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Automatic malware classification and new malwaredetection using machine learning Article

Liu LIU, Bao-sheng WANG, Bo YU, Qiu-xi ZHONG

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 9, Pages 1336-1347 doi: 10.1631/FITEE.1601325

Abstract: familiesand to detect new kinds of malware programs.The decision-making module uses the featuresto classify the malware and to identify suspicious malwareFinally,the detection module uses the shared nearest neighbor (SNN) clusteringalgorithm to discover newmalware families.98.9%, and successfullydetects 86.7% of the new malware.

Keywords： Malware classification Machine learning n-gram Gray-scale image Feature extraction Malware detection

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A survey of malware behavior description and analysis Review

Bo YU, Ying FANG, Qiang YANG, Yong TANG, Liu LIU

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 5, Pages 583-603 doi: 10.1631/FITEE.1601745

Abstract: Behavior-based malware analysis is an important technique for automatically analyzing and detecting malwareBy considering how malware behaves, we can tackle the malware obfuscation problem, which cannot be processedwe can also derive the as-built behavior specifications and cover the entire behavior space of the malwareAlthough there have been several works focusing on malware behavior analysis, such research is far from: malware behavior description, behavior analysis methods, and visualization techniques.

Keywords： Malware behavior Static analysis Dynamic Analysis Behavior data expression Behavior analysis Machinelearning Semantics-based analysis Behavior visualization Malware evolution

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Discovering optimal features using static analysis and a genetic search based method for Android malwaredetection None

Ahmad FIRDAUS, Nor Badrul ANUAR, Ahmad KARIM, Mohd Faizal Ab RAZAK

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 6, Pages 712-736 doi: 10.1631/FITEE.1601491

Abstract: With this in mind, security practitioners have conducted static and dynamic analyses to identify malwareHowever, static analysis requires a minimum number of features to efficiently classify malware.

Keywords： Genetic algorithm Static analysis Android Malware Machine learning

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Malware homology identification based on a gene perspective Research Papers

Bing-lin ZHAO, Zheng SHAN, Fu-dong LIU, Bo ZHAO, Yi-hang CHEN, Wen-jie SUN

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 6, Pages 801-815 doi: 10.1631/FITEE.1800523

Abstract:

Malware homology identification is important in attacking event tracing, emergency response schemeCurrent malware homology identification methods still rely on manual analysis, which is inefficient andA malware gene is represented by the subgraph, which can describe the homology of malware families.We extract the key subgraph from the function dependency graph as the malware gene by selecting the keyFinally, we use the family genes to guide the identification of malware based on homology.

Keywords： Malware classification Gene perspective Dependency graph Homology analysis

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Usability perceptions and beliefs about smart thermostats by chi-square test, signal detection theory, and fuzzy detection theory in regions of Mexico

Pedro PONCE, Therese PEFFER, Arturo MOLINA

Frontiers in Energy 2019, Volume 13, Issue 3, Pages 522-538 doi: 10.1007/s11708-018-0562-2

Abstract: This paper proposes the use of a signal detection theory (SDT), fuzzy detection theory (FDT), and chi-square

Keywords： thermostats perceptions beliefs signal detection theory (SDT) fuzzy signal detection theory (FSDT) chi-square

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Advances in airborne microorganisms detection using biosensors: A critical review

Frontiers of Environmental Science & Engineering 2021, Volume 15, Issue 3, doi: 10.1007/s11783-021-1420-8

Abstract: In recent years, the detection technology for airborne microorganisms has developed rapidly; it can be

Keywords： Biosensor Airborne microorganisms Microbiological detection technology

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Recent advances in SERS detection of perchlorate

Jumin Hao, Xiaoguang Meng

Frontiers of Chemical Science and Engineering 2017, Volume 11, Issue 3, Pages 448-464 doi: 10.1007/s11705-017-1611-9

Abstract: Development of novel detection methods for perchlorate with the potential for field use has been an urgentperchlorate in water and other media with an emphasis on the development of SERS substrates for perchlorate detection

Keywords： perchlorate SERS detection substrate modification nanostructure

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A fast antibiotic detection method for simplified pretreatment through spectra-based machine learning

Frontiers of Environmental Science & Engineering 2022, Volume 16, Issue 3, doi: 10.1007/s11783-021-1472-9

Abstract:

• A spectral machine learning approach is proposed for predicting mixed antibiotic.

