A deep learning system for health care IoT and smartphone malware detection

Amin, Muhammad; Shehwar, Duri; Ullah, Abrar; Guarda, Teresa; Tanveer, Tamleek Ali; Anwar, Sajid

doi:10.1007/s00521-020-05429-x

Cited by 24 publications

(25 citation statements)

References 31 publications

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“…(ey extract 178 features and rank them using InfoGain to select the top 120 of them for experiments. Other research articles that focused on android malware detection include [88][89][90][91][92][93][94][95][96][97][98][99].…”

Section: Android Malware Detectionmentioning

confidence: 99%

“…CNN [47,112,52,62,36,68,89,77,35,46,56,80,84,70,92,27,51,121,87,34,54,50,76,95,29,100,82,83,114,42,57,79,40,74,63,73,66,26,39,30,119,101,110,61,32,65,120 quality of dataset should be carefully considered when creating and developing predictive models and tools for malware detection [148]. Such datasets are created by the research community to serve as a source of research for empirical analysis and extracting new insights about apps.…”

Section: Algorithmsmentioning

confidence: 99%

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Deep Learning Methods for Malware and Intrusion Detection: A Systematic Literature Review

Ali

Iqbal

et al. 2022

Security and Communication Networks

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Android and Windows are the predominant operating systems used in mobile environment and personal computers and it is expected that their use will rise during the next decade. Malware is one of the main threats faced by these platforms as well as Internet of Things (IoT) environment and the web. With time, these threats are becoming more and more sophisticated and detecting them using traditional machine learning techniques is a hard task. Several research studies have shown that deep learning methods achieve better accuracy comparatively and can learn to efficiently detect and classify new malware samples. In this paper, we present a systematic literature review of the recent studies that focused on intrusion and malware detection and their classification in various environments using deep learning techniques. We searched five well-known digital libraries and collected a total of 107 papers that were published in scholarly journals or preprints. We carefully read the selected literature and critically analyze it to find out which types of threats and what platform the researchers are targeting and how accurately the deep learning-based systems can detect new security threats. This survey will have a positive impact on the learning capabilities of beginners who are interested in starting their research in the area of malware detection using deep learning methods. From the detailed critical analysis, it is identified that CNN, LSTM, DBN, and autoencoders are the most frequently used deep learning methods that have effectively been used in various application scenarios.

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Section: Android Malware Detectionmentioning

confidence: 99%

Section: Algorithmsmentioning

confidence: 99%

Deep Learning Methods for Malware and Intrusion Detection: A Systematic Literature Review

Ali

Iqbal

et al. 2022

Security and Communication Networks

View full text Add to dashboard Cite

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“…DL-based feature detection was proposed in [ 1 ], which was designed with the prime objective of detecting malware, and the application’s behavioral analysis was also done using different classifiers. Moreover, the features that were learned by the detecting system were also found to be reused, so that learning was also transferred towards efficient detection of malware, therefore contributing to accuracy.…”

Section: Introductionmentioning

confidence: 99%

Survival study on deep learning techniques for IoT enabled smart healthcare system

Munnangi

UdhayaKumar²,

Ravi

et al. 2023

Health Technol.

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Purpose The paper is to study a review of the employment of deep learning (DL) techniques inside the healthcare sector, together with the highlight of the strength and shortcomings of existing methods together with several research ultimatums. Our study lays the foundation for healthcare professionals and government with present-day inclinations in DL-based data analytics for smart healthcare. Methods A deep learning-based technique is designed to extract sensor displacement effects and predict abnormalities for activity recognition via Artificial Intelligence (AI). The presented technique minimizes the vanishing gradient issue of Recurrent Neural Networks (RNN), thereby reducing the time for detecting abnormalities with consideration of temporal and spatial factors. Proposed Moran Autocorrelation and Regression-based Elman Recurrent Neural Network (MAR-ERNN) introduced. Results Experimental results show the feasibility of the proposed method. The results show that the proposed method improves accuracy by 95% and reduces execution time by 18%. Conclusion MAR-ERNN performs well in the activity recognition of health status. Collectively, this IoT-enabled smart healthcare system is utilized by enhancing accuracy, and minimizing time and overhead reduction.

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“…Similarly, the Internet of ings (IoT) devices built on different processor architectures have increasingly become targets of adversarial attacks. Although there are many ways to detect malware on the Internet of ings [2,3], we still need to make further efforts in this field.…”

Section: Introductionmentioning

confidence: 99%

A Novel Malware Detection and Family Classification Scheme for IoT Based on DEAM and DenseNet

Wang

Zhao

Wang

et al. 2021

Security and Communication Networks

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With the rapid increase in the amount and type of malware, traditional methods of malware detection and family classification for IoT applications through static and dynamic analysis have been greatly challenged. In this paper, a new simple and effective attention module of Convolutional Neural Networks (CNNs), named as Depthwise Efficient Attention Module (DEAM), is proposed and combined with a DenseNet to propose a new malware detection and family classification model. Based on the good effect of the DenseNet in the field of image classification and the visual similarity of the malware family on images, the gray-scale image transformed from malware is input into the model combined with the DEAM and DenseNet for malware detection, and then the family classification is carried out. The DEAM is a general lightweight attention module improved based on the Convolutional Block Attention Module (CBAM), which can strengthen the attention to the characteristics of malware and improve the model effect. We use the MalImg dataset, Microsoft malware classification challenge dataset (BIG 2015), and our dataset constructed by the two above-mentioned datasets to verify the effectiveness of the proposed model in family classification and malware detection. Experimental results show that the proposed model achieves 99.3% in terms of accuracy for malware detection on our dataset and achieves 98.5% and 97.3% in terms of accuracy for family classification on the MalImg dataset and BIG 2015 dataset, respectively. The model can reliably detect IoT malware and classify its families.

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A deep learning system for health care IoT and smartphone malware detection

Cited by 24 publications

References 31 publications

Deep Learning Methods for Malware and Intrusion Detection: A Systematic Literature Review

Deep Learning Methods for Malware and Intrusion Detection: A Systematic Literature Review

Survival study on deep learning techniques for IoT enabled smart healthcare system

A Novel Malware Detection and Family Classification Scheme for IoT Based on DEAM and DenseNet

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