Intelligent malware classification based on network traffic and data augmentation techniques

Jasim, Ammar D.; Farhan, Rawaa Ismael

doi:10.11591/ijeecs.v30.i2.pp903-908

Cited by 2 publications

(2 citation statements)

References 13 publications

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“…Recently, deep learning techniques such as CNN have proven superior to more standard learning algorithms in a wide range. In view of this achievement, a CNN network was proposed for malware classification, along with data augmentation addresses to improve performance [1]. Bhanu et al [2] provide an integrated structure for dealing with the problem of malware, with a focus on threats sent via SMS messages on android devices.…”

Section: Literature Reviewmentioning

confidence: 99%

A novel method of detecting malware on Android mobile devices with explainable artificial intelligence

Vanjire,

Lakshmi

2024

Bulletin EEI

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The increasing prevalence of malware targeting android mobile devices has raised significant concerns regarding user privacy and security. In response, effective methods for malware classification and detection are crucial to protect users from malicious applications. This paper presents an approach that leverages deep learning techniques and explainable artificial intelligence (XAI) for android mobile malware classification and detection. Convolutional neural networks (CNNs) are deep learning model that has shown impressive performance in several application areas, including image and text classification. In the context of android mobile malware, CNNs have shown promising results in capturing intricate patterns and features inherent in malware samples. By training these models on large datasets of benign and malicious applications, accurate classification can be achieved. To enhance transparency and interpretability, XAI techniques are integrated into the classification process. These techniques provide insights into the decision-making process of the deep learning models, enabling the identification of critical features and characteristics that contribute to the classification results. This research, by combining deep learning and XAI methods, presents a fresh strategy for identifying and categorizing Android malware. This research paper will focus on a fascinating CNN-based malware categorization technique.

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Section: Literature Reviewmentioning

confidence: 99%

A novel method of detecting malware on Android mobile devices with explainable artificial intelligence

Vanjire,

Lakshmi

2024

Bulletin EEI

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“…In recent research, the deep learning (DL) models are widely applied since they have the capability of handling OMM data misinterpretation successfully. However, DL models are robust in nature to detect the malware obfuscation type of programs eminently [12], [13].…”

Section: Introductionmentioning

confidence: 99%

An intelligent obfuscated mobile malware detection using deep supervised learning algorithms

Ganapathi,

Arumugam,

Dhathathri

2024

Bulletin EEI

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Obfuscated mobile malware (OMM) is a malicious software in mobile that hides to avoid detection and annihilation. These types of malwares are thorny to identify due to their inevitable nature. Deep learning (DL) algorithms are the most desirable to detect obfuscated malware based on the ‘n’ number of iterations with adjustable weights and neurons. This study investigates the accurate detection of OMM using significant DL algorithms such as multi-layer perceptron (MLP), self-organizing maps (SOM), long short-term memory (LSTM) networks, auto encoders (AE), and convolutional neural network (CNN) based on appropriate parameter tuning. The dataset taken for the study is CICMalMem2022 that contains 58,596 samples with 57 features which is basically designed for OMM detection. The dataset comprises Spyware, Ransomware, Trojan horse, and Benign. The DL models are evaluated based on performance metrics such as precision, recall, accuracy, training accuracy, test accuracy, validation accuracy, training loss, validation loss and receiver operating characteristic (ROC) curve. Based on the experimental evaluation, the study reveals that LSTM outperforms with 100% accuracy and MLP achieves 99.9% accuracy in detecting and classifying the OMM using deep supervised learning (SL) mechanism.

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Intelligent malware classification based on network traffic and data augmentation techniques

Cited by 2 publications

References 13 publications

A novel method of detecting malware on Android mobile devices with explainable artificial intelligence

A novel method of detecting malware on Android mobile devices with explainable artificial intelligence

An intelligent obfuscated mobile malware detection using deep supervised learning algorithms

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