Business Continuity Plan and Disaster Recovery Plan : Assignment

Running head: BUSINESS CONTINUITY PLAN AND DISASTER RECOVERY PLAN
Business Continuity Plan and Disaster Recovery Plan
Name of the Student
Name of the University
Author Note

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BUSINESS CONTINUITY PLAN AND DISASTER RECOVERY PLAN
Hackers use some of the most basic techniques for the hiding of their malware codes
from the detection of antivirus software’s. This report discusses the most advanced options,
which are used for by the hackers for the displaying of the new tactics and technology for the
detection of evasive malwares:
 Debuggers and anti-disassembly: Malware developers know the process, which is
used by the malware research workers use and the tools that are used for the hunting
of the threats. Researchers and programmers use disassemblers for the debugging of
what the code does (Lee & Park, 2016). There are many tools, which are used for the
detection of disassemblers, and debuggers many of which are included into the
windows functions.
 Rootkits: At the highest level of work, rootkits are used as a combination of tools and
techniques, which helps in the burrowing into the system, and successfully hide into
the operating system (Alazab et al., 2014). Processors inside computers have the
privilege of executing. These are exploited by the attackers into tricking the higher
level programs to grant them the access privilege. In a windows or Linux environment
there is the availability of user space and kernel space (Saracino et al., 2016). The
highest level is the kernel space. If a malware needs to hide itself in the operating
system the files needs to be embedded into the kernel space rather than the user space.
 Code, DLL, and Process Injection: Processes injection and dynamic-link library
(DLL) injection is a variety of techniques, which is used for the execution of codes
under the context of other procedures (Narudin et al., 2016). Malware developers
often makes use of these techniques to execute their codes in other windows
processes. They might inject codes into certain executable files of the windows
system. By using this procedure, the malware detection software cannot make out if
the program is a malware or not. It already knows that the file is not a malware but the

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BUSINESS CONTINUITY PLAN AND DISASTER RECOVERY PLAN
code is of malware quality. The process becomes difficult to find and kill. Malwares
can often be hooked onto a process, which has the ability of network capabilities for
the masking of their malicious network usage (Shabtai et al., 2014). Over the time,
Microsoft has worked on the patches of the processes to limit the code injection
techniques being used by the malware developers. However, the malware developers
are constantly finding new ways to work on the process of code injection.
These are some of the most commonly used procedures, which the malware
developers are using for the evasion of antivirus (Yerima, Sezer & McWilliams, 2014). Other
processes include the use of binding techniques for the attacking of the illegitimate program
into the main executable program and timing the attacks based on the avoidance of automated
analysis (Tang, Sethumadhavan & Stolfo, 2014). Most of the evasion techniques discussed in
this discussion work by the process of injecting codes and making a signature based
detection. There are other malware system, which are able to change the look of the program
and the working procedure of the program so that the file cannot execute its work.

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BUSINESS CONTINUITY PLAN AND DISASTER RECOVERY PLAN
References
Alazab, A., Hobbs, M., Abawajy, J., Khraisat, A., & Alazab, M. (2014). Using response
action with intelligent intrusion detection and prevention system against web
application malware. Information Management & Computer Security, 22(5), 431-449.
Available at: http://dro.deakin.edu.au/eserv/DU:30070785/hobbs-usingreponse-post-
2014.pdf
Lee, J. K., & Park, J. H. (2016). HB-DIPM: Human Behavior Analysis-Based Malware
Detection and Intrusion Prevention Model in the Future Internet. Journal of
information processing systems, 12(3), 489-501. Available at:
http://www.papersearch.net/thesis/article.asp?KEY=3482634
Narudin, F. A., Feizollah, A., Anuar, N. B., & Gani, A. (2016). Evaluation of machine
learning classifiers for mobile malware detection. Soft Computing, 20(1), 343-357.
Available at:
https://pdfs.semanticscholar.org/cf6b/5797d922678f0f03a8bbad96b0d7482d8c02.pdf
Saracino, A., Sgandurra, D., Dini, G., & Martinelli, F. (2016). Madam: Effective and efficient
behavior-based android malware detection and prevention. IEEE Transactions on
Dependable and Secure Computing. Available at:
http://www.micansinfotech.com/VIDEO-ABSTRACT-NS2-2016/MADAM
%20Effective%20and%20Efficient%20Behavior-based.pdf
Shabtai, A., Tenenboim-Chekina, L., Mimran, D., Rokach, L., Shapira, B., & Elovici, Y.
(2014). Mobile malware detection through analysis of deviations in application
network behavior. Computers & Security, 43, 1-18. Available at:
http://sci-hub.cc/10.1016/j.cose.2014.02.009

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BUSINESS CONTINUITY PLAN AND DISASTER RECOVERY PLAN
Tang, A., Sethumadhavan, S., & Stolfo, S. J. (2014, September). Unsupervised anomaly-
based malware detection using hardware features. In International Workshop on
Recent Advances in Intrusion Detection (pp. 109-129). Springer, Cham. Available at:
https://arxiv.org/pdf/1403.1631.pdf
Yerima, S. Y., Sezer, S., & McWilliams, G. (2014). Analysis of Bayesian classification-based
approaches for Android malware detection. IET Information Security, 8(1), 25-36.
Available at: https://arxiv.org/ftp/arxiv/papers/1608/1608.05812.pdf