Selective ensemble of Internet traffic classifiers for improving malware detection

Donald, A. David; Murali, G.

doi:10.1109/icecds.2017.8390121

Cited by 4 publications

(1 citation statement)

References 6 publications

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“…In order to improve the classification performance in classifying benign and malicious mobile application, this research proposed a classification technique that takes the differences of n-gram system call sequence as features and uses ensemble method approach to train multiple classifiers to solve the same problem. Many studies ( [7]; [36]; [37]) have shown that single classifier has their own domain of competence, therefore is not an optimal approach to solve all problems. This limitation leads to the increasing research in ensemble methods among machine learning community.…”

Section: Related Workmentioning

confidence: 99%

Ensemble Method for Mobile Malware Detection using N-Gram Sequences of System Calls

Ariff

2022

Int. j. commun. netw. inf. secur.

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Mobile device has become an essential tool among the community across the globe and has turned into a necessity in daily life. An extensive usage of mobile devices for everyday life tasks such as online banking, online shopping and exchanging e-mails has enable mobile devices to become data storage for users. The data stored in these mobile devices can contain sensitive and critical information to the users. Hence, making mobile devices as the prime target for cybercriminal. To date, Android based mobile devices is one of the mobile devices that are dominating the phone market. Moreover, the ease of use and open-source feature has made Android based mobile devices popular. However, the widely used Android mobile devices has encourage malware author to write malicious application. In a short duration of time mobile malware has rapidly evolve and have the capability to bypass signature detection approach which requires a constant signature update to detect mobile malware. To overcome this drawback an anomaly detection approach can be used to mitigate this issue. Yet, using a single classifier in an anomaly detection approach will not improve the classification detection performance. Based on this reason, this research formulates an ensemble classification method of different n-gram system call sequence features to improve the accuracy of mobile malware detection. This research proposes n-number of classifier models for each different n-gram sequence call feature. The probability output of each classifier is then combined to produce a better classification performance which is better compared to a single classifier.

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Section: Related Workmentioning

confidence: 99%

Ensemble Method for Mobile Malware Detection using N-Gram Sequences of System Calls

Ariff

2022

Int. j. commun. netw. inf. secur.

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Adaptive: An Adaptive Routing Protocol for Vehicle Delay-Tolerant Networks

Silva

2019

IEEE Latin Am. Trans.

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Unlocking the Power of Matlab: A Comprehensive Survey

Srinvias

Donald

Sameena

et al. 2023

IJARSCT

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Matlab has become a popular choice for researchers across various fields due to its versatility, ease of use, and powerful analytical capabilities. In this paper, we explore the role of Matlab as the ultimate solution for research challenges. We first discuss the benefits of using Matlab in research, including its ability to handle complex mathematical computations, data visualization, and simulation of complex systems.

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Selective ensemble of Internet traffic classifiers for improving malware detection

Cited by 4 publications

References 6 publications

Ensemble Method for Mobile Malware Detection using N-Gram Sequences of System Calls

Ensemble Method for Mobile Malware Detection using N-Gram Sequences of System Calls

Adaptive: An Adaptive Routing Protocol for Vehicle Delay-Tolerant Networks

Unlocking the Power of Matlab: A Comprehensive Survey

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