Metaheuristics with Deep Learning Model for Cybersecurity and Android Malware Detection and Classification

Albakri, Ashwag; Alhayan, Fatimah; Alturki, Nazik; Ahamed, Saahirabanu; Shamsudheen, Shermin

doi:10.3390/app13042172

Cited by 17 publications

(7 citation statements)

References 22 publications

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“…The structure utilizes the EffectiveNetB0 convolutional neural network (CNN) for feature abstraction, which was then delivered over a worldwide average pooling layer and provided in a stacking identifier. The author in [16] projects a Rock Hyrax Swarm Optimizer with a DL-based Android malware detection (RHSODL-AMD) approach. Moreover, the Adamax enhancer with attention recurrent autoencoder (ARAE) technique has been used for Android malware recognition.…”

Section: Literature Reviewmentioning

confidence: 99%

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Intelligent Pattern Recognition Using Equilibrium Optimizer With Deep Learning Model for Android Malware Detection

Maray,

Maashi,

Alshahrani

et al. 2024

IEEE Access

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Android malware recognition is the procedure of mitigating and identifying malicious software (malware) planned to target Android operating systems (OS) that are extremely utilized in smartphones and tablets. As the Android ecosystem endures to produce, therefore is the risk of malware attacks on these devices. Identifying Android malware is vital for keeping user data, privacy, and device integrity. Android malware detection utilizing deep learning (DL) signifies a cutting-edge system for the maintenance of mobile devices. DL approaches namely recurrent neural network (RNN) and convolutional neural network (CNN) are best in automatically removing intricate designs and behaviors in Android app data. By leveraging features such as application programming interface (API) call sequences, code patterns, and permissions, these approaches are efficiently differentiated between benign and malicious apps, even in the face of previous unseen attacks. This study presents an Intelligent Pattern Recognition using an Equilibrium Optimizer with Deep Learning (IPR-EODL) Approach for Android Malware Recognition. The purpose of the IPR-EODL approach is to properly identify and categorize the Android malware in such a way that security can be achieved. In the IPR-EODL technique, the data pre-processing step was applied to convert input data into a compatible setup. In addition, the IPR-EODL technique applies channel attention long shortterm memory (CA-LSTM) methodology for the recognition of Android malware. To enhance the solution of the CA-LSTM algorithm, the IPR-EODL system employs the Equilibrium optimization (EO) algorithm for the hyperparameter tuning method. The experimentation evaluation of the IPR-EODL model can be verified on a benchmark Android malware database. The extensive results highlight the significant result of the IPR-EODL approach to the Android malware detection process.

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Section: Literature Reviewmentioning

confidence: 99%

“…Google defines malware as possibly malicious features [7]. They categorized malware as commercial and noncommercial privilege escalation, spyware, phishing, and kinds of frauds like Trojans, backdoors toll fraud, and SMS fraud.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Pattern Recognition Using Equilibrium Optimizer With Deep Learning Model for Android Malware Detection

Maray,

Maashi,

Alshahrani

et al. 2024

IEEE Access

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“…Their efforts provide a substantial improvement in mobile security by automating the detection of harmful elements. For the objective of increasing cybersecurity and Android virus detection, Albakri et al [47] investigated the combination of metaheuristics with deep learning models. In the context of malware categorization, their study provides insightful information about the possible synergy between optimization approaches and deep learning.…”

Section: Literature Reviewmentioning

confidence: 99%

Detection of Android Malware Using Machine Learning and Siamese Shot Learning Technique for Security

Almarshad,

Zakariah,

Gashgari

et al. 2023

IEEE Access

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Android malware security tools that can swiftly identify and categorize various malware classes to create rapid response strategies have been trendy in recent years. Although many application fields have demonstrated the usefulness of implementing Machine Learning and deep learning methods to provide automation and self-learning services, the scarcity of data for malware samples has been cited as a hurdle in creating efficient deep learning-based solutions. In this paper, a one-shot learning-based Siamese neural network is proposed to overcome this issue, as it can both identify malware assaults and categorize malware into multiple categories. The Drebin dataset, which is divided into benign and harmful components, is used in our suggested methodology. The efficiency of the suggested strategy is evaluated through a database dataset made up of 9476 goodware applications and 5560 Android malware apps. The five critical phases of its implementation are pre-processing, data partitioning, model architecture, training, and assessment. In both the training and testing phases, Siamese networks are trained to rank sample similarity, and the accuracy is determined using N-way one-shot tasks. According to the experiment's findings, our Siamese Shot model fared better than the other standard approaches, obtaining an accuracy of 98.9%. Additionally, the most wellliked platforms are Keras and TensorFlow.

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“…Albakri et al [38] combined DL with rock hyrax swarm optimization (RHSO) for detecting Android malware attacks. The RHSO was mainly used to select the most contributing features to the target class.…”

Section: Android Detection Based On Deep Learningmentioning

confidence: 99%

Explainable Classification Model for Android Malware Analysis Using API and Permission-Based Features

Aslam,

Ullah Khan,

Abdulrahman Bader

et al. 2023

Computers, Materials &Amp; Continua

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One of the most widely used smartphone operating systems, Android, is vulnerable to cutting-edge malware that employs sophisticated logic. Such malware attacks could lead to the execution of unauthorized acts on the victims' devices, stealing personal information and causing hardware damage. In previous studies, machine learning (ML) has shown its efficacy in detecting malware events and classifying their types. However, attackers are continuously developing more sophisticated methods to bypass detection. Therefore, up-to-date datasets must be utilized to implement proactive models for detecting malware events in Android mobile devices. Therefore, this study employed ML algorithms to classify Android applications into malware or goodware using permission and application programming interface (API)-based features from a recent dataset. To overcome the dataset imbalance issue, RandomOverSampler, synthetic minority oversampling with tomek links (SMOTETomek), and RandomUnderSampler were applied to the Dataset in different experiments. The results indicated that the extra tree (ET) classifier achieved the highest accuracy of 99.53% within an elapsed time of 0.0198 s in the experiment that utilized the RandomOverSampler technique. Furthermore, the explainable Artificial Intelligence (EAI) technique has been applied to add transparency to the high-performance ET classifier. The global explanation using the Shapely values indicated that the top three features contributing to the goodware class are: Ljava/net/URL;->openConnection, Landroid/location/LocationManager;->getLastKgoodwarewnLocation, and Vibrate. On the other hand, the top three features contributing to the malware class are Receive_Boot_Completed, Get_Tasks, and Kill_Background_Processes. It is believed that the proposed model can contribute to proactively detecting malware events in Android devices to reduce the number of victims and increase users' trust.

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Metaheuristics with Deep Learning Model for Cybersecurity and Android Malware Detection and Classification

Cited by 17 publications

References 22 publications

Intelligent Pattern Recognition Using Equilibrium Optimizer With Deep Learning Model for Android Malware Detection

Intelligent Pattern Recognition Using Equilibrium Optimizer With Deep Learning Model for Android Malware Detection

Detection of Android Malware Using Machine Learning and Siamese Shot Learning Technique for Security

Explainable Classification Model for Android Malware Analysis Using API and Permission-Based Features

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