Classification of Malware from the Network Traffic Using Hybrid and Deep Learning Based Approach

Mobile connectivity and smart devices are spreading worldwide. As a result, the use of mobile devices and applications is rising exponentially. Therefore, nowadays hackers target such smart devices to steal information and misuse it for malicious purposes. It becomes absolutely essential to protect sensitive information such as app. permissions, login credentials, browse history, media contents etc. from intruders. Security can be breached easily if smart techniques are not devised to safeguard mobile data. In this article, an attempt is made to classify the different types of malware and to protect the sensitive information on Android devices that significantly reduce network congestion and improve network throughput by increasing data transmission. The proposed hybrid approach consists of AdaBoost, random forest and deep learning methods jointly classify the sophisticated malware. The empirical results indicate that this achieves better classification and detection accuracy and is capable of identifying the potential threat more efficiently. © 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.