MalDeWe: New Malware Website Detector Model based on Natural Language Processing using Balanced Dataset

Dobreva, Jovana; Mitrovikj, Aleksandra Popovska; Dimitrova, Vesna

doi:10.1109/csci54926.2021.00043

Cited by 3 publications

(2 citation statements)

References 12 publications

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“…(1) RNN [ 13 ]: The word embedding settings were unchanged; only the RNN single-channel model was used, L-Softmax enhancement was not added, and Focal Loss was rebalanced.…”

Section: Experiments and Analysismentioning

confidence: 99%

“…The excellent performance of most methods is based on the assumption that the samples between classes in the dataset are balanced [ 11 , 12 , 13 ]. For example, Irsoy et al [ 11 ] applied RNN for text sentiment orientation classification, Kim et al [ 12 ] used CNN for text sentiment orientation classification, and Soni et al [ 14 ] proposed TextConvoNet, a novel convolutional neural network (CNN)-based architecture for solving binary and multi-class text classification problems.…”

Section: Introductionmentioning

confidence: 99%

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Imbalanced Text Sentiment Classification Based on Multi-Channel BLTCN-BLSTM Self-Attention

Cai

Zhang

2023

Sensors

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With the continuous expansion of the field of natural language processing, researchers have found that there is a phenomenon of imbalanced data distribution in some practical problems, and the excellent performance of most methods is based on the assumption that the samples in the dataset are data balanced. Therefore, the imbalanced data classification problem has gradually become a problem that needs to be studied. Aiming at the sentiment information mining of an imbalanced short text review dataset, this paper proposed a fusion multi-channel BLTCN-BLSTM self-attention sentiment classification method. By building a multi-channel BLTCN-BLSTM self-attention network model, the sample after word embedding processing is used as the input of the multi-channel, and after fully extracting features, the self-attention mechanism is fused to strengthen the sentiment to further fully extract text features. At the same time, focus loss rebalancing and classifier enhancement are combined to realize text sentiment predictions. The experimental results show that the optimal F1 value is up to 0.893 on the Chnsenticorp-HPL-10,000 corpus. The comparison and ablation of experimental results, including accuracy, recall, and F1-measure, show that the proposed model can fully integrate the weight of emotional feature words. It effectively improves the sentiment classification performance of imbalanced short-text review data.

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“…(1) RNN [ 13 ]: The word embedding settings were unchanged; only the RNN single-channel model was used, L-Softmax enhancement was not added, and Focal Loss was rebalanced.…”

Section: Experiments and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Imbalanced Text Sentiment Classification Based on Multi-Channel BLTCN-BLSTM Self-Attention

Cai

Zhang

2023

Sensors

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Application of Machine Learning in Intrusion Detection Systems

Gjorgjievska Perusheska,

Dimitrova

2023

Lecture Notes in Networks and Systems

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Survey of Transformer-Based Malicious Software Detection Systems

Alshomrani,

Albeshri,

Alturki

et al. 2024

Electronics

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In the recent past, the level of cyber threats has changed drastically, leading to the current transformation of the cybersecurity landscape. For example, emerging threats like Zero-day and polymorphic malware cannot be detected by conventional detection methods like heuristic and signature-based methods, which have proven useful in the identification of malware. In view of this shift in the cybersecurity paradigm, this study proposes to discuss the utilization of transformer models to improve malware detection effectiveness and the accuracy and efficiency in detecting malicious software. In this regard, this study adopts the application of transformers in identifying different forms of malicious software: ransomware, spyware, and trojans. Transformers are endowed with the ability to handle sequential data and capture intricate patterns. By employing deep learning techniques and conducting thorough contextual analysis, these models enhance the detection process by identifying subtle indications of compromise, which traditional methods may overlook. This research also explains the challenges and limitations related to the application of transformer-based models in real-world cybersecurity settings, which include computing requirements and large-scale labeled datasets’ requirements. By the end, the article suggests potential future research avenues in order to improve and integrate these models into cybersecurity systems.

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MalDeWe: New Malware Website Detector Model based on Natural Language Processing using Balanced Dataset

Cited by 3 publications

References 12 publications

Imbalanced Text Sentiment Classification Based on Multi-Channel BLTCN-BLSTM Self-Attention

Imbalanced Text Sentiment Classification Based on Multi-Channel BLTCN-BLSTM Self-Attention

Application of Machine Learning in Intrusion Detection Systems

Survey of Transformer-Based Malicious Software Detection Systems

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