Detecting Android malware using Long Short-term Memory (LSTM)

Ravi, Vinayakumar; Soman, K. P.; Poornachandran, Prabaharan; Kumar, Sachin

doi:10.3233/jifs-169424

Cited by 124 publications

(54 citation statements)

References 22 publications

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“…The proposed model is evaluated using 4208 apps and it achieves an accuracy of 95%. In [368], The effectiveness of RNN and LSTM models for detection of malware in android systems is studied. The models are evaluated using well-known datasets and it is found that the LSTM performs better than RNN.…”

Section: ) Deep Neural Network (Dnn)mentioning

confidence: 99%

A Comprehensive Tutorial and Survey of Applications of Deep Learning for Cyber Security

Ravi¹,

Alazab²,

Sriram³

et al. 2020

Preprint

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Section: ) Deep Neural Network (Dnn)mentioning

confidence: 99%

A Comprehensive Tutorial and Survey of Applications of Deep Learning for Cyber Security

Ravi¹,

Alazab²,

Sriram³

et al. 2020

Preprint

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“…With the rapid development of the 3G/4G mobile Internet, a large number of malicious applications have emerged in the years [1], and they pose a great threat to the security of the Android platform [2]. The analysis and detection of Android malware has attracted widespread attention from academia and industry.…”

Section: Introductionmentioning

confidence: 99%

Android Malware Analysis and Detection Based on Attention-CNN-LSTM

Luo¹,

Liu²,

Li³

et al. 2019

JCP

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The increasing number of Android malware has made detection and analysis more difficult, aiming to the current malware attacking Android. This paper proposes an Android malware analysis and detection technology based on Attention-CNN-LSTM, which is a types of Multimodel Deep Learning. Selecting open source malware datasets of Drebin for research, extracting texture fingerprint information of Android malware to reflect the similarity of malware binary file blocks, at the same time, in order to improve the detection accuracy, AndroidMainfest.xml is treated as a text document, and its contextual text features are extracted through NLP. Besides, the above two types of features are merged to enhance the expression capability of texture fingerprint information , and Deep Belief Network is used to screen the above features. Above all, the texture fingerprint is processed by one-dimensional serial signal processing, and the end-to-end local correlation features are extracted according to a one-dimensional time-do main convolutional network. At the same time, considering the context relationship of the timing signal for the AndroidMainfest.xml text, combined with the LSTM model with stronger time-series modeling capabilities to analyze and detect the Android malicious code. The experimental results show that the proposed method can detect and analyze malware more effectively.

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“…We implement the MTS trading strategy using the long short-term memory network (LSTM) and compare its performance to the pas-sive trading strategy. LSTMs are found usable in broad areas, such as sentence classification [2], trajectory prediction of autonomous vehicles [9], flood forecasting [8] and malware detection [15]. Furthermore, LSTM are used in engineering for estimating remaining useful life of systems [17] and in medicine for automated diagnosis of arrhythmia [11].…”

Section: Introductionmentioning

confidence: 99%

Online Long Short-Term Memory Network for Stock Trading

Fister¹,

Jagrič²

2019

StuCoSReC. Proceedings of the 2019 6th Student Computer Science Research Conference

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Economic theory teaches that it is impossible to earn higherthan-normal returns on trading stocks, and that the only chance to earn higher profits is to take higher risk. Often, the practice reveals that higher-than-normal stock returns may indeed be earned, which is a hypothesis we would like to empirically test. In this way, we design so-called mechanical trading system, which focuses on the technical analysis of past stock data, and perform daily trading for the German stock from period 2010-2019. Long short-term memory network is taken as a basis of the mechanical trading system. Obtained results show that higher-than-normal returns are easily obtainable, which polemicizes the efficiency of the observed stock.

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Detecting Android malware using Long Short-term Memory (LSTM)

Cited by 124 publications

References 22 publications

A Comprehensive Tutorial and Survey of Applications of Deep Learning for Cyber Security

A Comprehensive Tutorial and Survey of Applications of Deep Learning for Cyber Security

Android Malware Analysis and Detection Based on Attention-CNN-LSTM

Online Long Short-Term Memory Network for Stock Trading

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