4,948 research outputs found
A malware instruction set for behavior-based analysis
We introduce a new representation for monitored behavior of malicious software called Malware Instruction Set (MIST). The representation is optimized for effective and efficient analysis of behavior using data mining and machine learning techniques. It can be obtained automatically during analysis of malware with a behavior monitoring tool or by converting existing behavior reports. The representation is not restricted to a particular monitoring tool and thus can also be used as a meta language to unify behavior reports of different sources
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
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
Unsupervised Anomaly-based Malware Detection using Hardware Features
Recent works have shown promise in using microarchitectural execution
patterns to detect malware programs. These detectors belong to a class of
detectors known as signature-based detectors as they catch malware by comparing
a program's execution pattern (signature) to execution patterns of known
malware programs. In this work, we propose a new class of detectors -
anomaly-based hardware malware detectors - that do not require signatures for
malware detection, and thus can catch a wider range of malware including
potentially novel ones. We use unsupervised machine learning to build profiles
of normal program execution based on data from performance counters, and use
these profiles to detect significant deviations in program behavior that occur
as a result of malware exploitation. We show that real-world exploitation of
popular programs such as IE and Adobe PDF Reader on a Windows/x86 platform can
be detected with nearly perfect certainty. We also examine the limits and
challenges in implementing this approach in face of a sophisticated adversary
attempting to evade anomaly-based detection. The proposed detector is
complementary to previously proposed signature-based detectors and can be used
together to improve security.Comment: 1 page, Latex; added description for feature selection in Section 4,
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