1,476 research outputs found
PDF-Malware Detection: A Survey and Taxonomy of Current Techniques
Portable Document Format, more commonly known as PDF, has become, in the last 20 years, a standard for document exchange and dissemination due its portable nature and widespread adoption. The flexibility and power of this format are not only leveraged by benign users, but from hackers as well who have been working to exploit various types of vulnerabilities, overcome security restrictions, and then transform the PDF format in one among the leading malicious code spread vectors. Analyzing the content of malicious PDF files to extract the main features that characterize the malware identity and behavior, is a fundamental task for modern threat intelligence platforms that need to learn how to automatically identify new attacks. This paper surveys existing state of the art about systems for the detection of malicious PDF files and organizes them in a taxonomy that separately considers the used approaches and the data analyzed to detect the presence of malicious code. © Springer International Publishing AG, part of Springer Nature 2018
On the Feasibility of Malware Authorship Attribution
There are many occasions in which the security community is interested to
discover the authorship of malware binaries, either for digital forensics
analysis of malware corpora or for thwarting live threats of malware invasion.
Such a discovery of authorship might be possible due to stylistic features
inherent to software codes written by human programmers. Existing studies of
authorship attribution of general purpose software mainly focus on source code,
which is typically based on the style of programs and environment. However,
those features critically depend on the availability of the program source
code, which is usually not the case when dealing with malware binaries. Such
program binaries often do not retain many semantic or stylistic features due to
the compilation process. Therefore, authorship attribution in the domain of
malware binaries based on features and styles that will survive the compilation
process is challenging. This paper provides the state of the art in this
literature. Further, we analyze the features involved in those techniques. By
using a case study, we identify features that can survive the compilation
process. Finally, we analyze existing works on binary authorship attribution
and study their applicability to real malware binaries.Comment: FPS 201
Understanding Android Obfuscation Techniques: A Large-Scale Investigation in the Wild
In this paper, we seek to better understand Android obfuscation and depict a
holistic view of the usage of obfuscation through a large-scale investigation
in the wild. In particular, we focus on four popular obfuscation approaches:
identifier renaming, string encryption, Java reflection, and packing. To obtain
the meaningful statistical results, we designed efficient and lightweight
detection models for each obfuscation technique and applied them to our massive
APK datasets (collected from Google Play, multiple third-party markets, and
malware databases). We have learned several interesting facts from the result.
For example, malware authors use string encryption more frequently, and more
apps on third-party markets than Google Play are packed. We are also interested
in the explanation of each finding. Therefore we carry out in-depth code
analysis on some Android apps after sampling. We believe our study will help
developers select the most suitable obfuscation approach, and in the meantime
help researchers improve code analysis systems in the right direction
Generic Black-Box End-to-End Attack Against State of the Art API Call Based Malware Classifiers
In this paper, we present a black-box attack against API call based machine
learning malware classifiers, focusing on generating adversarial sequences
combining API calls and static features (e.g., printable strings) that will be
misclassified by the classifier without affecting the malware functionality. We
show that this attack is effective against many classifiers due to the
transferability principle between RNN variants, feed forward DNNs, and
traditional machine learning classifiers such as SVM. We also implement GADGET,
a software framework to convert any malware binary to a binary undetected by
malware classifiers, using the proposed attack, without access to the malware
source code.Comment: Accepted as a conference paper at RAID 201
An investigation of a deep learning based malware detection system
We investigate a Deep Learning based system for malware detection. In the
investigation, we experiment with different combination of Deep Learning
architectures including Auto-Encoders, and Deep Neural Networks with varying
layers over Malicia malware dataset on which earlier studies have obtained an
accuracy of (98%) with an acceptable False Positive Rates (1.07%). But these
results were done using extensive man-made custom domain features and investing
corresponding feature engineering and design efforts. In our proposed approach,
besides improving the previous best results (99.21% accuracy and a False
Positive Rate of 0.19%) indicates that Deep Learning based systems could
deliver an effective defense against malware. Since it is good in automatically
extracting higher conceptual features from the data, Deep Learning based
systems could provide an effective, general and scalable mechanism for
detection of existing and unknown malware.Comment: 13 Pages, 4 figure
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