Towards Design of a Novel Android Malware Detection Framework Using Hybrid Deep Learning Techniques

Dhabal, Gourab; Gupta, Govind

doi:10.1007/978-981-19-3590-9_15

Cited by 3 publications

(3 citation statements)

References 22 publications

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“…However, the model is not practical for real-world applications where the dataset does not include any IoT traffic or attack conditions due to dynamic and intricate patterns. Dhabal [26] investigated RNN-based DL-powered intrusion detection with competitive recall and precision; however, its real-time application is restricted due to the model's lengthy detection test period.…”

Section: Literature Reviewmentioning

confidence: 99%

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A Hybrid Optimization Model for Efficient Detection and Classification of Malware in the Internet of Things

Ahmad,

Wan,

Ahmad

et al. 2024

Mathematics

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The proliferation of Internet of Things (IoT) devices and their integration into critical infrastructure and business operations has rendered them susceptible to malware and cyber-attacks. Such malware presents a threat to the availability and reliability of IoT devices, and a failure to address it can have far-reaching impacts. Due to the limited resources of IoT devices, traditional rule-based detection systems are often ineffective against sophisticated attackers. This paper addressed these issues by designing a new framework that uses a machine learning (ML) algorithm for the detection of malware. Additionally, it also employed sequential detection architecture and evaluated eight malware datasets. The design framework is lightweight and effective in data processing and feature selection algorithms. Moreover, this work proposed a classification model that utilizes one support vector machine (SVM) algorithm and is individually tuned with three different optimization algorithms. The employed optimization algorithms are Nuclear Reactor Optimization (NRO), Artificial Rabbits Optimization (ARO), and Particle Swarm Optimization (PSO). These algorithms are used to explore a diverse search space and ensure robustness in optimizing the SVM for malware detection. After extensive simulations, our proposed framework achieved the desired accuracy among eleven existing ML algorithms and three proposed ensemblers (i.e., NRO_SVM, ARO_SVM, and PSO_SVM). Among all algorithms, NRO_SVM outperforms the others with an accuracy rate of 97.8%, an F1 score of 97%, and a recall of 99%, and has fewer false positives and false negatives. In addition, our model successfully identified and prevented malware-induced attacks with a high probability of recognizing new evolving threats.

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Section: Literature Reviewmentioning

confidence: 99%

“…The linear, polynomial, radial basis function (RBF), and sigmoid kernel functions are often utilized. The SVM optimization problem can be expressed mathematically as follows [26] in Equation (12):…”

Section: Svmmentioning

confidence: 99%

A Hybrid Optimization Model for Efficient Detection and Classification of Malware in the Internet of Things

Ahmad,

Wan,

Ahmad

et al. 2024

Mathematics

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“…(Xing et al 2022) proposed a novel malware detection model which combines a grey-scale image representation of malware with an autoencoder network in a deep learning model, analyses the feasibility of the grey-scale image approach of malware based on the reconstruction error of the autoencoder, and uses the dimensionality reduction features of the autoencoder to achieve the classification of malware from benign software. In the right context, (Dhabal & Gupta, 2023) proposed a novel Android malware detection framework using a hybrid of bidirectional long short-term memory (BiLSTM) and merged sparse auto-encoder (MSAE) with softmax deep learning mode. In the study done by (Alomari et al 2023), introduced deep learning and feature selection methodologies to arrive at a high-performance malware detection system.…”

Section: Android Malware Detection With Static Analysismentioning

confidence: 99%

Android Malware Detection Approach Using Stacked AutoEncoder and Convolutional Neural Networks

Menaouer,

Islem,

Nada

2023

International Journal of Intelligent Information Technologies

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In the past decade, Android has become a standard smartphone operating system. The mobile devices running on the Android operating system are particularly interesting to malware developers, as the users often keep personal information on their mobile devices. This paper proposes a deep learning model for mobile malware detection and classification. It is based on SAE for reducing the data dimensionality. Then, a CNN is utilized to detect and classify malware apps in Android devices through binary visualization. Tests were carried out with an original Android application (Drebin-215) dataset consisting of 15,036 applications. The conducted experiments prove that the classification performance achieves high accuracy of about 98.50%. Other performance measures used in the study are precision, recall, and F1-score. Finally, the accuracy and results of these techniques are analyzed by comparing the effectiveness with previous works.

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ANFIS-AMAL: Android Malware Threat Assessment Using Ensemble of ANFIS and GWO

Nwasra,

Daoud,

Qaisar

2024

Cybernetics and Information Technologies

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The Android malware has various features and capabilities. Various malware has distinctive characteristics. Ransomware threatens financial loss and system lockdown. This paper proposes a threat-assessing approach using the Grey Wolf Optimizer (GWO) to train and tune the Adaptive Neuro-Fuzzy Inference System (ANFIS) to categorize Android malware accurately. GWO improves efficiency and efficacy in ANFIS training and learning for Android malware feature selection and classification. Our approach categorizes Android malware as a high, moderate, or low hazard. The proposed approach qualitatively assesses risk based on critical features and threats. Our threat-assessing mechanism’s scale categorizes Android malware. The proposed approach resolves the issue of overlapping features in different types of malware. Comparative results with other classifiers show that the ensemble of GWO is effective in the training and learning process of ANFIS and thus achieves 95% F-score, 94% specificity, and 94% accuracy. The ensemble makes fast learning possible and improves classification accuracy.

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Towards Design of a Novel Android Malware Detection Framework Using Hybrid Deep Learning Techniques

Cited by 3 publications

References 22 publications

A Hybrid Optimization Model for Efficient Detection and Classification of Malware in the Internet of Things

A Hybrid Optimization Model for Efficient Detection and Classification of Malware in the Internet of Things

Android Malware Detection Approach Using Stacked AutoEncoder and Convolutional Neural Networks

ANFIS-AMAL: Android Malware Threat Assessment Using Ensemble of ANFIS and GWO

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