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Papers/Sequential Embedding-based Attentive (SEA) classifier for ...

Sequential Embedding-based Attentive (SEA) classifier for malware classification

Muhammad Ahmed, Anam Qureshi, Jawwad Ahmed Shamsi, Murk Marvi

2023-02-11Malware ClassificationClassificationMalware Detection
PaperPDFCode(official)

Abstract

The tremendous growth in smart devices has uplifted several security threats. One of the most prominent threats is malicious software also known as malware. Malware has the capability of corrupting a device and collapsing an entire network. Therefore, its early detection and mitigation are extremely important to avoid catastrophic effects. In this work, we came up with a solution for malware detection using state-of-the-art natural language processing (NLP) techniques. Our main focus is to provide a lightweight yet effective classifier for malware detection which can be used for heterogeneous devices, be it a resource constraint device or a resourceful machine. Our proposed model is tested on the benchmark data set with an accuracy and log loss score of 99.13 percent and 0.04 respectively.

Results

TaskDatasetMetricValueModel
Malware ClassificationMicrosoft Malware Classification ChallengeAccuracy (10-fold)0.9912SEA
Malware ClassificationMicrosoft Malware Classification ChallengeLogLoss0.0431SEA
Malware ClassificationMicrosoft Malware Classification ChallengeMacro F1 (10-fold)0.9908SEA

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