Efficient Approach for Malware Detection Using Machine Learning Classifier
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-2854-5_14
Reference17 articles.
1. Naseer M, Rusdi J, Shanono N, Salam S, Zulkiflee M, Abu N, Abadi I (2021) Malware detection: issues and challenges. J Phys Conf Ser 1807:012011. https://doi.org/10.1088/1742-6596/1807/1/012011
2. Nikam UV, Deshmuh VM (2022) Performance evaluation of machine learning classifiers in malware detection. In: 2022 IEEE international conference on distributed computing and electrical circuits and electronics (ICDCECE), pp 1–5.https://doi.org/10.1109/ICDCECE53908.2022.9793102
3. Arslan RS (2021) Identify type of android malware with machine learning based ensemble model. In: 2021 5th international symposium on multidisciplinary studies and innovative technologies (ISMSIT), pp 628–632. https://doi.org/10.1109/ISMSIT52890.2021.9604661
4. Ali R, Ali A, Iqbal F, Hussain M, Ullah F (2022) Deep learning methods for malware and intrusion detection: a systematic literature review. Sec Commun Netw 2022:31. Article ID 2959222. https://doi.org/10.1155/2022/2959222
5. Miranda TC et al (2022) Debiasing android malware datasets: how can I trust your results if your dataset is biased? IEEE Trans Inform Forensics Sec 17:2182–2197
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