Total views : 566
A Dynamic Malware Analysis for Windows Platform - A Survey
Background: The progression of malware is on upsurge lately. The architects of malware make it robust and sheath such that it becomes untraceable while running and hence users fall-prey for these malicious software. These malicious software programs developed by attackers are polymorphic and metamorphic which have the capability to alter their code as they propagate. Methods: The existing malware detection and prevention tools need to be enhanced when it comes to these newly developed malwares. So, to prevent this we take a generic approach that integrates the methods and tools which already exist in order to detect the malware with utmost accuracy and efficiency. Findings: The survey on this paper gives a picture to make use of Op-Code frequency and n-gram for feature extraction and efficient way for detecting malware incase gets on to the system by any means. Different authors claim that they five the best results by increasing the true positives and decreasing the False positive rates. Application: Dynamic and Hybrid methods can be used to detect known and unknown malwares.
Accuracy and Efficiency, Malware, N-Gram Method, Op-Code Frequency
- Gandotra E, Bansal D, Sofat S. Malware Analysis and Classification: A Survey. Journal of Information Security. 2014;5(2):56–64.
- Distler D. Malware Analysis: An Introduction. SANS Institute InfoSec Reading Room. 2007.
- Wen S, Zhou W, Zhang J, Xiang Y, Zhou W, Jia W et al. Modeling and Analysis on the Propagation Dynamics of Modern Email Malware. IEEE Transaction on dependable and Secure Computing. 2014; 11(4):361–74.
- Gadhiya S, Bhavsar K. Techniques for Malware Analysis. IJARCSSE. 2013 Apr; 3(4):972–5.
- Zhao L, Ren X, Liu M, Wang L, Zhang H, Zhang H. Collaborative Reversing of Input Formats and Program Data Structures for Security Applications. China Communications.2014; 11(9):135–47.
- Landage J, Wankhade MP. Malware and Malware Detection Techniques: A Survey. International Journal of Engineering Research and Technology (IJERT). 2013 Dec; 2(12):61–8.
- Uppal D, Mehra V, Verma V. Basic survey on Malware Analysis, Tools and Techniques. International Journal on Computational Sciences and Applications (IJCSA). 2014;4(1):103–12.
- Okane P, Sezer S, McLaughlin K, Im E. Malware Detection: Program run length against detection rate. IET Software.2014; 8(1):42–51.
- Alzab M, Layton R, Venkatraman S. Malware Detection Based on Structural and Behavioral Features of API Calls. 1st International Cyber Resilience Conference. Edith Cowan University; Perth, Western Australia; 2010. p. 1–10.
- O’Kane P, Sezer S, McLaughlin K, Im E. SVM Training Phase Reduction Using Dataset Feature Filtering for Malware Detection. IEEE Transactions on Information Forensics and Security. 2013; 8(3):500–9.
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.