Detecting Malicious Executable Files Based on Static-Dynamic Analysis Using Machine Learning

被引：1

作者：

Ognev, R. A. ^{[1
]}

Zhukovskii, E. V. ^{[1
]}

Zegzhda, D. P. ^{[1
]}

Kiselev, A. N. ^{[2
]}

机构：

[1] Peter Great St Petersburg Polytech Univ, St Petersburg 195251, Russia

[2] Mozhaisky Mil Space Acad, St Petersburg 197198, Russia

来源：

AUTOMATIC CONTROL AND COMPUTER SCIENCES | 2022年 / 56卷 / 08期

关键词：

information security systems; malware detection; static-dynamic analysis; feature selection of program behavior parameters;

D O I：

10.3103/S0146411622080120

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In current operating systems, executable files are used to solve various problems, which in turn can be either benign (perform only necessary actions) or malicious (the main purpose of which is to perform destructive actions in relation to the system). Thus, malware is a program used for unauthorized access to information and/or impact on information or resources of an automated information system. Here, the problem of determining the types of executable files and detecting malware is solved.

引用

页码：852 / 864

页数：13

共 50 条

[31] An Approach to Static-Dynamic Software Analysis
Gonzalez-de-Aledo, Pablo
Sanchez, Pablo
Huuck, Ralf
[J]. FORMAL TECHNIQUES FOR SAFETY-CRITICAL SYSTEMS, (FTSCS 2015), 2016, 596 : 225 - 240
[32] Integrating Static and Dynamic Malware Analysis Using Machine Learning
Mangialardo, R. J.
Duarte, J. C.
[J]. IEEE LATIN AMERICA TRANSACTIONS, 2015, 13 (09) : 3080 - 3087
[33] Machine Learning on Merging Static and Dynamic Features to Identify Malicious Mobile Apps
Su, Ming-Yang
Chang, Jer-Yuan
Fung, Kek-Tung
[J]. 2017 NINTH INTERNATIONAL CONFERENCE ON UBIQUITOUS AND FUTURE NETWORKS (ICUFN 2017), 2017, : 863 - 867
[34] Detection of Malicious Binaries by Applying Machine Learning Models on Static and Dynamic Artefacts
Chukka, Anantha Rao
Devi, V. Susheela
[J]. PROCEEDINGS OF THE 6TH INTERNATIONAL CONFERENCE ON INTERNET OF THINGS, BIG DATA AND SECURITY (IOTBDS), 2021, : 29 - 37
[35] Detecting Malicious Botnets in IoT Networks Using Machine Learning Techniques
Asghar, Muhammad Nabeel
Asif, Muhammad
Murad, Zara
Alyahya, Ahmed
[J]. IPSI BGD TRANSACTIONS ON INTERNET RESEARCH, 2024, 20 (02):
[36] Detecting Malicious Botnets in IoT Networks Using Machine Learning Techniques
Asghar, Muhammad Nabeel
Raza, Muhammad Asif
Murad, Zara
Alyahya, Ahmed
[J]. IPSI BGD TRANSACTIONS ON INTERNET RESEARCH, 2024, 20 (01): : 24 - 35
[37] Machine Learning-Based System for Detecting Unseen Malicious Software
Bisio, Federica
Gastaldo, Paolo
Meda, Claudia
Nasta, Stefano
Zunino, Rodolfo
[J]. APPLICATIONS IN ELECTRONICS PERVADING INDUSTRY, ENVIRONMENT AND SOCIETY, APPLEPIES 2014, 2016, 351 : 9 - 15
[38] A Machine Learning Approach to Threat Hunting in Malicious PDF Files
Teymourlouei, Haydar
Harris, Vareva E.
[J]. 2023 INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND COMPUTATIONAL INTELLIGENCE, CSCI 2023, 2023, : 782 - 787
[39] Static detection of application backdoorsDetecting both malicious software behavior and malicious indicators from the static analysis of executable code
Chris Wysopal
Chris Eng
Tyler Shields
[J]. Datenschutz und Datensicherheit - DuD, 2010, 34 (3) : 149 - 155
[40] 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

← 1 2 3 4 5 →