Improving software quality using machine learning

The evolution of malware is causing mobile devices to crash with increasing frequency. Therefore, adequate security evaluations that detect Android malware are crucial. Two techniques can be used in this regard: Static analysis, which meticulously examines the full codes of applications, and dynamic analysis, which monitors malware behaviour. While both perform security evaluations successfully, there is still room for improvement. The goal of this research is to examine the effectiveness of static analysis to detect Android malware by using permission-based features. This study proposes machine learning with different sets of classifiers was used to evaluate Android malware detection. The feature selection method in this study was applied to determine which features were most capable of distinguishing malware. A total of 5,000 Drebin malware samples and 5,000 Androzoo benign samples were utilised. The performances of the different sets of classifiers were then compared. The results indicated that with a TPR value of 91.6%, the Random Forest algorithm achieved the highest level of accuracy in malware detection.

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“…[ 51 ] has shown 91.95% accuracy in malware detection. However, only 7400 datasets were used compared with 10,000 data sets in this study [ 9 ].…”

Section: Discussionmentioning

confidence: 99%

“…Weka is one of the machine learning software used to solve complex issues. It provides a graphical interface to display the results and different algorithms to predict and model the data [ 51 ]. applied WEKA to evaluate and improve the quality of Android software [ 52 ].…”

Section: Methodsmentioning

confidence: 99%

A static analysis approach for Android permission-based malware detection systems

et al. 2021

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“…It includes many applications such as speech recognition, language processing, machine intelligence, and fog computing [53,54]. Machine learning is one of the popular fields of artificial intelligence that is successfully used in solving various computational problems of different areas [56,57]. Now a days it is extended to more deep networks such as deep learning [58], extreme learning [59], deep extreme learning networks etc.…”

Section: Machine Learning Methodsmentioning

confidence: 99%

Comparing and Analyzing Applications of Intelligent Techniques in Cyberattack Detection

Dixit

Kohli

Acevedo-Duque

et al. 2021

Security and Communication Networks

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Now a day’s advancement in technology increases the use of automation, mobility, smart devices, and application over the Internet that can create serious problems for protection and the privacy of digital data and raised the global security issues. Therefore, the necessity of intelligent systems or techniques can prevent and protect the data over the network. Cyberattack is the most prominent problem of cybersecurity and now a challenging area of research for scientists and researchers. These attacks may destroy data, system, and resources and sometimes may damage the whole network. Previously numerous traditional techniques were used for the detection and mitigation of cyberattack, but the techniques are not efficient for new attacks. Today’s machine learning and metaheuristic techniques are popularly applied in different areas to achieve efficient computation and fast processing of complex data of the network. This paper is discussing the improvements and enhancement of security models, frameworks for the detection of cyberattacks, and prevention by using different machine learning and optimization techniques in the domain of cybersecurity. This paper is focused on the literature of different metaheuristic algorithms for optimal feature selection and machine learning techniques for the classification of attacks, and some of the prominent algorithms such as GA, evolutionary, PSO, machine learning, and others are discussed in detail. This study provides descriptions and tutorials that can be referred from various literature citations, references, or latest research papers. The techniques discussed are efficiently applied with high performance for detection, mitigation, and identification of cyberattacks and provide a security mechanism over the network. Hence, this survey presents the description of various existing intelligent techniques, attack datasets, different observations, and comparative studies in detail.

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“…Currently, with the wide application of machine learning models in various fields, [13][14][15][16]. The machine learning model plays an important role in bankruptcy classification.…”

Section: Important Literature In the Field Of Bankruptcy Predictionmentioning

confidence: 99%

Undersampling bankruptcy prediction: Taiwan bankruptcy data

Wang

Liu

2021

PLoS ONE

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Machine learning models have increasingly been used in bankruptcy prediction. However, the observed historical data of bankrupt companies are often affected by data imbalance, which causes incorrect prediction, resulting in substantial economic losses. Many studies have proposed the insolvency imbalance problem, but little attention has been paid to the effect of the undersampling technology. Therefore, a framework is used to spot-check algorithms quickly and combine which undersampling method and classification model performs best. The results show that Naive Bayes (NB) after Edited Nearest Neighbors (ENN) has the best performance, with an F2-measure of 0.423. In addition, by changing the undersampling rate of the cluster centroid-based method, we find that the performance of the Linear Discriminant Analysis (LDA) and Naive Bayes (NB) are affected by the undersampling rate. Neither of them is uniformly declining, and LDA has higher performance when the undersampling rate is 30%. This study accordingly provides another perspective and a guide for future design.

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Improving software quality using machine learning

Cited by 24 publications

References 6 publications

A static analysis approach for Android permission-based malware detection systems

A static analysis approach for Android permission-based malware detection systems

Comparing and Analyzing Applications of Intelligent Techniques in Cyberattack Detection

Undersampling bankruptcy prediction: Taiwan bankruptcy data

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