Abstract
Artificial immune system (AIS) is a computational system inspired by the principles and processes of the Biological immune system which has the capabilities to learn, adapt, self tolerance and memories actions, which make it a good example that we can take for solving some major problems in many fields, including the problem of malware detection in the field of computer security. The main idea is to detect any type of files that trying to harm the computer system by infecting some executable software when these files running, spread it to other files or computers. In this paper, we proposed a framework to detect malware using the innate immune system combined with danger theory to eliminate tow major drawbacks of current malware detection methods; detection accuracy and high false positive alarms.
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Acknowledgments
This work and research is done by support of Faculty of Computer Science and Information Systems, Universiti Teknologi Malaysia, Malaysia, Faculty of Mathematical Sciences, University of Khartoum, Sudan.
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Ali, M.A.M., Maarof, M.A. (2013). A Novel Malware Detection Framework Based on Innate Immunity and Danger Theory. In: Kim, K., Chung, KY. (eds) IT Convergence and Security 2012. Lecture Notes in Electrical Engineering, vol 215. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-5860-5_4
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DOI: https://doi.org/10.1007/978-94-007-5860-5_4
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