Trend of Malware Detection Using Deep Learning

Lee, Yoon-seon; Lee, Jae-ung; Soh, Wooyoung

doi:10.1145/3206129.3239430

Cited by 6 publications

(3 citation statements)

References 2 publications

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“…Based on their results, the RF algorithm did a lot better than the DNN models. These results indicated that deep learning may not perform well for malware detection [24].…”

Section: Related Workmentioning

confidence: 95%

“…Known malware analysis methods based on deep learning include CNN [27], deep belief network (DBN) [28], graph convolutional network (GCN) [29], long short-term memory (LSTM), gated recurrent unit (GRU) [30], and VGG16 [31]. For example, Lee et al [24] discussed how to use deep learning to analyze malware. For this process, data must be extracted, developed, and network models trained.…”

Section: Related Workmentioning

confidence: 99%

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Windows PE Malware Detection Using Ensemble Learning

et al. 2021

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In this Internet age, there are increasingly many threats to the security and safety of users daily. One of such threats is malicious software otherwise known as malware (ransomware, Trojans, viruses, etc.). The effect of this threat can lead to loss or malicious replacement of important information (such as bank account details, etc.). Malware creators have been able to bypass traditional methods of malware detection, which can be time-consuming and unreliable for unknown malware. This motivates the need for intelligent ways to detect malware, especially new malware which have not been evaluated or studied before. Machine learning provides an intelligent way to detect malware and comprises two stages: feature extraction and classification. This study suggests an ensemble learning-based method for malware detection. The base stage classification is done by a stacked ensemble of fully-connected and one-dimensional convolutional neural networks (CNNs), whereas the end-stage classification is done by a machine learning algorithm. For a meta-learner, we analyzed and compared 15 machine learning classifiers. For comparison, five machine learning algorithms were used: naïve Bayes, decision tree, random forest, gradient boosting, and AdaBoosting. The results of experiments made on the Windows Portable Executable (PE) malware dataset are presented. The best results were obtained by an ensemble of seven neural networks and the ExtraTrees classifier as a final-stage classifier.

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“…Based on their results, the RF algorithm did a lot better than the DNN models. These results indicated that deep learning may not perform well for malware detection [24].…”

Section: Related Workmentioning

confidence: 95%

Section: Related Workmentioning

confidence: 99%

Windows PE Malware Detection Using Ensemble Learning

et al. 2021

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“…Similarly, Kim et al (2019) used a CNN-based construction object detection model to identify collisions at construction sites [25]. employed YOLOv3 to develop an algorithm that automatically detects whether construction workers are wearing proper safety equipment [26].…”

Section: Literature Reviewmentioning

confidence: 99%

Transfer Learning-Based Object Detection Model for Steel Structure Bolt Fastening Inspection

Choi,

Ha,

Lee

2023

Applied Sciences

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As improper inspection of construction works can cause an increase in project costs and a decrease in project quality, construction inspection is considered a critical factor for project success. While traditional inspection tasks are still mainly labor-intensive and time-consuming, computer vision has the potential to revolutionize the construction inspection process by providing more efficient and effective ways to monitor the progress and quality of construction projects. However, previous studies have also indicated that the performance of vision-based site monitoring heavily relies on the volume of training data. To address the issues of challenging data collection at construction sites, this study developed models using transfer learning-based object detection models incorporating data augmentation and transfer learning. The performance of three object detection algorithms was compared based on average precision and inference time for detecting T/S bolt fastening of steel structure. Despite the limited training data available, the model’s performance was improved through data augmentation and transfer learning. The proposed inspection model can increase the efficiency of quality control works for building construction projects and the safety of inspectors.

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