Malware Detection by Eating a Whole EXE

Raff, Edward; Barker, Jon; Sylvester, Jared; Brandon, Robert N.; Catanzaro, Bryan; Nicholas, Charles

doi:10.48550/arxiv.1710.09435

Cited by 49 publications

(108 citation statements)

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“…Raff et al [16] propose MalConv, a deep learning model which discriminates programs based on their byte representation, without extracting any feature. The intuition of this approach is based on spatial properties of binary programs: (i) code, and data may be mixed, and it is difficult to extract proper features; (ii) there is correlation between different portions of the input program; (iii) binaries may have different length, as they are strings of bytes.…”

Section: Deep Learning For Malware Detection In Binary Filesmentioning

confidence: 99%

“…In particular, we rely upon an explainable technique known as feature attribution [12] to identify the most influential input features contributing to each decision. We focus on a case study related to the detection of Windows Portable Executable (PE) malware files, using a recently-proposed convolutional neural network named MalConv [16]. This network is trained directly on the raw input bytes to discriminate between malicious and benign PE files, reporting good classification accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…The structure of this paper is the following: in Section 2 we introduce how to solve the problem of malware detection using machine learning techniques, and we present the architecture of MalConv; (ii) we introduce the integrated gradient technique as an algorithm that extracts which features contributes most [16], adapted from [9]. for the classification problem; (iii) we collect the result of the method mentioned above applied on MalConv, highlighting its weaknesses, and (iv) we show how an adversary may exploit this information to craft an attack against MalConv.…”

Section: Introductionmentioning

confidence: 99%

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Explaining Vulnerabilities of Deep Learning to Adversarial Malware Binaries

Demetrio¹,

Biggio²,

Lagorio³

et al. 2019

Preprint

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Recent work has shown that deep-learning algorithms for malware detection are also susceptible to adversarial examples, i.e., carefullycrafted perturbations to input malware that enable misleading classification. Although this has questioned their suitability for this task, it is not yet clear why such algorithms are easily fooled also in this particular application domain. In this work, we take a first step to tackle this issue by leveraging explainable machine-learning algorithms developed to interpret the black-box decisions of deep neural networks. In particular, we use an explainable technique known as feature attribution to identify the most influential input features contributing to each decision, and adapt it to provide meaningful explanations to the classification of malware binaries. In this case, we find that a recently-proposed convolutional neural network does not learn any meaningful characteristic for malware detection from the data and text sections of executable files, but rather tends to learn to discriminate between benign and malware samples based on the characteristics found in the file header. Based on this finding, we propose a novel attack algorithm that generates adversarial malware binaries by only changing few tens of bytes in the file header. With respect to the other state-of-the-art attack algorithms, our attack does not require injecting any padding bytes at the end of the file, and it is much more efficient, as it requires manipulating much fewer bytes.

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Section: Deep Learning For Malware Detection In Binary Filesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Explaining Vulnerabilities of Deep Learning to Adversarial Malware Binaries

Demetrio¹,

Biggio²,

Lagorio³

et al. 2019

Preprint

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“…In this regard, Anderson et al [4] provide Ember, a very good dataset to train ML algorithms. On the other hand, Raff et al [5] use Natural Language Processing tools to analyse bit sequences extracted from binary files. Their MalConv algorithm gives very good results but requires a lot of computing power to train it.…”

Section: State Of Artmentioning

confidence: 99%

Malware Analysis with Artificial Intelligence and a Particular Attention on Results Interpretability

Marais¹,

Quertier²,

Chesneau³

2021

Preprint

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Malware detection and analysis are active research subjects in cybersecurity over the last years. Indeed, the development of obfuscation techniques, as packing, for example, requires special attention to detect recent variants of malware. The usual detection methods do not necessarily provide tools to interpret the results. Therefore, we propose a model based on the transformation of binary files into grayscale image, which achieves an accuracy rate of 88%. Furthermore, the proposed model can determine if a sample is packed or encrypted with a precision of 85%. It allows us to analyze results and act appropriately. Also, by applying attention mechanisms on detection models, we have the possibility to identify which part of the files looks suspicious. This kind of tool should be very useful for data analysts, it compensates for the lack of interpretability of the common detection models, and it can help to understand why some malicious files are undetected.

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“…6) Slack Attacks: A byte-based convolutional neural network (MalConv) was introduced in [64]. Unlike image perturbation attacks [29], where the fidelity of the image is of little concern, attacks that alter the binaries of malware files must maintain the semantic fidelity of the original file because altering the bytes of the malware arbitrarily could affect the malicious effect of the malware.…”

Section: A Adversarial Attacks On ML For Endpoint Protectionmentioning

confidence: 99%

The Threat of Adversarial Attacks on Machine Learning in Network Security -- A Survey

Ibitoye¹,

Rana²,

Shehaby³

et al. 2019

Preprint

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Machine learning models have made many decision support systems to be faster, more accurate and more efficient. However, applications of machine learning in network security face more disproportionate threat of active adversarial attacks compared to other domains. This is because machine learning applications in network security such as malware detection, intrusion detection, and spam filtering are by themselves adversarial in nature. In what could be considered an arms race between attackers and defenders, adversaries constantly probe machine learning systems with inputs which are explicitly designed to bypass the system and induce a wrong prediction. In this survey, we first provide a taxonomy of machine learning techniques, styles, and algorithms. We then introduce a classification of machine learning in network security applications. Next, we examine various adversarial attacks against machine learning in network security and introduce two classification approaches for adversarial attacks in network security. First, we classify adversarial attacks in network security based on a taxonomy of network security applications. Secondly, we categorize adversarial attacks in network security into a problem space vs. feature space dimensional classification model. We then analyze the various defenses against adversarial attacks on machine learning-based network security applications. We conclude by introducing an adversarial risk model and evaluate several existing adversarial attacks against machine learning in network security using the risk model. We also identify where each attack classification resides within the adversarial risk model.

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Malware Detection by Eating a Whole EXE

Cited by 49 publications

References 0 publications

Explaining Vulnerabilities of Deep Learning to Adversarial Malware Binaries

Explaining Vulnerabilities of Deep Learning to Adversarial Malware Binaries

Malware Analysis with Artificial Intelligence and a Particular Attention on Results Interpretability

The Threat of Adversarial Attacks on Machine Learning in Network Security -- A Survey

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