An innovative two-stage fuzzy kNN-DST classifier for unknown intrusion detection
المؤلفون المشاركون
Jing, Xueyan
Deng, Hai
Bi, Yingtao
المصدر
The International Arab Journal of Information Technology
العدد
المجلد 13، العدد 4 (31 يوليو/تموز 2016)8ص.
الناشر
تاريخ النشر
2016-07-31
دولة النشر
الأردن
عدد الصفحات
8
التخصصات الرئيسية
تكنولوجيا المعلومات وعلم الحاسوب
الموضوعات
الملخص EN
Intrusion detection is the essential part of network security in combating against illegal network access or malicious attacks.
Due to constantly evolving nature of network attacks, it has been a technical challenge for an Intrusion Detection System (IDS) to recognize unknown attacks or known attacks with inadequate training data.
In this work, an innovative fuzzy classifier is proposed for effectively detecting both unknown attacks and known attacks with insufficient or inaccurate training information.
A Fuzzy C-Means (FCM) algorithm is firstly employed to softly compute and optimise clustering centers of the training datasets with some degree of fuzziness counting for inaccuracy and ambiguity in the training data.
Subsequently, a distance-weighted k-Nearest Neighbors (k-NN) classifier, combined with the Dempster Shafer Theory (DST) is introduced to assess the belief functions and pignistic probabilities of the incoming data associated with each of known classes.
Finally, a two-stage intrusion detection scheme is implemented based on the obtained pignistic probabilities and their entropy function to determine if the input data are normal, one of the known attacks or an unknown attack.
The proposed intrusion detection algorithm is evaluated through the application of the KDD’99 datasets and their variants containing known and unknown attacks.
The experimental results show that the new algorithm outperforms other intrusion detection algorithms and is especially effective in detecting unknown attacks.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Jing, Xueyan& Bi, Yingtao& Deng, Hai. 2016. An innovative two-stage fuzzy kNN-DST classifier for unknown intrusion detection. The International Arab Journal of Information Technology،Vol. 13, no. 4.
https://search.emarefa.net/detail/BIM-655053
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Jing, Xueyan…[et al.]. An innovative two-stage fuzzy kNN-DST classifier for unknown intrusion detection. The International Arab Journal of Information Technology Vol. 13, no. 4 (Jul. 2016).
https://search.emarefa.net/detail/BIM-655053
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Jing, Xueyan& Bi, Yingtao& Deng, Hai. An innovative two-stage fuzzy kNN-DST classifier for unknown intrusion detection. The International Arab Journal of Information Technology. 2016. Vol. 13, no. 4.
https://search.emarefa.net/detail/BIM-655053
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes appendices.
رقم السجل
BIM-655053
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