A Framework for Optimizing Malware Classification by Using Genetic Algorithm

Yusoff, Mohd Najwadi; Jantan, Aman

doi:10.1007/978-3-642-22191-0_5

Cited by 10 publications

(10 citation statements)

References 17 publications

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“…The malware classification based on decision trees is very fast and accurate. The disadvantage of decision trees is that an error in higher level of the tree may cause an error in the lower part of tree [48]. 5) Boosted Classifiers: Boosting [15] is a method of merging multiple classifiers.…”

Section: ) Decision Treesmentioning

confidence: 99%

Pragmatic Analysis on Malware Classification

Chanana¹

2017

IJRASET

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Malware are tiny software which are created to harm our computers or to steal valuable information. Recently with the advancement in machine learning algorithms, researches also started looking to a solution which can detect the malware and classify them automatically so that necessary step can be decided. In this paper we discussed and reviewed the methods adopted and suggested by various researchers to detect and classify malwares using machine learning and clustering algorithms. Since this is not a very old field so research papers used to review are not very old.

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Section: ) Decision Treesmentioning

confidence: 99%

Pragmatic Analysis on Malware Classification

Chanana¹

2017

IJRASET

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“…The malware classification based on the decision trees is very fast and also accurate. The disadvantage of the decision trees is that an error in higher level of the tree may cause an error in the lower part of the tree [48].…”

Section: Decision Treesmentioning

confidence: 99%

Hidden Markov models for malware classification

Annachhatre

Austin

Stamp

2014

J Comput Virol Hack Tech

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“…Yusoff and Jantan [14] implemented Genetic Algorithm (GA) to improve classification and the accuracy rate of PE file that failed to be classified by decision tree classifier. While work done by [15], implemented (GA) to the layered system to detect and filter http botnet attack and has succeeded provide less false positive rate.…”

Section: Previous Workmentioning

confidence: 99%

Systematic Analysis on Mobile Botnet Detection Techniques Using Genetic Algorithm

Rahman

Saudi

2014

Lecture Notes in Electrical Engineering

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Nowadays smart phone has been used all over the world and has become as one of the most targeted platforms of mobile botnet to steal confidential information especially related with online banking. It is seen as one of the most dangerous cyber threat. Therefore in this research paper, a systematic analysis on mobile botnet detection techniques is further investigated and evaluated. A case study was carried out to reverse engineering the mobile botnet codes. Based on the findings, this mobile botnet has successfully posed itself as a fake anti-virus and has the capability to steal important data such as username and password from the Android-based devices. Furthermore, this paper also discusses the challenges and the potential research for future work with relate of the genetic algorithm. This research paper can be used as a reference and guidance for further study on mobile botnet detection techniques.

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A Framework for Optimizing Malware Classification by Using Genetic Algorithm

Cited by 10 publications

References 17 publications

Pragmatic Analysis on Malware Classification

Pragmatic Analysis on Malware Classification

Hidden Markov models for malware classification

Systematic Analysis on Mobile Botnet Detection Techniques Using Genetic Algorithm

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