Convolution Neural Network-Based Higher Accurate Intrusion Identification System for the Network Security and Communication
المؤلفون المشاركون
Hong, Cheng
Khan, Sulaiman
Nazir, Shah
Gu, Zhiwei
المصدر
Security and Communication Networks
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-10، 10ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-08-28
دولة النشر
مصر
عدد الصفحات
10
التخصصات الرئيسية
تكنولوجيا المعلومات وعلم الحاسوب
الملخص EN
With the development of communication systems, information securities remain one of the main concerns for the last few years.
The smart devices are connected to communicate, process, compute, and monitor diverse real-time scenarios.
Intruders are trying to attack the network and capture the organization’s important information for its own benefits.
Intrusion detection is a way of identifying security violations and examining unwanted occurrences in a computer network.
Building an accurate and effective identification system for intrusion detection or malicious activities can secure the existing system for smooth and secure end-to-end communication.
In the proposed research work, a deep learning-based approach is followed for the accurate intrusion detection purposes to ensure the high security of the network.
A convolution neural network based approach is followed for the feature classification and malicious data identification purposes.
In the end, comparative results are generated after evaluating the performance of the proposed algorithm to other rival algorithms in the proposed field.
These comparative algorithms were FGSM, JSMA, C&W, and ENM.
After evaluating the performance of these algorithms and the proposed algorithm based on different threshold values ranging, Lp norms, and different parametric values for c, it was concluded that the proposed algorithm outperforms with small Lp values and high Kitsune scores.
These results reflect that the proposed research is promising toward the identification of attack on data packets, and it also reflects the applicability of the proposed algorithms in the network security field.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Gu, Zhiwei& Nazir, Shah& Hong, Cheng& Khan, Sulaiman. 2020. Convolution Neural Network-Based Higher Accurate Intrusion Identification System for the Network Security and Communication. Security and Communication Networks،Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1208629
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Gu, Zhiwei…[et al.]. Convolution Neural Network-Based Higher Accurate Intrusion Identification System for the Network Security and Communication. Security and Communication Networks No. 2020 (2020), pp.1-10.
https://search.emarefa.net/detail/BIM-1208629
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Gu, Zhiwei& Nazir, Shah& Hong, Cheng& Khan, Sulaiman. Convolution Neural Network-Based Higher Accurate Intrusion Identification System for the Network Security and Communication. Security and Communication Networks. 2020. Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1208629
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1208629
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