An Efficient Android Malware Detection Using Adaptive Red Fox Optimization Based CNN

Mahesh, P. C. Senthil; Hemalatha, S.

doi:10.1007/s11277-022-09765-0

Cited by 10 publications

(8 citation statements)

References 33 publications

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“…The results demonstrated that this method outperformed other methods in the literature, making it a more effective diagnostic tool for COVID-19 virus detection. Mahesh and Hemalatha (2022) proposed a CNN-ARFO approach based on a CNN to assist users in identifying malicious applications. Pugal Priya, Saradadevi Sivarani, and Gnana Saravanan (2021) developed a RFO algorithm that employs deep long short-term memory to classify mild, moderate, and severe stages of NPDR.…”

Section: Related Workmentioning

confidence: 99%

Bridging the Gap

Badawi

2024

ARO

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Effective organization and retrieval of news content are heavily reliant on accurate news classification. While the mountainous research has been conducted in resourceful languages like English and Chinese, the researches on under-resourced languages like the Kurdish language are severely lacking. To address this challenge, we introduce a hybrid approach called RFO-CNN in this paper. The proposed method combines an improved version of red fox optimization algorithm (RFO) and convolutional neural network (CNN) for finetuning CNN’s parameters. Our model’s efficacy was tested on two widely used Kurdish news datasets, KNDH and KDC-4007, both of which contain news articles classified into various categories. We compared the performance of RFO-CNN to other cutting-edge deep learning models such as bidirectional long short-term memory networks and bidirectional encoder representations from transformers (BERT) transformers, as well as classical machine learning approaches such as multinomial naive bayes, support vector machine, and K-nearest neighbors. We trained and tested our datasets using four different scenarios: 60:40, 70:30, 80:20, and 90:10. Our experimental results demonstrate the superiority of the RFO-CNN model across all scenarios, outperforming the benchmark BERT model and other machine learning models in terms of accuracy and F1-score.

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Section: Related Workmentioning

confidence: 99%

Bridging the Gap

Badawi

2024

ARO

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“…Some of the most commonly used methods are discussed in brief below. ✓ ✓ ✓ Zhu et al [190] ✓ ✓ ✓ A. Altaher [191] ✓ ✓ ✓ Su et al [192] ✓ ✓ ✓ Mahindru et al [193] ✓ ✓ ✓ Dehkordy et al [194] ✓ ✓ ✓ Nguyen et al [195] ✓ ✓ ✓ Taheri et al [196] ✓ ✓ ✓ Analysis Malware Detection Normal Malware Mahesh et al [197] ✓ ✓ ✓ Firdaus et al [198] ✓ ✓ ✓ Shrivastava et al [199] ✓ ✓ ✓ [20] chose to eliminate such permissions as they might introduce ambiguity in the malware detection process. Moreover, they ranked the features based on their frequency of usage in malicious and benign apps.…”

Section: Techniques Usedmentioning

confidence: 99%

“…Just like a student who learns a concept under the supervision of a teacher, the machines are used to predict an output correctly with the help of the training data working as a supervisor that teaches the machines. The type of machine learning in which the devices are trained using well [190] ✓ ✓ Permission rate, system events Su et al [192] ✓ ✓ ✓ ✓ Strings, certificate-payload info, code pattern Mahindru et al [193] ✓ ✓ Number of user download an app, rating of an app Dehkordy et al [194] ✓ ✓ ✓ ✓ URLs, activity, service receiver, provider Nguyen et al [195] ✓ ✓ ✓ ✓ Provider, activity, service, URLs Taheri et al [196] ✓ ✓ Mahesh et al [197] ✓ ✓ Firdaus et al [198] ✓ Directory path, telephony Shrivastava et al [199] ✓ ✓ Varsha et al [ Regression This type of learning is used when a relationship exists between the input and the output variable and the prediction is of continuous variables. Some commonly used regression algorithms are linear regression, regression trees, nonlinear regression, Bayesian linear regression, polynomial regression [214] etc.…”

Section: Supervised Machine Learningmentioning

confidence: 99%

“…Algorithm involving clustering-based sampling with the specific multi-view learning approach Zhu et al [190] RF, SVM A. Altaher [191] Neuro-fuzzy inference system Su et al [192] DBN, SVM Mahindru et al [193] SVM, NB, RF, LR, K-Means, FF, FC, DB, Bayesnet, Adaboost, DT, KNN, J48 DT MLP, DNN, SOM, Y-J48, Y-SMO, Y-MLP, best training ensemble approach (BTE), majority voting ensemble approach (MVE) and nonlinear ensemble decision tree forest approach (NDTF) Dehkordy et al [194] SVM, KNN, ID3 DT Nguyen et al [195] SVM DNN Taheri et al [196] SVM Robust NN, C4N CNN Mahesh et al [197] CNN, ANN , DBN, Method level behavioral semantic approach (MLBS) DE-CNN CNN-Adaptive Red Fox Optimization (CNN-ARFO)…”

Section: Deep-netmentioning

confidence: 99%

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A comprehensive review on permissions-based Android malware detection

Sharma,

Arora

2024

Int. J. Inf. Secur.

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The first Android-ready "G1" phone debuted in late October 2008. Since then, the growth of Android malware has been explosive analogous to the rise in the popularity of Android. The major positive aspect of Android has been its open-source nature, which empowers app developers to expand their work. But at the same time, authors with malicious intentions pose grave threats to users. In the presence of such threats, Android malware detection is the need of an hour. Consequently, researchers have proposed various techniques involving static, dynamic, and hybrid analysis to address such threats using numerous features in the last decade. But the feature that most researchers have extensively used to perform malware analysis and detection in Android security is Android permission. Hence, to provide a clarified overview of the latest and past work done in Android malware analysis and detection, we perform a comprehensive literature review using permissions as a central feature or in combination with other components by collecting and analyzing 200 studies from January 2009 to February 2023. We extracted information such as the choice opted by researchers between analysis or detection, techniques used to select or rank the permissions feature set, features used along with permissions, detection models employed, the malware datasets used by researchers, and lastly, the limitations and challenges in the field of Android malware detection to propose some future research directions. Additionally, based on the information extracted, we answer the six research questions designed considering the above factors.

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“…A graph attention network is constructed which is based on residual connection to analyze homology of malicious code. Mahesh et al [33] proposed an adaptive red fox optimization based on convolutional neural networks. They used the Minmax technique to normalize the features extracted from the APK of benign and malicious applications.…”

Section: Related Workmentioning

confidence: 99%

Android Malware Detection using LSTM with Smali Codes

Anand

Singh

Khan

et al. 2023

Preprint

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With the widespread adoption of smartphones and the rapid growth of the mobile Internet, the Android platform has become highly popular. However, its open-source nature has also made it susceptible to rampant malware attacks. In light of this, this research paper proposes a malware detection system based on Smali-LSTM to enhance the efficiency of malware detection on the Android platform. The detection system employs a static analysis approach to extract Smali files from applications. These extracted Smali files undergo a series of preprocessing steps to extract relevant features. To ensure compatibility with the LSTM model, the preprocessed Smali files are fragmented into smaller pieces. The paper explores and tests fragments of various sizes to identify the optimal configuration that yields the best results. The findings of the study demonstrate that the proposed Smali-LSTM model outperforms existing works that utilize the same dataset and LSTM model. The achieved results showcase an accuracy of 96.58\% and an impressive precision of 100\%. These outcomes validate the effectiveness and superiority of the proposed model in accurately detecting malware in Android applications.

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An Efficient Android Malware Detection Using Adaptive Red Fox Optimization Based CNN

Cited by 10 publications

References 33 publications

Bridging the Gap

Bridging the Gap

A comprehensive review on permissions-based Android malware detection

Android Malware Detection using LSTM with Smali Codes

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