Discovering Malware with Time Series Shapelets

Patri, Om Prasad; Wojnowicz, Michael; Wolff, Matt

doi:10.24251/hicss.2017.734

Cited by 22 publications

(9 citation statements)

References 27 publications

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“…DroidMat used k-means, naive bayes, k-nearest neighbour algorithms and accomplished the highest f-measure of 91.8% [27]. Patri et al performed entropy analysis and shapelet-based classification for PE files [15]. Wenjia Li et al in 2018 used kirin rules on Drebin to achieve a recall of 94.29% [11].…”

Section: Related Workmentioning

confidence: 99%

Identification of Significant Permissions for Efficient Android Malware Detection

Rathore¹,

Sahay²,

Rajvanshi³

et al. 2021

Preprint

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Since Google unveiled Android OS for smartphones, malware are thriving with 3Vs, i.e. volume, velocity and variety. A recent report indicates that one out of every five business/industry mobile application leaks sensitive personal data. Traditional signature/heuristic based malware detection systems are unable to cope up with current malware challenges and thus threaten the Android ecosystem. Therefore recently researchers have started exploring machine learning and deep learning based malware detection systems. In this paper, we performed a comprehensive feature analysis to identify the significant Android permissions and propose an efficient Android malware detection system using machine learning and deep neural network. We constructed a set of 16 permissions (8% of the total set) derived from variance threshold, auto-encoders, and principal component analysis to build a malware detection engine which consumes less train and test time without significant compromise on the model accuracy. Our experimental results show that the Android malware detection model based on the random forest classifier is most balanced and achieves the highest area under curve score of 97.7%, which is better than the current state-of-art systems. We also observed that deep neural networks attain comparable accuracy to the baseline results but with a massive computational penalty.

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Section: Related Workmentioning

confidence: 99%

Identification of Significant Permissions for Efficient Android Malware Detection

Rathore¹,

Sahay²,

Rajvanshi³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Learning from imbalanced classes can naturally cause the algorithm to favor the more numerous class, but also be detrimental in failing to learn how to properly separate the classes. Class imbalance is a common problem in the malware domain (Patri et al, 2017;Cross and Munson, 2011;Li et al, 2017;Yan et al, 2013;Moskovitch et al, 2009a;Yan, 2015), which makes it especially important to consider the evaluation metric used for both malware detection and family classification (as mentioned in subsection 7.1 and subsection 7.2).…”

Section: Multiple Views Of Malwarementioning

confidence: 99%

A Survey of Machine Learning Methods and Challenges for Windows Malware Classification

Raff,

Nicholas

2020

Preprint

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Malware classification is a difficult problem, to which machine learning methods have been applied for decades. Yet progress has often been slow, in part due to a number of unique difficulties with the task that occur through all stages of the developing a machine learning system: data collection, labeling, feature creation and selection, model selection, and evaluation. In this survey we will review a number of the current methods and challenges related to malware classification, including data collection, feature extraction, and model construction, and evaluation. Our discussion will include thoughts on the constraints that must be considered for machine learning based solutions in this domain, and yet to be tackled problems for which machine learning could also provide a solution. This survey aims to be useful both to cybersecurity practitioners who wish to learn more about how machine learning can be applied to the malware problem, and to give data scientists the necessary background into the challenges in this uniquely complicated space.

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“…Los sistemas de detección actuales utilizan varios algoritmos: clasificador Naive Bayes [12], Support Vector Machine [13], Random Forest [14], redes neuronales profundas [15], redes neuronales convolucionales [16], redes long-short term memory (LSTM) [17,18] y otros [19][20][21][22][23].…”

Section: Malwareunclassified

Ciberseguridad : el reto del siglo XXI

2019

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Discovering Malware with Time Series Shapelets

Abstract: Malicious software ('malware')

Cited by 22 publications

References 27 publications

Identification of Significant Permissions for Efficient Android Malware Detection

Identification of Significant Permissions for Efficient Android Malware Detection

A Survey of Machine Learning Methods and Challenges for Windows Malware Classification

Ciberseguridad : el reto del siglo XXI

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