Malware images

Nataraj, Lakshmanan; Karthikeyan, S.; Jacob, Grégoire; Manjunath, B.S.

doi:10.1145/2016904.2016908

Cited by 833 publications

(252 citation statements)

References 20 publications

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“…Finally, motivated by Ahmadi's work (Ahmadi et al, 2016), and with similarities to (Nataraj et al, 2011), Gibert (Gibert Llaurad o, 2016) applied a CNN to malware bytecode represented as two dimensional greyscale images. A similar down sampling approach as we employed was applied to normalize the size of each sample to 32 x 32 pixels.…”

Section: Features Extracted From Executed Codementioning

confidence: 99%

“…Overviews of the applications of AI to security and digital forensics are provided in (Franke and Srihari, 2008) and (Mitchell, 2014). A number of approaches have been implemented to aid digital forensic investigation through AI techniques (Mohammed et al, 2016;Rughani and Bhatt, 2017), automation (In de Braekt et al, 2016), and big data processing (Guarino, 2013).…”

Section: Introductionmentioning

confidence: 99%

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Deep learning at the shallow end: Malware classification for non-domain experts

Boydell

Namee

et al. 2018

Digital Investigation

156

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a b s t r a c tCurrent malware detection and classification approaches generally rely on time consuming and knowledge intensive processes to extract patterns (signatures) and behaviors from malware, which are then used for identification. Moreover, these signatures are often limited to local, contiguous sequences within the data whilst ignoring their context in relation to each other and throughout the malware file as a whole. We present a Deep Learning based malware classification approach that requires no expert domain knowledge and is based on a purely data driven approach for complex pattern and feature identification.

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Section: Features Extracted From Executed Codementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep learning at the shallow end: Malware classification for non-domain experts

Boydell

Namee

et al. 2018

Digital Investigation

156

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“…Although works mentioned above [13], [14], [15], [16] and [17] are helpful to detect and classify new malware and their variants, still they have some limitations. For instance, on the one hand, global texture features lose local information needed for classification.…”

Section: Related Workmentioning

confidence: 99%

A Cross-Platform Malware Variant Classification based on Image Representation

2019

KSII TIIS

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Recent internet development is helping malware researchers to generate malicious code variants through automated tools. Due to this reason, the number of malicious variants is increasing day by day. Consequently, the performance improvement in malware analysis is the critical requirement to stop the rapid expansion of malware. The existing research proved that the similarities among malware variants could be used for detection and family classification. In this paper, a Cross-Platform Malware Variant Classification System (CP-MVCS) proposed that converted malware binary into a grayscale image. Further, malicious features extracted from the grayscale image through Combined SIFT-GIST Malware (CSGM) description. Later, these features used to identify the relevant family of malware variant. CP-MVCS reduced computational time and improved classification accuracy by using CSGM feature description along machine learning classification. The experiment performed on four publically available datasets of Windows OS and Android OS. The experimental results showed that the computation time and malware classification accuracy of CP-MVCS was higher than traditional methods. The evaluation also showed that CP-MVCS was not only differentiated families of malware variants but also identified both malware and benign samples in mix fashion efficiently.

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“…In [11] and [12], the authors used function based features for detecting and classifying malwares. Nataraj et al [13] used image processing techniques for visualizing malwares. They used K-nearest neighbor method with Euclidean distance for malware classification.…”

Section: Static Analysis Featuresmentioning

confidence: 99%

Integrated Framework for Classification of Malwares

Gandotra

Bansal

Sofat

2014

Proceedings of the 7th International Conference on Security of Information and Networks

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Malware is one of the most terrible and major security threats facing the Internet today. It is evolving, becoming more sophisticated and using new ways to target computers and mobile devices.The traditional defences like antivirus softwares typically rely on signature based methods and are unable to detect previously unseen malwares. Machine learning approaches have been adopted to classify malwares based on the features extracted using static or dynamic analysis. Both type of malware analysis have their pros and cons. In this paper, we propose a classification framework which uses integration of both static and dynamic features for distinguishing malwares from clean files. A real world corpus of recent malwares is used to validate the proposed approach. The experimental results, based on a dataset of 998 malwares and 428 cleanware files provide an accuracy of 99.58% indicating that the hybrid approach enhances the accuracy rate of malware detection and classification over the results obtained when these features are considered separately.

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Malware images

Cited by 833 publications

References 20 publications

Deep learning at the shallow end: Malware classification for non-domain experts

Deep learning at the shallow end: Malware classification for non-domain experts

A Cross-Platform Malware Variant Classification based on Image Representation

Integrated Framework for Classification of Malwares

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