Dynamic analysis of malicious behavior propagation based on feature selection in software network

被引:0
|
作者
Xue, Huajian [1 ,2 ]
Wang, Yali [3 ]
Tang, Qiguang [4 ]
机构
[1] Tongling Univ, Coll Math & Comp Sci, Tongling, Peoples R China
[2] Tongling Univ, Anhui Engn Res Ctr Intelligent Mfg Copper based Ma, Tongling, Peoples R China
[3] Suzhou City Univ, Coll Comp Sci & Artificial Intelligence, Suzhou, Peoples R China
[4] Zhongyuan Oilfield Co SINOPEC, Zhongyuan Oilfield Oil & Gas Engn Serv Ctr, Puyang, Peoples R China
来源
FRONTIERS IN PHYSICS | 2024年 / 12卷
关键词
recurrent neural networks; information propagation; feature selection; dynamic analysis; software network;
D O I
10.3389/fphy.2024.1493209
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
In the era of big data, the propagation of malicious software poses a significant threat to corporate data security. To safeguard data assets from the encroachment of malware, it is essential to conduct a dynamic analysis of various information propagation behaviors within software. This paper introduces a dynamic analysis detection method for malicious behavior based on feature extraction (MBDFE), designed to effectively identify and thwart the spread of malicious software. The method is divided into three stages: First, variable-length N-gram algorithms are utilized to extract subsequences of varying lengths from the sample APl call sequences as continuous dynamic features. Second, feature selection techniques based on information gain are employed to identify suitable classification features. Lastly, recurrent neural networks (RNN) are applied for the classification training and prediction of diverse software behaviors. Experimental results and analysis demonstrate that this approach can accurately detect and promptly interrupt the information dissemination of malicious software when such behavior occurs, thereby enhancing the precision and timeliness of malware detection.
引用
收藏
页数:11
相关论文
共 50 条
  • [41] Software defect prediction based on enhanced metaheuristic feature selection optimization and a hybrid deep neural network
    Zhu, Kun
    Ying, Shi
    Zhang, Nana
    Zhu, Dandan
    JOURNAL OF SYSTEMS AND SOFTWARE, 2021, 180
  • [42] A combined feature selection approach for malicious email detection based on a comprehensive email dataset
    Zhang, Han
    Shi, Yong
    Liu, Ming
    Chen, Libo
    Wu, Songyang
    Xue, Zhi
    CYBERSECURITY, 2025, 8 (01):
  • [43] IoT malicious traffic identification using wrapper-based feature selection mechanisms
    Shafiq, Muhammad
    Tian, Zhihong
    Bashir, Ali Kashif
    Du, Xiaojiang
    Guizani, Mohsen
    COMPUTERS & SECURITY, 2020, 94
  • [44] Analysis and improvements on feature selection methods based on artificial neural network weights
    da Costa, Nattane Luiza
    de Lima, Marcio Dias
    Barbosa, Rommel
    APPLIED SOFT COMPUTING, 2022, 127
  • [45] Feature representation and selection in malicious code detection methods based on static system calls
    Ding Yuxin
    Yuan Xuebing
    Zhou Di
    Dong Li
    An Zhanchao
    COMPUTERS & SECURITY, 2011, 30 (6-7) : 514 - 524
  • [46] Malicious Behavior Analysis of Android GUI Based on ADB
    Yang, Li
    Wang, Lijun
    Zhang, Dongdong
    2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND ENGINEERING (CSE) AND IEEE/IFIP INTERNATIONAL CONFERENCE ON EMBEDDED AND UBIQUITOUS COMPUTING (EUC), VOL 2, 2017, : 147 - 153
  • [47] Malicious behavior analysis method based on program semantic
    Institute of Computer Science and Technology, Peking University, Beijing 100871, China
    Beijing Daxue Xuebao Ziran Kexue Ban, 2008, 4 (537-542):
  • [48] A novel hybrid feature selection method based on dynamic feature importance
    Wei, Guangfen
    Zhao, Jie
    Feng, Yanli
    He, Aixiang
    Yu, Jun
    APPLIED SOFT COMPUTING, 2020, 93
  • [49] Feature selection and interpretability analysis of compound faults in rolling bearings based on the causal feature weighted network
    Yu, Chongchong
    Li, Mengxiong
    Wu, Zongning
    Gao, Kuo
    Wang, Fei
    MEASUREMENT SCIENCE AND TECHNOLOGY, 2024, 35 (08)
  • [50] Feature Selection of Sudden Failure Based on Affinity Propagation Clustering
    Li, Limin
    Wang, Zhongsheng
    ADVANCED MATERIALS AND INFORMATION TECHNOLOGY PROCESSING II, 2012, 586 : 241 - 246