Techniques of Malware Detection: Research Review

被引:6
作者
Baghirov, Elshan [1 ]
机构
[1] ANAS, Inst Informat Technol, Baku, Azerbaijan
来源
2021 IEEE 15TH INTERNATIONAL CONFERENCE ON APPLICATION OF INFORMATION AND COMMUNICATION TECHNOLOGIES (AICT2021) | 2021年
关键词
malware; static analysis; dynamic analysis; classification; machine learning; MACHINE LEARNING TECHNIQUES; CLASSIFICATION;
D O I
10.1109/AICT52784.2021.9620415
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Analysis, and detection of malicious software play a crucial role in computer security. Signature-based malware detection methods were a classical solution in this area. However, malware creators are able to bypass these detection methods using some obfuscation methods like metamorphism, polymorphism. To address this issue, methods based on machine learning have been applied. However, some challenges are still present. This work presents a planned and detailed review of the malware detection mechanisms used by researchers. For this purpose, scientific works on malware detection topics were classified according to applied methods of malware detection, the accuracy of detection, etc. Several scientific works have been reviewed for analysis, and the current situation in the fight against malware has been analyzed. The main contributions of this paper are to provide detailed information to researchers about challenges on malware detection, to present to researchers a general overview of the malware detection field, to provide valuable information about tools and malware datasets that are commonly used by researchers.
引用
收藏
页数:6
相关论文
共 39 条
[1]   Feature Subset Selection for Malware Detection in Smart IoT Platforms [J].
Abawajy, Jemal ;
Darem, Abdulbasit ;
Alhashmi, Asma A. .
SENSORS, 2021, 21 (04) :1-19
[2]  
Aguilar LT, 2015, SYST CONTROL-FOUND A, P1, DOI 10.1007/978-3-319-23303-1_1
[3]  
Anderson H. S., 2018, ARXIV
[4]  
Bagirov E., 2019, INFORMASIYA TAHLUKSI, P200
[5]   Detection of Malicious Code Variants Based on Deep Learning [J].
Cui, Zhihua ;
Xue, Fei ;
Cai, Xingjuan ;
Cao, Yang ;
Wang, Gai-ge ;
Chen, Jinjun .
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2018, 14 (07) :3187-3196
[6]  
Darabian H., 2019, CONCURR COMP-PRACT E, P1
[7]   MalInsight: A systematic profiling based malware detection framework [J].
Han, Weijie ;
Xue, Jingfeng ;
Wang, Yong ;
Liu, Zhenyan ;
Kong, Zixiao .
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS, 2019, 125 :236-250
[8]   An improved two-hidden-layer extreme learning machine for malware hunting [J].
Jahromi, Amir Namavar ;
Hashemi, Sattar ;
Dehghantanha, Ali ;
Choo, Kim-Kwang Raymond ;
Karimipour, Hadis ;
Newton, David Ellis ;
Parizi, Reza M. .
COMPUTERS & SECURITY, 2020, 89
[9]   Malware-Detection Method with a Convolutional Recurrent Neural Network Using Opcode Sequences [J].
Jeon, Seungho ;
Moon, Jongsub .
INFORMATION SCIENCES, 2020, 535 :1-15
[10]   Detecting Malware with an Ensemble Method Based on Deep Neural Network [J].
Yan, Jinpei ;
Qi, Yong ;
Rao, Qifan .
SECURITY AND COMMUNICATION NETWORKS, 2018,