An efficient intrusion detection framework based on embedding feature selection and ensemble learning technique
المؤلفون المشاركون
Muqbil, Fawwaz
Dan, Wang
Uthman, Musa
al-Samhi, Said
Ping, Yang
المصدر
The International Arab Journal of Information Technology
العدد
المجلد 19، العدد 2 (31 مارس/آذار 2022)، ص ص. 237-248، 12ص.
الناشر
جامعة الزرقاء عمادة البحث العلمي
تاريخ النشر
2022-03-31
دولة النشر
الأردن
عدد الصفحات
12
التخصصات الرئيسية
تكنولوجيا المعلومات وعلم الحاسوب
الملخص EN
Network security has emerged as a crucial universal issue that affects enterprises, governments, and individuals.
The strategies utilized by the attackers are continuing to evolve, and therefore the rate of attacks targeting the network system has expanded dramatically.
An Intrusion Detection System (IDS) is one of the significant defense solutions against sophisticated cyberattacks.
However, the challenge of improving the accuracy, detection rate, and minimal false alarms of the IDS continues.
This paper proposes a robust and effective intrusion detection framework based on the ensemble learning technique using eXtreme Gradient Boosting (XGBoost) and an embedded feature selection method.
Further, the best uniform feature subset is extracted using the up-to-date real-world intrusion dataset Canadian Institute for Cybersecurity Intrusion Detection (CICIDS2017) for all attacks.
The proposed IDS framework has successfully exceeded several evaluations on a big test dataset over both multi and binary classification.
The achieved results are promising on various measurements with an accuracy overall, precision, detection rate, specificity, F-score, false-negative rate, false-positive rate, error rate, and The Area Under the Curve (AUC) scores of 99.86%, 99.69%, 99.75%, 99.69%, 99.72%, 0.17%, 0.2%, 0.14%, and 99.72 respectively for abnormal class.
Moreover, the achieved results of multi-classification are also remarkable and impressively great on all performance metrics.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Muqbil, Fawwaz& Dan, Wang& Uthman, Musa& Ping, Yang& al-Samhi, Said. 2022. An efficient intrusion detection framework based on embedding feature selection and ensemble learning technique. The International Arab Journal of Information Technology،Vol. 19, no. 2, pp.237-248.
https://search.emarefa.net/detail/BIM-1437182
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Muqbil, Fawwaz…[et al.]. An efficient intrusion detection framework based on embedding feature selection and ensemble learning technique. The International Arab Journal of Information Technology Vol. 19, no. 2 (Mar. 2022), pp.237-248.
https://search.emarefa.net/detail/BIM-1437182
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Muqbil, Fawwaz& Dan, Wang& Uthman, Musa& Ping, Yang& al-Samhi, Said. An efficient intrusion detection framework based on embedding feature selection and ensemble learning technique. The International Arab Journal of Information Technology. 2022. Vol. 19, no. 2, pp.237-248.
https://search.emarefa.net/detail/BIM-1437182
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references : p. 246-247
رقم السجل
BIM-1437182
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