Keywords： Antibiotic contamination Spectral detection Machine learning

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Development of Pulsed Radiation Detection Technology

Ouyang Xiaoping

Strategic Study of CAE 2008, Volume 10, Issue 4, Pages 44-55

Abstract: Pulse radiation detection has found its various applications in scientific researches, including nuclearThese diagnostic techniques and their relative detection systems, the design principle, the parameters

Keywords： fission reaction fusion reaction pulse radiation detection detecting system neutron detection gamma-raydetection

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Edge detection of steel plates at high temperature using image measurement

Qiong Zhou, Qi An

Frontiers of Mechanical Engineering 2009, Volume 4, Issue 1, Pages 77-82 doi: 10.1007/s11465-009-0013-1

Abstract: An edge detection method for the measurement of steel plate’s thermal expansion is proposed in this paperFirst, images are picked up by an imaging system; a method of regional edge detection based on grayscales

Keywords： thermal expansion image measurement edge detection image calibration

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Machine vision-based automatic fruit quality detection and grading

Frontiers of Agricultural Science and Engineering doi: 10.15302/J-FASE-2023532

Abstract:

● A machine vision-based prototype system was developed for fruit grading.

Keywords： Computer and machine vision convolution neural network deep learning defective fruit detection fruit

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Experimental and numerical study on microcrack detection using contact nonlinear acoustics

Xiaojia CHEN, Yuanlin WANG

Frontiers of Structural and Civil Engineering 2009, Volume 3, Issue 2, Pages 137-141 doi: 10.1007/s11709-009-0028-z

Abstract: This paper introduces a non-classical nonlinear acoustic theory for microcrack detection in materialsconcentrates on the experimental and numerical verification of the effectivity of contact nonlinear acoustic detection

Keywords： microcrack detection contact nonlinearity numerical analysis

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Digital image correlation-based structural state detection through deep learning

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 1, Pages 45-56 doi: 10.1007/s11709-021-0777-x

Abstract: CNN can extract the structural state information from the vibration signals and classify them; 2) the detection

Keywords： structural state detection deep learning digital image correlation vibration signal steel frame

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Damage detection in beam-like structures using static shear energy redistribution

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 12, Pages 1552-1564 doi: 10.1007/s11709-022-0903-4

Abstract: Another advantage of the proposed approach is that damage detection can be performed without establishing

Keywords： damage detection beam structure strain energy static displacement variation energy damage index

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Fast detection algorithm for cracks on tunnel linings based on deep semantic segmentation

Frontiers of Structural and Civil Engineering 2023, Volume 17, Issue 5, Pages 732-744 doi: 10.1007/s11709-023-0965-y

Abstract: The proposed method addresses the low accuracy of tunnel crack segmentation and the slow detection speed

Keywords： tunnel engineering crack segmentation fast detection DeepLabv3+ feature fusion attention mechanism

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Title Author Date Type Operation

Automatic malware classification and new malwaredetection using machine learning

Liu LIU, Bao-sheng WANG, Bo YU, Qiu-xi ZHONG

Journal Article

A survey of malware behavior description and analysis

Bo YU, Ying FANG, Qiang YANG, Yong TANG, Liu LIU

Journal Article

Discovering optimal features using static analysis and a genetic search based method for Android malwaredetection

Ahmad FIRDAUS, Nor Badrul ANUAR, Ahmad KARIM, Mohd Faizal Ab RAZAK

Journal Article

Malware homology identification based on a gene perspective

Bing-lin ZHAO, Zheng SHAN, Fu-dong LIU, Bo ZHAO, Yi-hang CHEN, Wen-jie SUN

Journal Article

Usability perceptions and beliefs about smart thermostats by chi-square test, signal detection theory, and fuzzy detection theory in regions of Mexico

Pedro PONCE, Therese PEFFER, Arturo MOLINA

Journal Article

Advances in airborne microorganisms detection using biosensors: A critical review

Journal Article

Recent advances in SERS detection of perchlorate

Jumin Hao, Xiaoguang Meng

Journal Article

A fast antibiotic detection method for simplified pretreatment through spectra-based machine learning

Journal Article

Development of Pulsed Radiation Detection Technology

Ouyang Xiaoping

Journal Article