EVALUATING THE EFFECTIVENESS OF ANTIVIRUS EVASION TOOLS AGAINST WINDOWS PLATFORM
Keywords:
Antivirus, Evasion Tools, Malware, Metasploit, HackersAbstract
Despite the prevalence of cyber-crimes, information and communication technology ICT has become the most convenient medium of communication and information exchanges. With this development, the information security breach is now one of the complex and challenging issues software developers are facing. The tools that have been developed for penetration testing with the purpose to raise the level of security strength, have been used also by malicious intruders to gain access to our devices. This paper aimed to evaluate the effectiveness of some selected antivirus (AV) evasion tools: Avet, Veil 3.0, PeCloak.py, Shellter, and a Fat Rat, against a Window platform. The selection of these tools was made for the purpose of testing how they can generate undetectable malware against the current best Antivirus Solution products in the market. This, in turn, revealed AV solutions with the best performance in detecting malware with evasion capability. The paper adopted an experimental research design, in a Virtual lab setup with VMware Oracle VirtualBox, consisted of two machines (attacking and target machine). The results obtained indicated that the software evasion ranges from 0% to 83%. The Avet and PeCloak.py AV evasion tools were the best, while Kaspersky and Bitdefender antivirus appeared to be the best performing software protection in detecting the malware evasion tricks.
References
Ade-Ojo, Toluwani (2018). Development of an intelligent decision support system for prompt diagnosis of Ebola and Lassa fever disease (Doctoral dissertation, Federal University Oye- Ekiti).
ACS. (2017). Breast Cancer Basics. About Breast Cancer, 6-8.
Brook, A., El-Yaniv, R., Isler, E., Kimmel, R., Meir, R., and Peleg, D. (2008). Breast cancer diagnosis from biopsy images using generic features and SVMs. Technion - Computer Science Department - Technical Report CS-2008-07 - 2008, 1-16.
Charan, S., Khan, M. J., and Khurshid, K. (2018). Breast cancer detection in mammogram using convolutional neural network. Department of Electrical Engineering Institute of Space Technology, 1-6.
Fallahi, A., & Jafari, S. (2011). An expert system for detection of breast cancer using data preprocessing and bayesian network. International Journal of Advanced Science and Technology Vol. 34, 1-6.
Hamouda S, (2017). Enhancement accuracy of breast tumor diagnosis in digital mammograms. Journal of Biomedical Sciences, 1-8.
Higa, A. (2018). Diagnosis of breast cancer using decision tree and artificial neural network algorithms. International Journal of Computer Applications Technology and Research,7(1):1-6.
Khodary, S., El-Ezz, R. H., and Wahed, M. E. (2017). Enhancement accuracy of breast tumor diagnosis in digital mammograms. Journal of Biomedical Science, 6(4): 1-8.
Levy, D. and Jain, A. (2016). Breast mass classification from mammograms using deep convolutional neural networks. 1-6.
Lotfy, E. A. and Salem, A.-B. M. (2010). A breast cancer classifier based on a combination of case-based reasonong and ontology approach. International Multiconference on Computer Science and Information Technology, 1-8.
Nahid, A.A. and Kong, Y. (2018). Histopathological breast-image classification using local and frequency domains by convolutional neural network. School of Engineering, Macquarie University, Sydney, 1-26.
Narang, S., Verma, H. K., & Sachdev, U. (2012). Breast cancer detection using art2 model of neural networks. International Journal of Computer Applications (0975 – 8887), 1-5.
Wang, L. (2017). Early diagnosis of breast cancer. Sensors, 1-20.
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FUDMA Journal of Sciences
