DL-FHMC: Deep Learning-Based Fine-Grained Hierarchical Learning Approach for Robust Malware Classification

Abusnaina, Ahmed; Abuhamad, Mohammed; Alasmary, Hisham; Anwar, Afsah; Jang, Rhongho; Salem, Saeed; Nyang, DaeHun; Mohaisen, Aziz

doi:10.1109/tdsc.2021.3097296

Cited by 28 publications

(9 citation statements)

References 45 publications

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“…According to the quantity of manually labelled data required for the algorithmic model to be trained, the current FGI classification methods using DL can be divided into two categories: those that use strongly supervised information and those that use weakly supervised information [5]. Among them, the latter combines the target detection algorithm, attention mechanism, reinforcement learning and other methods, which not only saves the cost of manual annotation, but also has more research value for practical application and promotion on the basis of meeting or even exceeding the FGI classification method based on strongly supervised information [6] In addition, Chen X proposed an improved model fusing segmented linear representation and weighted support vector machine for the problems related to FGI classification by applying it in real stock image analysis, thus effectively improving the recognition accuracy of FGI [11].…”

Section: Related Workmentioning

confidence: 99%

Multi-Model Fusion Fine-Grained Image Classification Method Based on Migration Learning

Zhang,

Wang

2024

IEEE Access

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Current single-model methods for fine-grained image classification suffer from insufficient generalisation ability, while multi-model fusion methods suffer from weight curing. The study suggests and experimentally tests a dynamic weight multi-model fusion strategy for transfer learning-based fine-grained picture classification. The results of the experiment showed that the suggested fusion model enhanced recognition accuracy by 1.33%, 1.19%, and 0.83% compared to the single model on the medical dataset and 3.25%, 1.34%, and 7.28% on the agronomy dataset, respectively. Furthermore, when compared to the comparison method, the models under the proposed method of the study improved recognition accuracy by 0.18%, 0.61%, 0.43%, and 0.43% on the medical dataset, and the experimental time consumed was 3.25 minutes less than that of the sum-of-maximum-probabilities method; however, the fusion models of the proposed method of the study had higher recognition accuracy than that of the comparison met Overall, the proposed dynamic weight multi-model fusion method for fine-grained image classification using migration learning has better performance and generalisation ability, which can improve performance while reducing time cost, and has higher application value for the actual fine-grained image classification task.

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Section: Related Workmentioning

confidence: 99%

Multi-Model Fusion Fine-Grained Image Classification Method Based on Migration Learning

Zhang,

Wang

2024

IEEE Access

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“…The researchers have also focused on several Deep Learning techniques for IoT malware analysis and classification, such as in [60], [61], [62] and [63]. For example, the authors of [64] propose an approach for Linux IoT botnet detection based on a combination of PSI graphs and a Convolutional Neural Network (CNN).…”

Section: Figure 1 Taxonomy Of Iot Malware Analysismentioning

confidence: 99%

IoT Malware Analysis Using Federated Learning: A Comprehensive Survey

2023

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The Internet of Things (IoT) has paved the way to a highly connected society where all things are interconnected and exchanging information has become more accessible through the internet. With the use of IoT devices, the threat of malware has increased rapidly. The increased number of existing and new malware variants has made protecting IoT devices and networks challenging. The malware can hide in the systems and disables its activity when there are attempts to discover and detect them. With technological advances, there are various emerging techniques to address this problem. However, they still encounter issues concerning the privacy and security of the user's data and suffer from a single point of failure. To address this issue, there are recent research developments conducted to use Federated Learning (FL). FL is a decentralized technique that trains the user's data on-device and exchanges the parameters without sharing the user's data. FL is implemented to secure the user's data, provide safe and accurate models, and prevent the single point of failure in the centralized models. This paper provides an overview of different approaches that integrate FL with IoT. Finally, we discuss the applications of FL, the research challenges, and future research directions.

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“…Feature-based Methods. In early works, deep learning models are trained from carefully crafted malware features [7,15,16,18,26,28,34,58,63]. When checking a suspicious sample, models need to extract the specific features, process them in specific ways, and then detect malicious codes to give their results.…”

Section: Deep Malware Detectionmentioning

confidence: 99%

“…Also, attacking based on the gradient is an effective way to confuse deep learning models [17,30,32]. On the contrary, there are also plenty of solutions to this problem [7,12,20,33,57]. Although SeqNet could defend against several attacks, we still cannot completely guarantee the safety of SeqNet.…”

Section: Limitationmentioning

confidence: 99%

SeqNet: An Efficient Neural Network for Automatic Malware Detection

Xu¹,

Fu²,

Bu³

et al. 2022

Preprint

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Malware continues to evolve rapidly, and more than 450,000 new samples are captured every day, which makes manual malware analysis impractical. However, existing deep learning detection models need manual feature engineering or require high computational overhead for long training processes, which might be laborious to select feature space and difficult to retrain for mitigating model aging. Therefore, a crucial requirement for a detector is to realize automatic and efficient detection.In this paper, we propose a lightweight malware detection model called SeqNet which could be trained at high speed with low memory required on the raw binaries. By avoiding contextual confusion and reducing semantic loss, SeqNet maintains the detection accuracy when reducing the number of parameters to only 136K. We demonstrate the effectiveness of our methods and the low training cost requirement of SeqNet in our experiments. Besides, we make our datasets and codes public to stimulate further academic research.

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DL-FHMC: Deep Learning-Based Fine-Grained Hierarchical Learning Approach for Robust Malware Classification

Cited by 28 publications

References 45 publications

Multi-Model Fusion Fine-Grained Image Classification Method Based on Migration Learning

Multi-Model Fusion Fine-Grained Image Classification Method Based on Migration Learning

IoT Malware Analysis Using Federated Learning: A Comprehensive Survey

SeqNet: An Efficient Neural Network for Automatic Malware Detection

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