ACCURACY COMPARISON OF HYBRID CNNBILSTM AND MACHINE LEARNING MODELS IN ANDROID MALWARE DETECTION
Keywords:
Android, malware, detection, Hybrid CNN-BiLSTM, machine learning, permissions, API, cybersecurityAbstract
Malware is malicious software designed to damage computer and mobile devices by accessing, stealing personal information, or taking control of devices through networks. This threat is grave on Android platforms, as it can disrupt device performance and compromise user data. Therefore, effective and reliable malware detection methods are required. This research aims to develop and evaluate a malware detection method for Android devices using a Hybrid Convolutional Neural Network (CNN+BiLSTM) approach. The dataset used in this study was sourced from TUANDROMD (Tezpur University Android Malware Dataset). The results indicate that the Hybrid CNN-BiLSTM method achieves a higher accuracy than other machine learning methods. These findings suggest that this hybrid approach is one of the solutions that can be used to improve the security of Android devices against malware attacks compared to CNN and other machine learning methods in this study.
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