Defending Malicious Script Attacks Using Machine Learning Classifiers

المؤلفون المشاركون

Khan, Nayeem
Abdullah, Johari
Khan, Adnan Shahid

المصدر

Wireless Communications and Mobile Computing

العدد

المجلد 2017، العدد 2017 (31 ديسمبر/كانون الأول 2017)، ص ص. 1-9، 9ص.

الناشر

Hindawi Publishing Corporation

تاريخ النشر

2017-02-07

دولة النشر

مصر

عدد الصفحات

9

التخصصات الرئيسية

تكنولوجيا المعلومات وعلم الحاسوب

الملخص EN

The web application has become a primary target for cyber criminals by injecting malware especially JavaScript to perform malicious activities for impersonation.

Thus, it becomes an imperative to detect such malicious code in real time before any malicious activity is performed.

This study proposes an efficient method of detecting previously unknown malicious java scripts using an interceptor at the client side by classifying the key features of the malicious code.

Feature subset was obtained by using wrapper method for dimensionality reduction.

Supervised machine learning classifiers were used on the dataset for achieving high accuracy.

Experimental results show that our method can efficiently classify malicious code from benign code with promising results.

نمط استشهاد جمعية علماء النفس الأمريكية (APA)

Khan, Nayeem& Abdullah, Johari& Khan, Adnan Shahid. 2017. Defending Malicious Script Attacks Using Machine Learning Classifiers. Wireless Communications and Mobile Computing،Vol. 2017, no. 2017, pp.1-9.
https://search.emarefa.net/detail/BIM-1205952

نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)

Khan, Nayeem…[et al.]. Defending Malicious Script Attacks Using Machine Learning Classifiers. Wireless Communications and Mobile Computing No. 2017 (2017), pp.1-9.
https://search.emarefa.net/detail/BIM-1205952

نمط استشهاد الجمعية الطبية الأمريكية (AMA)

Khan, Nayeem& Abdullah, Johari& Khan, Adnan Shahid. Defending Malicious Script Attacks Using Machine Learning Classifiers. Wireless Communications and Mobile Computing. 2017. Vol. 2017, no. 2017, pp.1-9.
https://search.emarefa.net/detail/BIM-1205952

نوع البيانات

مقالات

لغة النص

الإنجليزية

الملاحظات

Includes bibliographical references

رقم السجل

BIM-1205952