Abstract
Malware is malicious code that tends to take control of the system remotely. The author of these codes drops their malicious payload on to the vulnerable system and continues to maintain access to this system at will. In order to unravel and establish the ability of rootkit to hide system network interface, we developed a network model, and implementation of this model was carried out on four notable live rootkits. Our results show the ability of the four rootkits to hide the system network interfaces, which are being used by the attackers to gain access and communicate correctly with the compromised system.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Huda S, Islam R, Abawajy J, Yearwood J, Hassan MM, Fortino G (2018) A hybrid-multi filter-wrapper framework to identify run-time behaviour for fast malware detection. Future Gener Comput Syst 83:193–207
Nikolopoulos SD, Polenakis I (2017) Preventing malware pandemics in mobile devices by establishing response-time bounds. J Inf Secur Appl 37:1–14
Salehi Z, Sami A, Ghiasi M (2014) Using feature generation from API calls for malware detection. Comput Fraud Secur 2014(9):9–18
Marpaung JAP, Sain M, Hoon-Jae L (2012) Survey on malware evasion techniques: state of the art and challenges. In: 2012 14th International Conference Advanced Communication Technology (ICACT), pp 744–749 ISSN: 1738-9445. Retrieved from http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6174775
Hwang HJ, Tak JI, Nah SY (2011) The perception of computer security focused on the familiarity of rootkits in Korea and Kazakhstan. Int J Softw Eng Appl 5(2):13–24
Chalurkar SN, Meshram BB (2012) Detection of traditional and new types of Malware using Host-based detection scheme. Int J Adv Res Comput Eng Technol (IJARCET) 1(4):341
Carvey H (2014) Malware detection. Windows forensic analysis toolkit, Chapter six, 4th edn. Advanced Analysis Techniques for Windows 8, pp 169–209
Maiorca D, Ariu D, Corona I, Aresu M, Giacinto G (2015) Stealth attacks: an extended insight into the obfuscation effects on android malware. Comput Secur 51:16–31
Miller LC, Gregory PH (2016) CISSP for dummies. Wiley
Bazargan F, Yeun CY, Zemerly MJ (2012) State-of-the-art of virtualization, its security threats and deployment models. Int J Inf Secur Res (IJISR) 2(3/4):335–343
Cheenu MS (2014) A review of ZeroAccess peer-to-peer Botnet. Int J Comput Trends Technol (IJCTT) 12(2). Retrieved from http://www.ijcttjournal.org/Volume12/number-2/IJCTT-V12P112.pdf
Kornblum JD, ManTech C (2006) Exploiting the rootkit paradox with windows memory analysis. Int J Digital Evid 5(1):1–5
Rrushi, JL (2016) NIC displays to thwart malware attacks mounted from within the OS. Comput Secur 61:59–71
Nguyen G, Nguyen BM, Tran D, Hluchy L (2018) A heuristics approach to mine behavioural data logs in mobile malware detection system. Data Knowl Eng
Abazari F, Analoui M, Takabi H (2016) Effect of anti-malware software on infectious nodes in cloud environment. Comput Secur 58:139–148
Genge B, Graur F, Haller P (2015) Experimental assessment of network design approaches for protecting industrial control systems. Int J Crit Infrastruct Prot 11:24–38
Ding Y, Xia X, Chen S, Li Y (2018) A malware detection method based on family behavior graph. Comput Secur 73:73–86
Lin C-H, Pao H-K, Liao J-W (2018) Efficient dynamic malware analysis using virtual time control mechanics. Comput Secur 73:359–373
Talha, KA, Alper DI, Aydin C (2015) APK auditor: permission-based Android malware detection system. Digital Investig 13:1–14
Alam S, Horspool RN, Traore I, Sogukpinar I (2015) A framework for metamorphic malware analysis and real-time detection. Comput Secur 48:212–233
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Subairu, S.O. et al. (2020). An Experimental Approach to Unravel Effects of Malware on System Network Interface. In: Jain, V., Chaudhary, G., Taplamacioglu, M., Agarwal, M. (eds) Advances in Data Sciences, Security and Applications. Lecture Notes in Electrical Engineering, vol 612. Springer, Singapore. https://doi.org/10.1007/978-981-15-0372-6_17
Download citation
DOI: https://doi.org/10.1007/978-981-15-0372-6_17
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-0371-9
Online ISBN: 978-981-15-0372-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)