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Journal ArticleDOI

A survey of malware behavior description and analysis

TLDR
This paper conducts a survey on malware behavior description and analysis considering three aspects: malware behavior described, behavior analysis methods, and visualization techniques.
Abstract
Behavior-based malware analysis is an important technique for automatically analyzing and detecting malware, and it has received considerable attention from both academic and industrial communities. By considering how malware behaves, we can tackle the malware obfuscation problem, which cannot be processed by traditional static analysis approaches, and we can also derive the as-built behavior specifications and cover the entire behavior space of the malware samples. Although there have been several works focusing on malware behavior analysis, such research is far from mature, and no overviews have been put forward to date to investigate current developments and challenges. In this paper, we conduct a survey on malware behavior description and analysis considering three aspects: malware behavior description, behavior analysis methods, and visualization techniques. First, existing behavior data types and emerging techniques for malware behavior description are explored, especially the goals, principles, characteristics, and classifications of behavior analysis techniques proposed in the existing approaches. Second, the inadequacies and challenges in malware behavior analysis are summarized from different perspectives. Finally, several possible directions are discussed for future research.

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Citations
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Journal ArticleDOI

Dynamic Analysis for IoT Malware Detection With Convolution Neural Network Model

TL;DR: A dynamic analysis for IoT malware detection (DAIMD) is proposed to reduce damage to IoT devices by detecting both well-known IoT malware and new and variant IoT malware evolved intelligently.
Journal ArticleDOI

Tight Arms Race: Overview of Current Malware Threats and Trends in Their Detection

TL;DR: A detailed meta-review of the existing surveys related to malware and its detection techniques, showing an arms race between these two sides of a barricade, is presented in this article.
Journal ArticleDOI

Crypto-ransomware early detection model using novel incremental bagging with enhanced semi-random subspace selection

TL;DR: Two novel techniques; incremental bagging (iBagging) and enhanced semi-random subspace selection (ESRS) are proposed and incorporates them into an ensemble-based detection model and achieved higher detection accuracy than existing solutions.
Journal ArticleDOI

A Pseudo Feedback-Based Annotated TF-IDF Technique for Dynamic Crypto-Ransomware Pre-Encryption Boundary Delineation and Features Extraction

TL;DR: A Dynamic Pre-encryption Boundary Delineation and Feature Extraction (DPBD-FE) scheme that determines the boundary of the pre-enc encryption phase, from which the features are extracted and selected more accurately compared to related works is proposed.
Book ChapterDOI

Analysis and Evaluation of Dynamic Feature-Based Malware Detection Methods

TL;DR: The main objective is to find more discriminative dynamic features to detect malware executables by analyzing different dynamic features with common malware detection approaches by evaluating some dynamic feature-based malware detection and classification approaches.
References
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Proceedings Article

Scalable, behavior-based malware clustering

Ulrich Bayer
TL;DR: Recent researchers have started to explore automated clustering techniques that help to identify samples that exhibit similar behavior, which allows an analyst to discard reports of samples that have been seen before, while focusing on novel, interesting threats.
Journal ArticleDOI

Automatic analysis of malware behavior using machine learning

TL;DR: An incremental approach for behavior-based analysis, capable of processing the behavior of thousands of malware binaries on a daily basis is proposed, significantly reduces the run-time overhead of current analysis methods, while providing accurate discovery and discrimination of novel malware variants.
Book ChapterDOI

Learning and Classification of Malware Behavior

TL;DR: The effectiveness of the proposed method for learning and discrimination of malware behavior is demonstrated, especially in detecting novel instances of malware families previously not recognized by commercial anti-virus software.
Book ChapterDOI

Automated classification and analysis of internet malware

TL;DR: This paper examines the ability of existing host-based anti-virus products to provide semantically meaningful information about the malicious software and tools used by attackers and proposes a new classification technique that describes malware behavior in terms of system state changes rather than in sequences or patterns of system calls.
Proceedings ArticleDOI

DroidMat: Android Malware Detection through Manifest and API Calls Tracing

TL;DR: A static feature-based mechanism to provide a static analyst paradigm for detecting the Android malware and shows that the recall rate of the approach is better than one of well-known tool, Androguard, published in Black hat 2011, which focuses on Android malware analysis.
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