Machine Learning for Analyzing Malware

被引:1
|
作者
Dong, Yajie [1 ]
Liu, Zhenyan [1 ]
Yan, Yida [1 ]
Wang, Yong [1 ]
Peng, Tu [1 ]
Zhang, Ji [1 ]
机构
[1] Beijing Inst Technol, Sch Software, Beijing, Peoples R China
来源
NETWORK AND SYSTEM SECURITY | 2017年 / 10394卷
基金
国家重点研发计划;
关键词
Machine learning; Classification; Analyzing malware; Clustering; Association analysis; EXECUTABLES;
D O I
10.1007/978-3-319-64701-2_28
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The Internet has become an indispensable part of people's work and life. It provides favorable communication conditions for malwares. Therefore, malwares are endless and spread faster and become one of the main threats of current network security. Based on the malware analysis process, from the original feature extraction and feature selection to malware detection, this paper introduces the machine learning algorithm such as clustering, classification and association analysis, and how to use the machine learning algorithm to malware and its variants for effective analysis.
引用
收藏
页码:386 / 398
页数:13
相关论文
共 50 条
  • [1] Analyzing Machine Learning Approaches for Online Malware Detection in Cloud
    Kimmell, Jeffrey C.
    Abdelsalam, Mahmoud
    Gupta, Maanak
    [J]. 2021 IEEE INTERNATIONAL CONFERENCE ON SMART COMPUTING (SMARTCOMP 2021), 2021, : 189 - 196
  • [2] Analyzing Various Machine Learning Approaches for Detecting Android Malware
    Dickey, Kyler
    Hwang, Doosung
    Kim, Donghoon
    [J]. SOUTHEASTCON 2024, 2024, : 1288 - 1293
  • [3] Analyzing and comparing the effectiveness of malware detection: A study of machine learning approaches
    Azeem, Muhammad
    Khan, Danish
    Iftikhar, Saman
    Bawazeer, Shaikhan
    Alzahrani, Mohammed
    [J]. HELIYON, 2024, 10 (01)
  • [4] Machine-Learning-Based Malware Detection for Virtual Machine by Analyzing Opcode Sequence
    Wang, Xiao
    Zhang, Jianbiao
    Zhang, Ai
    [J]. ADVANCES IN BRAIN INSPIRED COGNITIVE SYSTEMS, BICS 2018, 2018, 10989 : 717 - 726
  • [5] Analyzing the Efficiency of Machine Learning Classifiers in Hardware-based Malware Detectors
    Kuruvila, Abraham Peedikayil
    Kundu, Shamik
    Basu, Kanad
    [J]. 2020 IEEE COMPUTER SOCIETY ANNUAL SYMPOSIUM ON VLSI (ISVLSI 2020), 2020, : 452 - 457
  • [6] Application of Machine Learning in Malware Detection
    Van Quynh, Trinh
    Hien, Vu Thanh
    Nguyen, Vu Thanh
    Bao, Huynh Quoc
    [J]. FUTURE DATA AND SECURITY ENGINEERING. BIG DATA, SECURITY AND PRIVACY, SMART CITY AND INDUSTRY 4.0 APPLICATIONS, FDSE 2022, 2022, 1688 : 362 - 374
  • [7] Malware Classification Using Machine Learning
    Savard, Nolan
    Feinauer, David M.
    Alghazo, Jaafar M.
    Abdelhamid, Sherif E.
    [J]. SOUTHEASTCON 2024, 2024, : 843 - 847
  • [8] Machine Learning in Malware Traffic Classifications
    Yu, Ken F.
    Harang, Richard E.
    [J]. MILCOM 2017 - 2017 IEEE MILITARY COMMUNICATIONS CONFERENCE (MILCOM), 2017, : 6 - 10
  • [9] IoT Malware Detection with Machine Learning
    Buttyan, Levente
    Ferenc, Rudolf
    [J]. ERCIM NEWS, 2022, (129): : 17 - 19
  • [10] Machine Learning to Identify Android Malware
    Tam, Geran
    Hunter, Aaron
    [J]. 2018 9TH IEEE ANNUAL UBIQUITOUS COMPUTING, ELECTRONICS & MOBILE COMMUNICATION CONFERENCE (UEMCON), 2018,