systems, we will also show examples
of application of the new methods to efficient uncertainty quantification of
energy usage in buildings, and stability assessment of interconnected power
networks
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