Analysis of Machine Learning Classifier in Android Malware Detection Through Opcode

Anuar, Noor Azleen; Mas'ud, M. Z.; Bahaman, Nazrulazhar; Ariff, Nor Azman Mat

doi:10.1109/ains50155.2020.9315060

Cited by 7 publications

(3 citation statements)

References 20 publications

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“…After generating a malware and cleanware dataset, it is put to use in testing. We analysed malware and placed it into different groups using a number of supervised machine learning methods, such as kNN, DT, RF, AdaBoost, SGD, extra trees and the Gaussian NB Classifier [ 22 , 25 ].…”

Section: Resultsmentioning

confidence: 99%

“…The relevant scientific literature was partitioned into two groups, with one for each signature and behaviour type, and a comparative analysis of methods was carried out. In addition, recent studies have shown that hybrid techniques are more accurate than static or dynamic analysis alone; therefore, they should not be neglected [ 25 ].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Included were more than 198,063 separate data points gathered from a wide variety of sources. There were 373 rows in the data and 531 columns as shown in Table 1 [22][23][24][25]. column that indicates whether or not the file is harmful.…”

Section: Datasetmentioning

confidence: 99%

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Evaluation of Machine Learning Algorithms for Malware Detection

Akhtar

Feng

2023

Sensors

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This research study mainly focused on the dynamic malware detection. Malware progressively changes, leading to the use of dynamic malware detection techniques in this research study. Each day brings a new influx of malicious software programmes that pose a threat to online safety by exploiting vulnerabilities in the Internet. The proliferation of harmful software has rendered manual heuristic examination of malware analysis ineffective. Automatic behaviour-based malware detection using machine learning algorithms is thus considered a game-changing innovation. Threats are automatically evaluated based on their behaviours in a simulated environment, and reports are created. These records are converted into sparse vector models for use in further machine learning efforts. Classifiers used to synthesise the results of this study included kNN, DT, RF, AdaBoost, SGD, extra trees and the Gaussian NB classifier. After reviewing the test and experimental data for all five classifiers, we found that the RF, SGD, extra trees and Gaussian NB Classifier all achieved a 100% accuracy in the test, as well as a perfect precision (1.00), a good recall (1.00), and a good f1-score (1.00). Therefore, it is reasonable to assume that the proof-of-concept employing autonomous behaviour-based malware analysis and machine learning methodologies might identify malware effectively and rapidly.

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Section: Resultsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%