An Evaluation of One-Class Feature Selection and Classification for Zero-Day Android Malware Detection

Wang, Yang; Zheng, Jun

doi:10.1007/978-3-030-43020-7_15

Cited by 3 publications

(3 citation statements)

References 7 publications

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“…Wang et al [ 33 ] discuss one-class classification methods for detecting zero-day Android malware attacks using Intra-Class Distance (ICD) feature selection method. The one-class classification methods use benign samples only to construct the detection model as opposed to the two-class models that use both benign and malware samples.…”

Section: Related Workmentioning

confidence: 99%

Feature Subset Selection for Malware Detection in Smart IoT Platforms

Abawajy

Darem

Alhashmi

2021

Sensors

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Malicious software (“malware”) has become one of the serious cybersecurity issues in Android ecosystem. Given the fast evolution of Android malware releases, it is practically not feasible to manually detect malware apps in the Android ecosystem. As a result, machine learning has become a fledgling approach for malware detection. Since machine learning performance is largely influenced by the availability of high quality and relevant features, feature selection approaches play key role in machine learning based detection of malware. In this paper, we formulate the feature selection problem as a quadratic programming problem and analyse how commonly used filter-based feature selection methods work with emphases on Android malware detection. We compare and contrast several feature selection methods along several factors including the composition of relevant features selected. We empirically evaluate the predictive accuracy of the feature subset selection algorithms and compare their predictive accuracy and the execution time using several learning algorithms. The results of the experiments confirm that feature selection is necessary for improving accuracy of the learning models as well decreasing the run time. The results also show that the performance of the feature selection algorithms vary from one learning algorithm to another and no one feature selection approach performs better than the other approaches all the time.

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Section: Related Workmentioning

confidence: 99%

Feature Subset Selection for Malware Detection in Smart IoT Platforms

Abawajy

Darem

Alhashmi

2021

Sensors

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“…Sharmeen et al [27] propose two different detection models using a semisupervised approach of deep learning and adaptive frameworks. Wang and Zheng [28] evaluate the performance of different one-class feature selection and classification methods for zero-day Android malware detection. Wen and Chow [29] propose using a CNN-based model to detect malware from very small sequences of binary fragments in PE files.…”

Section: Literature Surveymentioning

confidence: 99%

BFEDroid: A Feature Selection Technique to Detect Malware in Android Apps Using Machine Learning

Chimeleze

Jamil

Ismail

et al. 2022

Security and Communication Networks

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Malware detection refers to the process of detecting the presence of malware on a host system, or that of determining whether a specific program is malicious or benign. Machine learning-based solutions first gather information from applications and then use machine learning algorithms to develop a classifier that can distinguish between malicious and benign applications. Researchers and practitioners have long paid close attention to the issue. Most previous work has addressed the differences in feature importance or the computation of feature weights, which is unrelated to the classification model used, and therefore, the implementation of a selection approach with limited feature hiccups, and increases the execution time and memory usage. BFEDroid is a machine learning detection strategy that combines backward, forward, and exhaustive subset selection. This proposed malware detection technique can be updated by retraining new applications with true labels. It has higher accuracy (99%), lower memory consumption (1680), and a shorter execution time (1.264SI) than current malware detection methods that use feature selection.

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“…In Wang and Zheng (2020) used the intra-class distance (ICD) attribute selection approach to explore a one-class categorization framework for detecting zero-day Android malware threats. One classification systems employ only harmless examples to build the detection algorithm, whereas the two-class classification methods both use benign and malicious data.…”

Section: Related Workmentioning

confidence: 99%

Cyber security for federated learning environment using AI technique

Alyamani

2022

Expert Systems

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A cyber-security threat in the Android environment is seen as malicious code. Due to the rapid development of Android malware deployments, manually detecting malicious apps inside the Android environment is almost impossible. In the end of an outcome, machine learning has emerged as a promising method for detecting malware.Because the increasing accessibility and appropriate attributes have a significant impact on machine learning performance, selecting features becomes more critical in malware detection using the machine learning. In the existing system, a filter-based feature selection method was used. The filter-based feature subset selection strategies are mathematically efficient for computation. But they refuse to address concerns including such multicollinearity, which impacts the filter methods' accuracy. To overcome this issue, this research develops a hybrid method that combines the findings of both static and dynamic malware examination. This method tackles the issue of more effectively analysing, identifying, and classifying Android malware. Many feature selection approaches are compared and contrasted based on a variety of parameters, such as the composition of important extracted features. By using extracted features, whilst completing static and dynamic malware examinations, several machine learning methods are used to identify and characterize malware. The experimental result shows the evaluation of the feature selection method which is varied from other algorithms. It enhances the accuracy and minimizes the run time of the learning models.

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An Evaluation of One-Class Feature Selection and Classification for Zero-Day Android Malware Detection

Cited by 3 publications

References 7 publications

Feature Subset Selection for Malware Detection in Smart IoT Platforms

Feature Subset Selection for Malware Detection in Smart IoT Platforms

BFEDroid: A Feature Selection Technique to Detect Malware in Android Apps Using Machine Learning

Cyber security for federated learning environment using AI technique

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