Exploring permissions in android applications using ensemble-based extra tree feature selection

al-kaaf, H.; Ali, Aida; Shamsuddin, Siti Mariyam; Hassan, Sitti Asmah

doi:10.11591/ijeecs.v19.i1.pp543-552

Cited by 7 publications

(6 citation statements)

References 23 publications

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“…Data sets from malware repositories, feature extraction techniques, and detection strategies are all discussed, as are the results of the various studies. Abubaker et al [4] gives framework that uses feature selection based on an ensemble extra tree classifier approach and a machine learning classifier to examine the behavior of malware apps by evaluating permissions. When it comes to detecting and classifying malware, a method has been presented [5].…”

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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“…It implies that the Static Ensemble method has received significant attention. (8)(9)(10)(11)(12)(13) , (32)(33)(34)(35)(36) 11 38% Dynamic Analysis Ensembles (14)(15)(16)(17)(18)(19)(20) , (37) 8 27% Hybrid Feature Ensembles (21)(22)(23)(24)(25)(26) 6 20% Structural Analysis Ensembles (27)(28)(29)(30)(31) 5 13%…”

Section: Structural Analysis Ensemblesmentioning

confidence: 99%

A Vast Review of Recognizing the Presence of Android Malware Based on Ensemble Machine Learning Technique

Malik,

Sharma

2024

IJST

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Background: It is evaluated that there is 70% to 80% of smartphone users have an Android mobile. Given its trend, a lot of malware strikes on the Android OS. In 2018, the largest number of malware attacks was identified, when there were 10.5 billion such malicious activity detected worldwide. Machine learning has emerged as a promising approach for detecting Android malware, and Ensemble machine learning has been shown to enhance the accuracy of malware detection in other domains. Objectives: In this paper, the systematic literature review were conducted using natural language processing. 30 papers are selected from January 2019 to August 2023 to give a clear picture of the most recent work in Android malware detection using ensemble machine learning. Methods: Initially the ensemble machine learning analysis were categorized in Android malware detection into four groups. Static Ensembles, Dynamic Ensembles, Hybrid Ensembles and Structural Ensembles method and compare the outcomes of empirical evidence with the help of a systematic literature review using the natural language processing method. Findings: The findings demonstrate an emerging trend of using NLP for Android malware detection in combination with ensemble machine learning models. The use of natural language processing (NLP) enhances the capacity to identify harmful patterns by making it easier to extract key features from textual input. The paper also emphasizes the variety of ensemble models used, including Tree-Based, Meta ensemble, Specialized ensemble and others. Significance : The novel aspects of this paper are its extensive comparative evaluation of ensemble and non-ensemble models, its original combination of NLP and ensemble machine learning for Android malware detection, and its extensive review of the literature with an eye toward future directions and research gaps. As a result, based on the present research community, it is important to develop some unique ways to enhance Android malware detection.

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“…The more significant or pertinent a feature is to the output variable, the higher the score. 32 For the purpose of developing a model, this score aids in selecting the most crucial aspects and eliminating the unnecessary ones. The ExtraTrees (ET) classifier or Extremely Randomized Trees Classifier technique is used to calculate the FI of a certain input with reference to the class attribute.…”

Section: Feature Selectionmentioning

confidence: 99%

“…Each feature of the data is given a score between zero and one by the feature importance score. The more significant or pertinent a feature is to the output variable, the higher the score 32 . For the purpose of developing a model, this score aids in selecting the most crucial aspects and eliminating the unnecessary ones.…”

Section: Proposed Modelmentioning

confidence: 99%

A feed forward deep neural network model using feature selection for cloud intrusion detection system

Sharma,

Singh

2023

Concurrency and Computation

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SummaryThe rapid advancement and growth of technology have rendered cloud computing services indispensable to our activities. Threats and intrusions have since multiplied exponentially across a range of industries. In such a scenario, the intrusion detection system, or simply the IDS, is deployed on the network to monitor and detect any attacks. The paper proposes a feed‐forward deep neural network (FFDNN) method based on deep learning methodology using a filter‐based feature selection model. The feature selection strategy aims to determine and select the most highly relevant subset of attributes from the feature importance score for training the deep learning model. Three benchmark data sets were used to assess the experiment: CIC‐IDS 2017, UNSW‐NB15, and NSL‐KDD. In order to justify the proposed technique, a comparison was done using other learning algorithms ranging from classical machine learning to ensemble learning methods that can detect various attacks. The experiments showed that the FFDNN model with reduced feature subsets gave the highest accuracy of 99.53% and 94.45% in the NSL‐KDD and UNSW‐NB15 data sets, while the ensemble‐based XGBoost model performed better in the CIC‐IDS 2017 data set. In addition, the results show that the overall accuracy, recall, and F1 score of the deep learning algorithm are generally better for all the data sets.

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Exploring permissions in android applications using ensemble-based extra tree feature selection

Cited by 7 publications

References 23 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

A Vast Review of Recognizing the Presence of Android Malware Based on Ensemble Machine Learning Technique

A feed forward deep neural network model using feature selection for cloud intrusion detection system

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