3,536 research outputs found
Machine Learning Aided Static Malware Analysis: A Survey and Tutorial
Malware analysis and detection techniques have been evolving during the last
decade as a reflection to development of different malware techniques to evade
network-based and host-based security protections. The fast growth in variety
and number of malware species made it very difficult for forensics
investigators to provide an on time response. Therefore, Machine Learning (ML)
aided malware analysis became a necessity to automate different aspects of
static and dynamic malware investigation. We believe that machine learning
aided static analysis can be used as a methodological approach in technical
Cyber Threats Intelligence (CTI) rather than resource-consuming dynamic malware
analysis that has been thoroughly studied before. In this paper, we address
this research gap by conducting an in-depth survey of different machine
learning methods for classification of static characteristics of 32-bit
malicious Portable Executable (PE32) Windows files and develop taxonomy for
better understanding of these techniques. Afterwards, we offer a tutorial on
how different machine learning techniques can be utilized in extraction and
analysis of a variety of static characteristic of PE binaries and evaluate
accuracy and practical generalization of these techniques. Finally, the results
of experimental study of all the method using common data was given to
demonstrate the accuracy and complexity. This paper may serve as a stepping
stone for future researchers in cross-disciplinary field of machine learning
aided malware forensics.Comment: 37 Page
Malware Detection using Machine Learning and Deep Learning
Research shows that over the last decade, malware has been growing
exponentially, causing substantial financial losses to various organizations.
Different anti-malware companies have been proposing solutions to defend
attacks from these malware. The velocity, volume, and the complexity of malware
are posing new challenges to the anti-malware community. Current
state-of-the-art research shows that recently, researchers and anti-virus
organizations started applying machine learning and deep learning methods for
malware analysis and detection. We have used opcode frequency as a feature
vector and applied unsupervised learning in addition to supervised learning for
malware classification. The focus of this tutorial is to present our work on
detecting malware with 1) various machine learning algorithms and 2) deep
learning models. Our results show that the Random Forest outperforms Deep
Neural Network with opcode frequency as a feature. Also in feature reduction,
Deep Auto-Encoders are overkill for the dataset, and elementary function like
Variance Threshold perform better than others. In addition to the proposed
methodologies, we will also discuss the additional issues and the unique
challenges in the domain, open research problems, limitations, and future
directions.Comment: 11 Pages and 3 Figure
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
Automatically combining static malware detection techniques
Malware detection techniques come in many different flavors, and cover different effectiveness and efficiency trade-offs. This paper evaluates a number of machine learning techniques to combine multiple static Android malware detection techniques using automatically constructed decision trees. We identify the best methods to construct the trees. We demonstrate that those trees classify sample apps better and faster than individual techniques alone
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