Imbalanced Malware Images Classification: a CNN based Approach

Yue, Songqing

doi:10.48550/arxiv.1708.08042

Cited by 25 publications

(25 citation statements)

References 8 publications

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“…The first category of papers referenced the challenge to either perform an abstract comparison or highlight the importance of machine learning for malware classification in industry, where the size of data is huge [43,19,28,47,18,38,49,44,25,53,46,21,4,57,16,17,39,50]. Papers in the second category performed partial or complete evaluation on the dataset to verify the effectiveness and/or efficiency of their proposed approach for various tasks.…”

Section: Citations Comparisonmentioning

confidence: 99%

Novel Feature Extraction, Selection and Fusion for Effective Malware Family Classification

Ahmadi

Ulyanov

Semenov

et al. 2016

Proceedings of the Sixth ACM Conference on Data and Application Security and Privacy

283

198

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Modern malware is designed with mutation characteristics, namely polymorphism and metamorphism, which causes an enormous growth in the number of variants of malware samples. Categorization of malware samples on the basis of their behaviors is essential for the computer security community, because they receive huge number of malware everyday, and the signature extraction process is usually based on malicious parts characterizing malware families. Microsoft released a malware classification challenge in 2015 with a huge dataset of near 0.5 terabytes of data, containing more than 20K malware samples. The analysis of this dataset inspired the development of a novel paradigm that is effective in categorizing malware variants into their actual family groups. This paradigm is presented and discussed in the present paper, where emphasis has been given to the phases related to the extraction, and selection of a set of novel features for the effective representation of malware samples. Features can be grouped according to different characteristics of malware behavior, and their fusion is performed according to a per-class weighting paradigm. The proposed method achieved a very high accuracy ($\approx$ 0.998) on the Microsoft Malware Challenge dataset

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Section: Citations Comparisonmentioning

confidence: 99%

Novel Feature Extraction, Selection and Fusion for Effective Malware Family Classification

Ahmadi

Ulyanov

Semenov

et al. 2016

Proceedings of the Sixth ACM Conference on Data and Application Security and Privacy

283

198

View full text Add to dashboard Cite

show abstract

“…We then tested performance for different combinations of the training components. We considered mean squared error and smooth-L1 loss, which we weighted by class frequency to remove the bias introduced by the imbalance of the dataset 43 . In addition to the standard optimisation algorithm SGD, other optimisers such as Adaptive Moment Estimation (Adam) 44 , Rectified Adam (RAdam) 45 and Adam with a corrected weight decay algorithm (AdamW) 46 were tested.…”

Section: Machine Learningmentioning

confidence: 99%

Automating the assessment of biofouling in images using expert agreement as a gold standard

Bloomfield,

Wei,

Woodham

et al. 2020

Preprint

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Biofouling is the accumulation of organisms on surfaces immersed in water. It is of particular concern to the international shipping industry because fouling increases the drag on vessels as they move through the water, resulting in higher fuel costs, and presents a biosecurity risk by providing a pathway for marine non-indigenous species (NIS) to establish in new areas. There is growing interest within jurisdictions to strengthen biofouling risk-management regulations, but it is expensive to conduct in-water inspections and assess the collected data to determine the biofouling state of vessel hulls. Machine learning is well suited to tackle the latter challenge, and here we apply so-called deep learning to automate the classification of images from in-water inspections for the presence and severity of biofouling. We combined images collected from in-water surveys conducted by the Australian Department of Agriculture, Water and the Environment, the New Zealand Ministry for Primary Industries and the California State Lands Commission, and annotated them using the Amazon Mechanical Turk (MTurk) crowdsourcing platform. We compared the annotations from three biofouling experts on a 120-sample subset of these images, and found that for two tasks, identifying images containing fouling, and identifying images containing heavy fouling, they showed 89% agreement (95% CI: 87-92%). It was found that the MTurk labelling approach achieved similar agreement with experts, which we defined as performing at most 5% worse than experts (p=0.004-0.020). If used in future this approach could offer significant cost savings over relying on expert consultants to annotate similar data. Our deep learning model trained with the MTurk annotations also showed reasonable performance in comparison to expert agreement, although at a lower significance level (p=0.071-0.093). We also demonstrate that significantly better performance than expert agreement can be achieved if a classifier with high recall or precision was required.

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“…Table 3 shows the test performance of the proposed models and others for Malimg dataset. Yue [24] uses a weighted loss function to handle im- 4 compares the test performance of the proposed models and others for BIG2015 dataset. Chen [30] and Khan et al [33] use transfer learning architectures for the dataset.…”

Section: Capsnetmentioning

confidence: 99%

Random CapsNet Forest Model for Imbalanced Malware Type Classification Task

Çayır¹,

Ünal²,

Dağ³

2019

Preprint

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Behavior of a malware varies with respect to malware types. Therefore, knowing type of a malware affects strategies of system protection softwares. Many malware type classification models empowered by machine and deep learning achieve superior accuracies to predict malware types. Machine learning based models need to do heavy feature engineering and feature engineering is dominantly effecting performance of models. On the other hand, deep learning based models require less feature engineering than machine learning based models. However, traditional deep learning architectures and components cause very complex and data sensitive models. Capsule network architecture minimizes this complexity and data sensitivity unlike classical convolutional neural network architectures. This paper proposes an ensemble capsule network model based on bootstrap aggregating technique. The proposed method are tested on two malware datasets, whose the-state-of-the-art results are well-known.

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Imbalanced Malware Images Classification: a CNN based Approach

Cited by 25 publications

References 8 publications

Novel Feature Extraction, Selection and Fusion for Effective Malware Family Classification

Novel Feature Extraction, Selection and Fusion for Effective Malware Family Classification

Automating the assessment of biofouling in images using expert agreement as a gold standard

Random CapsNet Forest Model for Imbalanced Malware Type Classification Task

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