3,405 research outputs found
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
Graph Kernels via Functional Embedding
We propose a representation of graph as a functional object derived from the
power iteration of the underlying adjacency matrix. The proposed functional
representation is a graph invariant, i.e., the functional remains unchanged
under any reordering of the vertices. This property eliminates the difficulty
of handling exponentially many isomorphic forms. Bhattacharyya kernel
constructed between these functionals significantly outperforms the
state-of-the-art graph kernels on 3 out of the 4 standard benchmark graph
classification datasets, demonstrating the superiority of our approach. The
proposed methodology is simple and runs in time linear in the number of edges,
which makes our kernel more efficient and scalable compared to many widely
adopted graph kernels with running time cubic in the number of vertices
Dimensionality of social networks using motifs and eigenvalues
We consider the dimensionality of social networks, and develop experiments
aimed at predicting that dimension. We find that a social network model with
nodes and links sampled from an -dimensional metric space with power-law
distributed influence regions best fits samples from real-world networks when
scales logarithmically with the number of nodes of the network. This
supports a logarithmic dimension hypothesis, and we provide evidence with two
different social networks, Facebook and LinkedIn. Further, we employ two
different methods for confirming the hypothesis: the first uses the
distribution of motif counts, and the second exploits the eigenvalue
distribution.Comment: 26 page
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