Binary File’s Visualization and Entropy Features Analysis Combined with Multiple Deep Learning Networks for Malware Classification
المؤلفون المشاركون
Huang, Cheng
Guo, Hui
Huang, Shuguang
Shi, Fan
Zhang, Min
Pan, Zulie
المصدر
Security and Communication Networks
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-19، 19ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-12-04
دولة النشر
مصر
عدد الصفحات
19
التخصصات الرئيسية
تكنولوجيا المعلومات وعلم الحاسوب
الملخص EN
In recent years, the research on malware variant classification has attracted much more attention.
However, there are still many challenges, including the low accuracy of classification of samples of similar malware families, high time, and resource consumption.
This paper proposes a new method of malware classification based on multiple visual features of malware and deep learning algorithms.
In prior research, visualization techniques and entropy demonstrated exemplary performance in many areas.
This paper extracts numerous visual features from the raw bytes and entropy sequence of the malware, which makes it more sensitive to malware samples of similar families and endows it the ability to classify malware variants more accurately.
To evaluate the proposed method, this paper conducted a series of experiments on two malware datasets with a total of more than 20,000 samples provided by the Malware Research Lab and Microsoft Research.
Through experiments, the method showed its superiority compared with some leading malware visual classification methods, achieving good performance on the accuracy with at least 1% improvement.
The accuracy of the method even could reach 99.73% and 99.54%, respectively, on the two datasets.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Guo, Hui& Huang, Shuguang& Huang, Cheng& Shi, Fan& Zhang, Min& Pan, Zulie. 2020. Binary File’s Visualization and Entropy Features Analysis Combined with Multiple Deep Learning Networks for Malware Classification. Security and Communication Networks،Vol. 2020, no. 2020, pp.1-19.
https://search.emarefa.net/detail/BIM-1208849
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Guo, Hui…[et al.]. Binary File’s Visualization and Entropy Features Analysis Combined with Multiple Deep Learning Networks for Malware Classification. Security and Communication Networks No. 2020 (2020), pp.1-19.
https://search.emarefa.net/detail/BIM-1208849
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Guo, Hui& Huang, Shuguang& Huang, Cheng& Shi, Fan& Zhang, Min& Pan, Zulie. Binary File’s Visualization and Entropy Features Analysis Combined with Multiple Deep Learning Networks for Malware Classification. Security and Communication Networks. 2020. Vol. 2020, no. 2020, pp.1-19.
https://search.emarefa.net/detail/BIM-1208849
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1208849
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر