Android Malware Classification Using Optimum Feature Selection and Ensemble Machine Learning

Islam, Rejwana; Sayed, Moinul Islam; Saha, Sajal Kumar; Hossain, Muhammad Shazzad; Masud, Md. Abdul

doi:10.2139/ssrn.4264058

Cited by 7 publications

(9 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The quantity and quality of the data collected are essential to create an accurate model. Malware detection, for example, Android malware detection with the MH-100K dataset [8], CICAndMal2017 [9], [6] CICInvesAndMal2019 dataset [10], CCCS-CIC-AndMal-2020 [11], The study utilizes the Andro-AutoPsy [12], Android Malware, McAfee Labs [13], Android malware AndroZoo [14], While some produce their datasets and employ various machine learning methodologies to validate them [15], [50] Table 2 lists the different dataset types that were used in malware detection. Because of its extensive representation of real-world network traffic, a wide range of cyberattacks, large volume, and established reputation within the cybersecurity research community, the Canadian Institute for Cybersecurity (CIC) dataset is the most commonly utilized.…”

Section: Data Collectionmentioning

confidence: 99%

“…MH-100K dataset [6] CICAndMal2017 [7], [4] CICInvesAndMal 2019 [8] CCCS-CIC-AndMal-2020 [9] Andro-AutoPsy [10] Android Malware, McAfee Labs [11] AndroZoo [12] To further confirm the efficacy of the suggested strategy, the authors advise running trials on a bigger dataset. To give users access to real-time risk assessment, future research can also investigate integrating the risk detection system into app stores or security tools.…”

Section: Dataset Names Behavioral Features Machine Learning Deep Lear...mentioning

confidence: 99%

See 1 more Smart Citation

Mobile based Malware Detection using Artificial Intelligence Techniques a review

Faez mahdi,

Saadoon Jasim

2024

J. Al-Qadisiyah Comp. Sci. Math.

View full text Add to dashboard Cite

Malware attacks on mobile devices are becoming more common and more complicated every year. Malware writers see the open-source Android app as their main target because so many people use it. Artificial intelligence is used by most of the literature's mobile malware detection methods to find ransomware. Our research, on the other hand, makes it clear that most of the earlier studies used different metrics and models, as well as different datasets and classification features that came from static, dynamic, or hybrid analysis strategies. This makes comparing the different suggested detection methods more difficult and may also make the results less certain. The goal of this work is to solve the problem of AI-powered malware detection by sorting current methods and approaches into three groups: the type of dataset, the type of detection method used (machine learning models, deep learning models, and Behavioral Analysis model), and how well the method works. In this way, we suggest a convergent plan that can be used as a basis for future methods of finding malware on Android and as a solid standard for artificial intelligence work in this area.

show abstract

Section: Data Collectionmentioning

confidence: 99%

Section: Dataset Names Behavioral Features Machine Learning Deep Lear...mentioning

confidence: 99%

Mobile based Malware Detection using Artificial Intelligence Techniques a review

Faez mahdi,

Saadoon Jasim

2024

J. Al-Qadisiyah Comp. Sci. Math.

View full text Add to dashboard Cite

show abstract

“…Machine Learning can be utilized through Data Analysis and Feature Selection, Model Development, Training and Validation, and Predictive analysis. The classification task of predicting a specific disease, malware, or conditions using ML techniques enables one to reduce the dimension of the features using feature selection techniques or applying the data analysis approaches and combining the different model's predictions using ensemble techniques (Islam et al, 2023).…”

Section: Related Workmentioning

confidence: 99%

Machine Learning-based maternal health risk prediction model for IoMT framework

Mondal,

Nag,

Barman

et al. 2023

IJERR

View full text Add to dashboard Cite

The Internet of Things (IoT) is vital as it offers extensive applicability in various fields, including healthcare. In the context of the risk level during pregnancy, to monitor and predict abnormalities, IoT devices provide a means to collect real-time health data, enabling continuous monitoring and analysis in the Internet of Medical Things (IoMT) environments. By integrating IoT devices into the system, crucial signs such as Heart Rate (HR), Systolic and Diastolic Blood Pressure (BP), Fetal Movements (FM), and Temperature (T) can be tracked remotely and non-invasively. This allows for the timely detection of abnormalities or potential risk factors during pregnancy, empowering healthcare professionals to intervene proactively and provide personalized care. This research focuses on developing a system for observing and predicting the maternal risk level in the IoT environment, mainly in remote areas. The goal is to improve maternal health and reduce maternal and child mortality rates, a significant decline according to United Nations targets for 2030. The research utilizes analytical tools and Machine Learning (ML) algorithms to analyze health data and risk factors associated with pregnancy. The acquired dataset contains various risk factors categorized and classified based on intensity. After comparing different ML models’ experimental results, Exploratory Data Analysis (EDA) approaches to determine the most effective risk factors. The fine-tuned Random Forest Classifier (RF) achieves the highest accuracy of 93.14%. An Android-based application has also been developed to deploy the prediction model to determine risk levels based on the different parameters.

show abstract

“…Since 2012, Android has been one of the most widely used operating systems (OS). It encompasses a range of concealed malicious activities alongside benign ones, distributed across app stores [2,3]. Thereby, alarmingly enhancing the rate of malicious applications and their difficulties has become a serious challenge.…”

Section: Introductionmentioning

confidence: 99%

“…The selected features are provided as input to the classification process to detect the malware, which has a significant International Journal of Intelligent Engineering and Systems, Vol.17, No. 3,2024 DOI: 10.22266/ijies2024.0630.49 effect on malware detection [11,12]. The existing researchers develop various Machine Learning (ML) for the detection and classification of malware in the android.…”

Section: Introductionmentioning

confidence: 99%

Feature Selection Using Adaptive Weight Based Grey Wolf Optimization for Malware Detection in Android

2024

IJIES

View full text Add to dashboard Cite

The malware application is one of the most dangerous threats to various applications, particularly in Android devices. To effectively detect the malware in Android, the existing researchers utilized the Machine Learning (ML) approaches. However, the researchers have not accurately detected the malware due to the malware complexity, continuous changes as well as increased damages caused by attackers. In this paper, an Adaptive Weight based Grey Wolf Optimization (AWGWO) based feature selection is proposed for malware detection in Android. The proposed AWGWO is evaluated by using Drebin and CICInvesAndMal2019 dataset which contains 216 and 428 malware features. Then, the acquired dataset is pre-processed by utilizing the Min-Max normalization technique to remove the computational complexity and achieve minimum error rates. In the classification stage, a voting combination of benign and malignant malware is used while utilizing three ensemble ML approaches. The ensemble approaches involve a Support Vector Machine (SVM), Random Forest (RF) and AdaBoost algorithm. The proposed AWGWO method achieves better results by using evaluation metrics like accuracy, precision, recall, and F1-score of 0.9953, 0.9930, 0.9963, and 0.9725 respectively which is comparatively better than the existing methods named Random Forest-OWL (RF-OWL), SVM, Adjacency Matrix (AdMat) and Effective Feature selection -RF (EF-RF).

show abstract

Android Malware Classification Using Optimum Feature Selection and Ensemble Machine Learning

Cited by 7 publications

References 0 publications

Mobile based Malware Detection using Artificial Intelligence Techniques a review

Mobile based Malware Detection using Artificial Intelligence Techniques a review

Machine Learning-based maternal health risk prediction model for IoMT framework

Feature Selection Using Adaptive Weight Based Grey Wolf Optimization for Malware Detection in Android

Contact Info

Product

Resources

About