Self-attention based convolutional-LSTM for android malware detection using network traffics grayscale image

Shen, Limin; Feng, Jiayin; Chen, Zhe; Sun, Zhongkui; Liang, Dongkui; Li, Hui; Wang, Yuying

doi:10.1007/s10489-022-03523-2

Cited by 8 publications

(1 citation statement)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They used Quantum Mayfly optimization for feature selection and Butterfly optimization for parameter tuning and get an accuracy of 98.33%. Shen et al [47] investigated the network traffic flow and converted them into grayscale images while maintaining the chronological order of occurrences of network traffic. They used self-attention-based convolutional-LSTM models to classify the malware by category and family.…”

Section: Related Workmentioning

confidence: 99%

Android Malware Detection using LSTM with Smali Codes

Anand

Singh

Khan

et al. 2023

Preprint

View full text Add to dashboard Cite

With the widespread adoption of smartphones and the rapid growth of the mobile Internet, the Android platform has become highly popular. However, its open-source nature has also made it susceptible to rampant malware attacks. In light of this, this research paper proposes a malware detection system based on Smali-LSTM to enhance the efficiency of malware detection on the Android platform. The detection system employs a static analysis approach to extract Smali files from applications. These extracted Smali files undergo a series of preprocessing steps to extract relevant features. To ensure compatibility with the LSTM model, the preprocessed Smali files are fragmented into smaller pieces. The paper explores and tests fragments of various sizes to identify the optimal configuration that yields the best results. The findings of the study demonstrate that the proposed Smali-LSTM model outperforms existing works that utilize the same dataset and LSTM model. The achieved results showcase an accuracy of 96.58\% and an impressive precision of 100\%. These outcomes validate the effectiveness and superiority of the proposed model in accurately detecting malware in Android applications.

show abstract

Section: Related Workmentioning

confidence: 99%