A Model Based on LSTM Neural Networks to Identify Five Different Types of Malware

Andrade, Elisabete; Viterbo, José; Vasconcelos, Cristina Nader; Guérin, Joris; Bernardini, Flávia

doi:10.1016/j.procs.2019.09.173

Cited by 28 publications

(16 citation statements)

References 9 publications

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“…For personal movement data, the external data to be collected should be the diversified data of gender, age, and body shape. To train the model, the dataset used in this experiment is based on the human movement data published by a large number of researchers on the Internet to form the final model, which is conducive to reflecting the accuracy of LSTM circular neural network model [24,25].…”

Section: Design Of Information Processing Systemmentioning

confidence: 99%

[Retracted] Design and Implementation of Human Motion Recognition Information Processing System Based on LSTM Recurrent Neural Network Algorithm

2021

Computational Intelligence and Neuroscience

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With the comprehensive development of national fitness, men, women, young, and old in China have joined the ranks of fitness. In order to increase the understanding of human movement, many researches have designed a lot of software or hardware to realize the analysis of human movement state. However, the recognition efficiency of various systems or platforms is not high, and the reduction ability is poor, so the recognition information processing system based on LSTM recurrent neural network under deep learning is proposed to collect and recognize human motion data. The system realizes the collection, processing, recognition, storage, and display of human motion data by constructing a three-layer human motion recognition information processing system and introduces LSTM recurrent neural network to optimize the recognition efficiency of the system, simplify the recognition process, and reduce the data missing rate caused by dimension reduction. Finally, we use the known dataset to train the model and analyze the performance and application effect of the system through the actual motion state. The final results show that the performance of LSTM recurrent neural network is better than the traditional algorithm, the accuracy can reach 0.980, and the confusion matrix results show that the recognition of human motion by the system can reach 85 points to the greatest extent. The test shows that the system can recognize and process the human movement data well, which has great application significance for future physical education and daily physical exercise.

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Section: Design Of Information Processing Systemmentioning

confidence: 99%

[Retracted] Design and Implementation of Human Motion Recognition Information Processing System Based on LSTM Recurrent Neural Network Algorithm

2021

Computational Intelligence and Neuroscience

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“…Different malware detection approaches in the literature have adopted different machine-learning techniques, such as random forest (RF) [5][6][7], neural network [9][10][11], decision tree [12,13], naïve Bayes [14,15], KNN and SVM [15], ARIMA [16], and reinforcement learning [17,18].…”

Section: Related Workmentioning

confidence: 99%

“…Neural-network-based approaches in malware detection were introduced in [9-11], while a recurrent neural network (RNN)-based model was explored by Andrade et al [9].…”

Section: Related Workmentioning

confidence: 99%

“…Most of the new intelligent approaches to malware detection are trained using the selective features of known malware that can represent malware in its best form. These representations are then used as training instances for a suitable machine-learning algorithm that generalizes or maps such features-based malware detection mechanisms [3][4][5][6][7][8][9][10][11][12][13]. This work extends a previously explored approach called RBCM, which is also based on reinforcement learning [3].…”

Section: Introductionmentioning

confidence: 99%

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A Novel Monte-Carlo Simulation-Based Model for Malware Detection (eRBCM)

2021

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The use of innovative and sophisticated malware definitions poses a serious threat to computer-based information systems. Such malware is adaptive to the existing security solutions and often works without detection. Once malware completes its malicious activity, it self-destructs and leaves no obvious signature for detection and forensic purposes. The detection of such sophisticated malware is very challenging and a non-trivial task because of the malware’s new patterns of exploiting vulnerabilities. Any security solutions require an equal level of sophistication to counter such attacks. In this paper, a novel reinforcement model based on Monte-Carlo simulation called eRBCM is explored to develop a security solution that can detect new and sophisticated network malware definitions. The new model is trained on several kinds of malware and can generalize the malware detection functionality. The model is evaluated using a benchmark set of malware. The results prove that eRBCM can identify a variety of malware with immense accuracy.

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“…For this case, the IoT devices are guarded against malware attacks by developing intelligent edge computing services [13]. Malware can attack any kind of software decisively to damage smart devices and information [14]. In worldwide, there are three methods commonly utilized for malware detection.…”

Section: Introductionmentioning

confidence: 99%

DLSTM-HHO: Enhanced Deep Learning Framework for Malware Detection at the Edge of the Iot System

Santhadevi¹,

Janet²

2021

Preprint

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Internet of Things (IoT) technology has a dynamic atmosphere due to incorporating multiple smart peripherals, which provide autonomous homes, cities, manufacturing industries, medical domain, etc.; however, a threat by cyber security is still at constant risk, and it gets much attention in researches. Cyber issues in the IoT environment are usually coming due to intruder’s malware activity. This kind of malware affects the confidential data of users in the IoT environment. In this research, a novel framework is implemented with the association of an improved deep LSTM with Harris Hawk Optimization (DLSTM-HHO). This framework is highly improved by adopting an Apache Spark technique for pre-processing IoT dataset. An Apache Spark replaces the traditional data pre-processing, which provides more efficiency to this model for detecting the malware at the edge of the IoT environment. The implementation of this framework is done in the MATLAB2020a platform with Apache Spark. The proposed model provides better performance evaluation in terms of accuracy is at 98%, and the F1-Score at 98.5%.

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A Model Based on LSTM Neural Networks to Identify Five Different Types of Malware

Cited by 28 publications

References 9 publications

[Retracted] Design and Implementation of Human Motion Recognition Information Processing System Based on LSTM Recurrent Neural Network Algorithm

[Retracted] Design and Implementation of Human Motion Recognition Information Processing System Based on LSTM Recurrent Neural Network Algorithm

A Novel Monte-Carlo Simulation-Based Model for Malware Detection (eRBCM)

DLSTM-HHO: Enhanced Deep Learning Framework for Malware Detection at the Edge of the Iot System

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