A Discrete Event System Based Approach for Obfuscated Malware Detection

被引:0
|
作者
Patanaik, Chinmaya K. [1 ]
Barbhuiya, Ferdous A. [1 ]
Biswas, Santosh [1 ]
Nandi, Sukumar [1 ]
机构
[1] Indian Inst Technol Guwahati, Gauhati 781039, India
来源
关键词
Discrete event systems; ClamAV; DDoS; Malwares;
D O I
10.1007/978-81-322-2464-8_1
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
With the growing use and popularity of Internet among people, security threats such as viruses, worms etc., are also rapidly increasing. In order to detect and prevent such threats, many antivirus softwares have been created. Signature matching approach used to detect malwares can be easily thwarted by using code obfuscation techniques. In this paper, we propose a discrete event systems-based approach to detect obfuscated malwares in a system, taking Bagle. A as our test virus. Commonly used obfuscation techniques have been applied to bagle. We built DES models for a process under attack and normal conditions with system calls as events. Based on the system calls evoked by any process, our detector will determine its maliciousness by comparing it with both the models.
引用
收藏
页码:3 / 16
页数:14
相关论文
共 50 条
  • [21] ObfusGate: Representation Learning-Based Gatekeeper for Hardware-Level Obfuscated Malware Detection
    Lie, Zhangying
    Fernandes, Chelsea William
    Sayadi, Hossein
    2024 DESIGN, AUTOMATION & TEST IN EUROPE CONFERENCE & EXHIBITION, DATE, 2024,
  • [22] A Robust Malware Detection Approach for Android System Based on Ensemble Learning
    Li, Wenjia
    Cai, Juecong
    Wang, Zi
    Cheng, Sihua
    UBIQUITOUS SECURITY, 2022, 1557 : 309 - 321
  • [23] Memory-efficient detection of large-scale obfuscated malware
    Wang Y.
    Zhang M.
    International Journal of Wireless and Mobile Computing, 2024, 26 (01) : 48 - 60
  • [24] Obfuscated Privacy Malware Classifiers Based on Memory Dumping Analysis
    Cevallos-Salas, David
    Grijalva, Felipe
    Estrada-Jimenez, Jose
    Benitez, Diego
    Andrade, Roberto
    IEEE ACCESS, 2024, 12 : 17481 - 17498
  • [25] Towards Behavior-Based Analysis of Android Obfuscated Malware
    Sawadogo, Zakaria
    Khan, Muhammad Taimoor
    Loukas, George
    Dembele, Jean-Marie
    Sakellari, Georgia
    Mendy, Gervais
    SOFTWARE ARCHITECTURE: ECSA 2023 TRACKS, WORKSHOPS, AND DOCTORAL SYMPOSIUM, ECSA 2023, CASA 2023, AMP 2023, FAACS 2023, DEMESSA 2023, QUALIFIER 2023, TWINARCH 2023, 2024, 14590 : 151 - 165
  • [26] An Artificial Immune System Approach for Malware Detection
    Zeng, Jinquan
    Tang, Weiwen
    BIO-INSPIRED COMPUTING - THEORIES AND APPLICATIONS, BIC-TA 2014, 2014, 472 : 557 - 561
  • [27] Machine Learning Based Obfuscated Malware Detection in the Cloud Environment with Nature-Inspired Feature Selection
    Ghazi, Mohd. Rehan
    Raghava, N. S.
    2022 5TH INTERNATIONAL CONFERENCE ON MULTIMEDIA, SIGNAL PROCESSING AND COMMUNICATION TECHNOLOGIES (IMPACT), 2022,
  • [28] Towards the Usage of Invariant-based App Behavioral Fingerprinting for the Detection of Obfuscated Versions of Known Malware
    Shehu, Zigrid
    Ciccotelli, Claudio
    Ucci, Daniele
    Aniello, Leonardo
    Baldoni, Roberto
    2016 10TH INTERNATIONAL CONFERENCE ON NEXT GENERATION MOBILE APPLICATIONS, SECURITY AND TECHNOLOGIES (NGMAST), 2016, : 121 - 126
  • [29] Detecting new obfuscated malware variants: A lightweight and interpretable machine learning approach
    Madamidola, Oladipo A.
    Ngobigha, Felix
    Ez-zizi, Adnane
    INTELLIGENT SYSTEMS WITH APPLICATIONS, 2025, 25
  • [30] A BEHAVIOR-BASED APPROACH FOR MALWARE DETECTION
    Mosli, Rayan
    Li, Rui
    Yuan, Bo
    Pan, Yin
    ADVANCES IN DIGITAL FORENSICS XIII, 2017, 511 : 187 - 201