ALDROID: efficient update of Android anti-virus software using designated active learning methods

Nissim, Nir; Moskovitch, Robert; Barad, O.; Rokach, Lior; Elovici, Yuval

doi:10.1007/s10115-016-0918-z

Cited by 34 publications

(20 citation statements)

References 48 publications

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“…Detection of a misuse of loading new untrusted code loaded from untrusted Websites like Java binary code. [22]. Static detection framework is beneficial in efficiency, granularity, layers, as well as correctness [23].…”

Section: Static Analysismentioning

confidence: 99%

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A Survey on Android Malware Detection Techniques

Riasat¹,

Sakeena²,

Wang³

et al. 2017

dtcse

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Abstract. Now-a-days widely applicable smart phone application adoption and rapid growth of contextually sensitive nature of smartphone devices has led to a renaissance in mobile application services and increased concerns over smartphone malware. Most of the smart phone users are connected to internet for various purposes. Even after the advanced technology used in the smartphones still the users are remaining unprotected from malware attacks. To overcome these issues and to keep the smartphones safe a number of malicious applications, malware threat and adwares for mobile phones is expected to increase with the functionality enhancement of smart mobile phone. Android Operating Systems are most commonly used systems in the smartphones. Many applications are available in android play store and it is very difficult to distinguish and to discriminate between clean and malicious applications. We have briefly discussed different methods for detecting android malwares in this review and present a clear picture about these detection tools in the smartphone applications.

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Section: Static Analysismentioning

confidence: 99%

“…In 2013, Ham and Choi [15] proposed an approach which was based either on several features extracted dynamically or the detection of new Android malware using machine learning algorithms. [22].…”

Section: Dynamic Analysismentioning

confidence: 99%

A Survey on Android Malware Detection Techniques

Riasat¹,

Sakeena²,

Wang³

et al. 2017

dtcse

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“…AL has been shown to be successful in decreasing the amount of labeling requirements, compared to a traditional passive learning method, in many domains including the cyber security (25–27, 37–41, 68–71) and biomedical domains (30–33, 53–54). While labeling and learning with an active learner is often much more efficient and achieves higher classification accuracy with a smaller labeled training set, the learning curve may vary greatly according to the labeler’s expertise in the domain.…”

Section: Introductionmentioning

confidence: 99%

“…This selection is expected to decrease the number of conditions that experts need to manually review and label. Studies in several domains have successfully applied AL to reduce the resources (i.e., time and money) required for labeling examples (25, 26, 27,68,69,70,71,81,82, 83). AL is divided roughly into two major approaches: 1) membership queries (28) in which examples are artificially generated from the problem space; and 2) selective-sampling (29) in which examples are selected from a pool, which is the focus of this paper.…”

Section: Introductionmentioning

confidence: 99%

Inter-labeler and intra-labeler variability of condition severity classification models using active and passive learning methods

Nissim

Shaḥar

Elovici

et al. 2017

Artificial Intelligence in Medicine

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Background and Objectives Labeling instances by domain experts for classification is often time consuming and expensive. To reduce such labeling efforts, we had proposed the application of active learning (AL) methods, introduced our CAESAR-ALE framework for classifying the severity of clinical conditions, and shown its significant reduction of labeling efforts. The use of any of three AL methods (one well known [SVM-Margin], and two that we introduced [Exploitation and Combination_XA]) significantly reduced (by 48% to 64%) condition labeling efforts, compared to standard passive (random instance-selection) SVM learning. Furthermore, our new AL methods achieved maximal accuracy using 12% fewer labeled cases than the SVM-Margin AL method. However, because labelers have varying levels of expertise, a major issue associated with learning methods, and AL methods in particular, is how to best to use the labeling provided by a committee of labelers. First, we wanted to know, based on the labelers’ learning curves, whether using AL methods (versus standard passive learning methods) has an effect on the Intra-labeler variability (within the learning curve of each labeler) and inter-labeler variability (among the learning curves of different labelers). Then, we wanted to examine the effect of learning (either passively or actively) from the labels created by the majority consensus of a group of labelers. Methods We used our CAESAR-ALE framework for classifying the severity of clinical conditions, the three AL methods and the passive learning method, as mentioned above, to induce the classifications models. We used a dataset of 516 clinical conditions and their severity labeling, represented by features aggregated from the medical records of 1.9 million patients treated at Columbia University Medical Center. We analyzed the variance of the classification performance within (intra-labeler), and especially among (inter-labeler) the classification models that were induced by using the labels provided by seven labelers. We also compared the performance of the passive and active learning models when using the consensus label. Results The AL methods produced, for the models induced from each labeler, smoother Intra-labeler learning curves during the training phase, compared to the models produced when using the passive learning method. The mean standard deviation of the learning curves of the three AL methods over all labelers (mean: 0.0379; range: [0.0182 to 0.0496]), was significantly lower (p = 0.049) than the Intra-labeler standard deviation when using the passive learning method (mean: 0.0484; range: [0.0275 to 0.0724). Using the AL methods resulted in a lower mean Inter-labeler AUC standard deviation among the AUC values of the labelers’ different models during the training phase, compared to the variance of the induced models’ AUC values when using passive learning. The Inter-labeler AUC standard deviation, using the passive learning method (0.039), was almost twice as high as the Inter-labeler standard deviation us...

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Section: Introductionmentioning

confidence: 99%

Improving condition severity classification with an efficient active learning based framework

Nissim

Boland

Tatonetti

et al. 2016

Journal of Biomedical Informatics

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Classification of condition severity can be useful for discriminating among sets of conditions or phenotypes, for example when prioritizing patient care or for other healthcare purposes. Electronic Health Records (EHRs) represent a rich source of labeled information that can be harnessed for severity classification. The labeling of EHRs is expensive and in many cases requires employing professionals with high level of expertise. In this study, we demonstrate the use of Active Learning (AL) techniques to decrease expert labeling efforts. We employ three AL methods and demonstrate their ability to reduce labeling efforts while effectively discriminating condition severity. We incorporate three AL methods into a new framework based on the original CAESAR (Classification Approach for Extracting Severity Automatically from Electronic Health Records) framework to create the Active Learning Enhancement framework (CAESAR-ALE). We applied CAESAR-ALE to a dataset containing 516 conditions of varying severity levels that were manually labeled by seven experts. Our dataset, called the “CAESAR dataset,” was created from the medical records of 1.9 million patients treated at Columbia University Medical Center (CUMC). All three AL methods decreased labelers’ efforts compared to the learning methods applied by the original CAESER framework in which the classifier was trained on the entire set of conditions; depending on the AL strategy used in the current study, the reduction ranged from 48% to 64% that can result in significant savings, both in time and money. As for the PPV (precision) measure, CAESAR-ALE achieved more than 13% absolute improvement in the predictive capabilities of the framework when classifying conditions as severe. These results demonstrate the potential of AL methods to decrease the labeling efforts of medical experts, while increasing accuracy given the same (or even a smaller) number of acquired conditions. We also demonstrated that the methods included in the CAESAR-ALE framework (Exploitation and Combination_XA) are more robust to the use of human labelers with different levels of professional expertise.

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ALDROID: efficient update of Android anti-virus software using designated active learning methods

Cited by 34 publications

References 48 publications

A Survey on Android Malware Detection Techniques

A Survey on Android Malware Detection Techniques

Inter-labeler and intra-labeler variability of condition severity classification models using active and passive learning methods

Improving condition severity classification with an efficient active learning based framework

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