Features of Detecting Malicious Installation Files Using Machine Learning Algorithms

被引：0
|
作者：
P. E. Yugai
E. V. Zhukovskii
P. O. Semenov
机构：
[1] Peter the Great St. Petersburg Polytechnic University,
来源：
Automatic Control and Computer Sciences | 2023年 / 57卷
关键词：
malware; installation files; Trojans; machine learning; naive Bayes classifier; random forest; C4.5 algorithm;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
引用
收藏
页码：968 / 974
页数：6
相关论文
共 50 条
  • [1] Features of Detecting Malicious Installation Files Using Machine Learning Algorithms
    Yugai, P. E.
    Zhukovskii, E. V.
    Semenov, P. O.
    [J]. AUTOMATIC CONTROL AND COMPUTER SCIENCES, 2023, 57 (08) : 968 - 974
  • [2] Detecting Malicious Executable Files Based on Static–Dynamic Analysis Using Machine Learning
    R. A. Ognev
    E. V. Zhukovskii
    D. P. Zegzhda
    A. N. Kiselev
    [J]. Automatic Control and Computer Sciences, 2022, 56 : 852 - 864
  • [3] Detecting Malicious Executable Files Based on Static-Dynamic Analysis Using Machine Learning
    Ognev, R. A.
    Zhukovskii, E. V.
    Zegzhda, D. P.
    Kiselev, A. N.
    [J]. AUTOMATIC CONTROL AND COMPUTER SCIENCES, 2022, 56 (08) : 852 - 864
  • [4] Detecting Malicious URLs Based on Machine Learning Algorithms and Word Embeddings
    Crisan, Andrei
    Florea, Gabriel
    Halasz, Lorand
    Lemnaru, Camelia
    Oprisa, Ciprian
    [J]. 2020 IEEE 16TH INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTER COMMUNICATION AND PROCESSING (ICCP 2020), 2020, : 187 - 193
  • [5] Detecting Malicious URLs using Machine Learning Techniques
    Vanhoenshoven, Frank
    Napoles, Gonzalo
    Falcon, Rafael
    Vanhoof, Keen
    Koppen, Mario
    [J]. PROCEEDINGS OF 2016 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (SSCI), 2016,
  • [6] A Visualization Technique for Installation Evidences Containing Malicious Executable Files Using Machine Language Sequence
    Park, Jun-Hyung
    Kim, Minsoo
    Noh, Bong-Nam
    [J]. INFORMATION SECURITY APPLICATIONS, 2009, 5379 : 201 - +
  • [7] Detecting Malicious PDF Files Using Semi-Supervised Learning Method
    Feng, Di
    Yu, Min
    Wang, Yongjian
    Liu, Chao
    Ma, Chunguang
    [J]. 5TH INTERNATIONAL CONFERENCE ON ADVANCED COMPUTER SCIENCE APPLICATIONS AND TECHNOLOGIES (ACSAT 2017), 2017, : 1 - 9
  • [8] Detecting Malicious Driving with Machine Learning
    Yardy, Kevin
    Almehmadi, Abdulaziz
    El-Khatib, Khalil
    [J]. 2019 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE (WCNC), 2019,
  • [9] Detecting malicious IoT traffic using Machine Learning techniques
    Jayaraman, Bhuvana
    Thai, Mirnalinee T. H. A. N. G. A. N. A. D. A. R. T. H. A. N. G. A.
    Anand, Anirudh
    Nadar, Sri Sivasubramaniya
    [J]. ROMANIAN JOURNAL OF INFORMATION TECHNOLOGY AND AUTOMATIC CONTROL-REVISTA ROMANA DE INFORMATICA SI AUTOMATICA, 2023, 33 (04): : 47 - 58
  • [10] Detecting Malicious Domains using the Splunk Machine Learning Toolkit
    Cersosimo, Michelle
    Lara, Adrian
    [J]. PROCEEDINGS OF THE IEEE/IFIP NETWORK OPERATIONS AND MANAGEMENT SYMPOSIUM 2022, 2022,
12345