Malware detection using bilayer behavior abstraction and improved one-class support vector machines

Miao, Qiguang; Liu, Jiachen; Cao, Ying; Song, Jianfeng

doi:10.1007/s10207-015-0297-6

Cited by 38 publications

(21 citation statements)

References 31 publications

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“…In this figure, there are five research that use higher than 5000 real samples during the evaluation process. The BBA approach [44] has the maximum dataset with 17,000 samples and the AMD approach [38] has the minimum dataset with 500 samples.…”

Section: Discussionmentioning

confidence: 99%

“…The exploratory outcomes illustrate that HDM-Analyzer accomplishes better general exactness and time manysided quality than static and element investigation strategies. Miao et al [44] presented a bilayer conduct reflection strategy in light of the semantic examination of dynamic API sequences. Operations on touchy framework assets and complex practices are disconnected in an interpretable way at various semantic layers.…”

Section: Review Of the Selected Behavior-based Approachesmentioning

confidence: 99%

See 1 more Smart Citation

A state-of-the-art survey of malware detection approaches using data mining techniques

Souri

Hosseini

2018

Hum. Cent. Comput. Inf. Sci.

315

166

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Data mining techniques have been concentrated for malware detection in the recent decade. The battle between security analyzers and malware scholars is everlasting as innovation grows. The proposed methodologies are not adequate while evolutionary and complex nature of malware is changing quickly and therefore turn out to be harder to recognize. This paper presents a systematic and detailed survey of the malware detection mechanisms using data mining techniques. In addition, it classifies the malware detection approaches in two main categories including signature-based methods and behavior-based detection. The main contributions of this paper are: (1) providing a summary of the current challenges related to the malware detection approaches in data mining, (2) presenting a systematic and categorized overview of the current approaches to machine learning mechanisms, (3) exploring the structure of the significant methods in the malware detection approach and (4) discussing the important factors of classification malware approaches in the data mining. The detection approaches have been compared with each other according to their importance factors. The advantages and disadvantages of them were discussed in terms of data mining models, their evaluation method and their proficiency. This survey helps researchers to have a general comprehension of the malware detection field and for specialists to do consequent examinations.

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Section: Discussionmentioning

confidence: 99%

Section: Review Of the Selected Behavior-based Approachesmentioning

confidence: 99%

A state-of-the-art survey of malware detection approaches using data mining techniques

Souri

Hosseini

2018

Hum. Cent. Comput. Inf. Sci.

315

166

View full text Add to dashboard Cite

show abstract

“…Machine learning and statistical analysis techniques, have the potential to discover unknown and unforeseen attacks, and have been widely used for cyber security research and development [19,27,1,31,17,36,20]. For example, Markel et al [19] developed a machine learning-based detection scheme to learn important patterns from metadata primarily contained in the header of executable files.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Wenke [17] investigated a rule-based approach, utilizing machine learning to conduct intrusion detection. Miao et al [20] developed a bilayer behavior abstraction scheme, based on the semantic analysis of dynamic API (Application Program Interface) sequences, to identify malware that consists of discriminant and stable features.…”

Section: Background and Related Workmentioning

confidence: 99%

ScanMe mobile

Zhang

Cole

et al. 2016

SIGAPP Appl. Comput. Rev.

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As mobile malware have increased in number and sophistication, it has become pertinent for users to have tools that can inform them of potentially malicious applications. To fulfill this need, we develop a cloud-based malware analysis service called ScanMe Mobile, for the Android platform. The objective of this service is to provide users with detailed information about Android Application Package (APK) files before installing them on their devices. With ScanMe Mobile, users are able to upload APK files from their device SD card, scan the APK in the malware detection system that could be deployed in the cloud, compile a comprehensive report, and store or share the report by publishing it to the website. ScanMe Mobile works by running the APK in a virtual sandbox to generate permission data, and analyzes the result in the machine learning detection system. Through our experimental results, we demonstrate that the proposed system can effectively detect malware on the Android platform.

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“…In [39], with the help of global features using the Gabor wavelet transform and Gist, the feed-forward ANN was developed to identify the behavior of malicious data with a good accuracy. In [40], after abstracting the complex behaviors based on the semantic analysis of dynamic API sequences, an SVM was proposed to achieve malware detection with good generalization ability. Furthermore, with the popular use of the deep learning method, some DNN models were also applied to tackle the issue of malware detection.…”

Section: Introductionmentioning

confidence: 99%

Kernel Mixture Correntropy Conjugate Gradient Algorithm for Time Series Prediction

Xue

Luo

Gao

et al. 2019

Entropy

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Kernel adaptive filtering (KAF) is an effective nonlinear learning algorithm, which has been widely used in time series prediction. The traditional KAF is based on the stochastic gradient descent (SGD) method, which has slow convergence speed and low filtering accuracy. Hence, a kernel conjugate gradient (KCG) algorithm has been proposed with low computational complexity, while achieving comparable performance to some KAF algorithms, e.g., the kernel recursive least squares (KRLS). However, the robust learning performance is unsatisfactory, when using KCG. Meanwhile, correntropy as a local similarity measure defined in kernel space, can address large outliers in robust signal processing. On the basis of correntropy, the mixture correntropy is developed, which uses the mixture of two Gaussian functions as a kernel function to further improve the learning performance. Accordingly, this article proposes a novel KCG algorithm, named the kernel mixture correntropy conjugate gradient (KMCCG), with the help of the mixture correntropy criterion (MCC). The proposed algorithm has less computational complexity and can achieve better performance in non-Gaussian noise environments. To further control the growing radial basis function (RBF) network in this algorithm, we also use a simple sparsification criterion based on the angle between elements in the reproducing kernel Hilbert space (RKHS). The prediction simulation results on a synthetic chaotic time series and a real benchmark dataset show that the proposed algorithm can achieve better computational performance. In addition, the proposed algorithm is also successfully applied to the practical tasks of malware prediction in the field of malware analysis. The results demonstrate that our proposed algorithm not only has a short training time, but also can achieve high prediction accuracy.

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Malware detection using bilayer behavior abstraction and improved one-class support vector machines

Cited by 38 publications

References 31 publications

A state-of-the-art survey of malware detection approaches using data mining techniques

A state-of-the-art survey of malware detection approaches using data mining techniques

ScanMe mobile

Kernel Mixture Correntropy Conjugate Gradient Algorithm for Time Series Prediction

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