Homology analysis of malware based on graph by Bing-lin ZHAO, Xi MENG, Jin HAN, Jing WANG, Fu-dong LIU

Subjects: “…malware…”
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Study on modern malware analysis system by Yi ZHAO, Jian GONG, Wang YANG

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Influence of Removable Devices' Heterouse on the Propagation of Malware by Xie Han, Yi-Hong Li, Li-Ping Feng, Li-Peng Song

“…The effects of removable devices’ heterouse in different areas on the propagation of malware spreading via removable devices remain unclear. …”
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Malware Analysis Using Visualized Image Matrices by KyoungSoo Han, BooJoong Kang, Eul Gyu Im

“…Our experimental results show that the image matrices of malware can effectively be used to classify malware families both statically and dynamically with accuracy of 0.9896 and 0.9732, respectively.…”
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Power Consumption Based Android Malware Detection by Hongyu Yang, Ruiwen Tang

“…In order to solve the problem that Android platform’s sand-box mechanism prevents security protection software from accessing effective information to detect malware, this paper proposes a malicious software detection method based on power consumption. …”
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Review of malware detection and classification visualization techniques by Jinwei WANG, Zhengjia CHEN, Xue XIE, Xiangyang LUO, Bin MA

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A Survey on Adversarial Attacks for Malware Analysis by Kshitiz Aryal, Maanak Gupta, Mahmoud Abdelsalam, Pradip Kunwar, Bhavani Thuraisingham

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Malware detection approach based on improved SOINN by Bin ZHANG, Lixun LI, Shuqin DONG

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Malware prediction technique based on program gene by Da XIAO, Bohan LIU, Baojiang CUI, Xiaochen WANG, Suoxing ZHANG

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Android malware detection method based on combined algorithm by Hao CHEN, Sihan QING

Subjects: “…malware detection…”
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Android malware detection based on improved random forest by Hong-yu YANG, Jin XU

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Android malware detection method based on SimHash by Bo CHEN, Yong-tao PAN, Tie-ming CHEN

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Research on Android malware detection based on permission and behavior by Xiao-min ZHANG, Jing LUI, Jun-xi ZHUANG, Ying-xu LAI

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Modeling and Analysis of the Spread of Malware with the Influence of User Awareness by Qingyi Zhu, Xuhang Luo, Yuhang Liu

“…By incorporating the security awareness of computer users into the susceptible-infected-susceptible (SIS) model, this study proposes a new malware propagation model, named the SID model, where D compartment denotes the group of nodes with user awareness. …”
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Behavior Intention Derivation of Android Malware Using Ontology Inference by Jian Jiao, Qiyuan Liu, Xin Chen, Hongsheng Cao

“…Previous researches on Android malware mainly focus on malware detection, and malware’s evolution makes the process face certain hysteresis. …”
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HTTP behavior characteristics generation and extraction approach for Android malware by Yaling LUO, Wenwei LI, Xin SU

“…Growing of Android malware，not only seriously endangered the security of the Android market，but also brings challenges for detection.A generation and extraction approach of automatic Android malware behavioral signatures was proposed based on HTTP traffic.Firstly，the behavioral signatures were extracted from the traffic traces generated by Android malware.Then，network behavioral characteristics were extracted from the generated network traffic.Finally，these behavioral signatures were used to detect Android malware.The experimental results show that the approach is able to extract Android malware network traffic behavioral signature with accuracy and efficiency.…”
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Research on the reverse analyses and monitoring data of Mirai malware botnet by Ya-liang CHEN, Qin-yun DAI, Hai-yan WU, Zheng WEI

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Android malware detection based on APK signature information feedback by Xin-yu LIU, Jian WENG, Yue ZHANG, Bing-wen FENG, Jia-si WENG

“…A new malware detection method based on APK signature of information feedback (SigFeedback) was proposed.Based on SVM classification algorithm,the method of eigenvalue extraction adoped heuristic rule learning to sig APK information verify screening,and it also implemented the heuristic feedback,from which achieved the purpose of more accurate detection of malicious software.SigFeedback detection algorithm enjoyed the advantage of the high detection rate and low false positive rate.Finally the experiment show that the SigFeedback algorithm has high efficiency,making the rate of false positive from 13% down to 3%.…”
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Online analytical model of massive malware based on feature clusting by Xiao-lin XU, Xiao-chun YUN, Yong-lin ZHOU, Xue-bin KANG

Subjects: “…malware…”
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Android malware detection method based on deep neural network by Fan CHAO, Zhi YANG, Xuehui DU, Yan SUN

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