7,189 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
Survey of Machine Learning Techniques for Malware Analysis
Coping with malware is getting more and more challenging, given their
relentless growth in complexity and volume. One of the most common approaches
in literature is using machine learning techniques, to automatically learn
models and patterns behind such complexity, and to develop technologies for
keeping pace with the speed of development of novel malware. This survey aims
at providing an overview on the way machine learning has been used so far in
the context of malware analysis. We systematize surveyed papers according to
their objectives (i.e., the expected output, what the analysis aims to), what
information about malware they specifically use (i.e., the features), and what
machine learning techniques they employ (i.e., what algorithm is used to
process the input and produce the output). We also outline a number of problems
concerning the datasets used in considered works, and finally introduce the
novel concept of malware analysis economics, regarding the study of existing
tradeoffs among key metrics, such as analysis accuracy and economical costs
Evolution and Detection of Polymorphic and Metamorphic Malwares: A Survey
Malwares are big threat to digital world and evolving with high complexity.
It can penetrate networks, steal confidential information from computers, bring
down servers and can cripple infrastructures etc. To combat the threat/attacks
from the malwares, anti- malwares have been developed. The existing
anti-malwares are mostly based on the assumption that the malware structure
does not changes appreciably. But the recent advancement in second generation
malwares can create variants and hence posed a challenge to anti-malwares
developers. To combat the threat/attacks from the second generation malwares
with low false alarm we present our survey on malwares and its detection
techniques.Comment: 5 Page
A Comparison of Clustering Techniques for Malware Analysis
In this research, we apply clustering techniques to the malware detection problem. Our goal is to classify malware as part of a fully automated detection strategy. We compute clusters using the well-known �-means and EM clustering algorithms, with scores obtained from Hidden Markov Models (HMM). The previous work in this area consists of using HMM and �-means clustering technique to achieve the same. The current effort aims to extend it to use EM clustering technique for detection and also compare this technique with the �-means clustering
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