Malware recognition approach based on self‐similarity and an improved clustering algorithm by Jinfu Chen, Chi Zhang, Saihua Cai, Zufa Zhang, Lu Liu, Longxia Huang

“…Abstract The recognition of malware in network traffic is an important research problem. …”
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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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MalHAPGNN: An Enhanced Call Graph-Based Malware Detection Framework Using Hierarchical Attention Pooling Graph Neural Network by Wenjie Guo, Wenbiao Du, Xiuqi Yang, Jingfeng Xue, Yong Wang, Weijie Han, Jingjing Hu

Subjects: “…malware detection…”
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Apk2Audio4AndMal: Audio Based Malware Family Detection Framework by Oguz Emre Kural, Erdal Kilic, Ceyda Aksac

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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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Stochastic Stabilization of Malware Propagation in Wireless Sensor Network via Aperiodically Intermittent White Noise by Xiaojing Zhong, Baihao Peng, Feiqi Deng, Guiyun Liu

“…Our theoretical results can be applied to understand the observed mechanisms of malware and design interventions to control the spread of malware. …”
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MalAware: A tabletop exercise for malware security awareness education and incident response training by Giddeon Angafor, Iryna Yevseyeva, Leandros Maglaras

Subjects: “…Malware…”
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Android malware detection method based on combined algorithm by Hao CHEN, Sihan QING

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

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FABLDroid: Malware detection based on hybrid analysis with factor analysis and broad learning methods for android applications by Kazım Kılıç, İsmail Atacak, İbrahim Alper Doğru

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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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Advanced Malware Detection: Integrating Convolutional Neural Networks with LSTM RNNs for Enhanced Security by Balsam Ridha Habeeb Alsaedi

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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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A novel lightweight Machine Learning framework for IoT malware classification based on matrix block mean Downsampling by Mahmoud E. Farfoura, Ibrahim Mashal, Ahmad Alkhatib, Radwan M. Batyha, Didi Rosiyadi

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Obfuscated Malware Detection and Classification in Network Traffic Leveraging Hybrid Large Language Models and Synthetic Data by Mehwish Naseer, Farhan Ullah, Samia Ijaz, Hamad Naeem, Amjad Alsirhani, Ghadah Naif Alwakid, Abdullah Alomari

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Malware Detection in Self-Driving Vehicles Using Machine Learning Algorithms by Seunghyun Park, Jin-Young Choi

“…In this study, we propose a machine learning-based data analysis method to accurately detect abnormal behaviors due to malware in large-scale network traffic in real time. …”
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