Meerkat clan-based feature selection in random forest algorithm for IoT intrusion detection
المؤلفون المشاركون
al-Umran, Adil Yusuf Husayn
Sadiq, Ahmad T.
المصدر
Iraqi Journal of Computer, Communications and Control Engineering
العدد
المجلد 22، العدد 3 (30 سبتمبر/أيلول 2022)، ص ص. 15-24، 10ص.
الناشر
تاريخ النشر
2022-09-30
دولة النشر
العراق
عدد الصفحات
10
التخصصات الرئيسية
تكنولوجيا المعلومات وعلم الحاسوب
الملخص EN
Hackers can conduct more destructive cyber-attacks thanks to the rapid spread of Internet of Things (IoT) devices, posing significant security risks for users.
Through a malicious process, the attacker intended to exhaust the capital of the target IoT network.
Researchers and company owners are concerned about the reliability of IoT networks, which is taken into account because it has a significant impact on the delivery of facilities provided by IoT systems and the security of user groups.
The intrusion prevention system ensures that the network is protected by detecting malicious activity.
In this paper, the focus is on predicting attacks and distinguishing between normal network use and network exploitation for intrusion and network attack and we will use Swarm Intelligence (SI) which is one of the types of artificial intelligence (AI) that we harness to choose features to determine the task of them and specifically we will use an algorithm Meerkat Clan (MCA) for this purpose, as this research suggested a modified IDS in machine learning (ML) based IoT environments to identify features and these features will be input into Random Forest algorithm.
The IoTID20 dataset is used where nominal traits are removed, so the final dataset contains 79 traits.
The data set contains three categories: the label that identifies whether it is a natural use or exploitation, the category that characterizes the type of exploitation, and the subcategory that describes that exploitation more accurately.
The number of trees in a random forest (RF) classifier for binary, class, and subclass will be determined by the experiment.
The trained classifier is then tested and the approach achieves 100% accuracy for binary target prediction, 96.5% for category and accuracy ranges of 83.7% for sub-category target prediction.
The proposed system is evaluated and compared with previous systems and its performance is shown through the use of confusion matrix and others
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
al-Umran, Adil Yusuf Husayn& Sadiq, Ahmad T.. 2022. Meerkat clan-based feature selection in random forest algorithm for IoT intrusion detection. Iraqi Journal of Computer, Communications and Control Engineering،Vol. 22, no. 3, pp.15-24.
https://search.emarefa.net/detail/BIM-1492771
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
al-Umran, Adil Yusuf Husayn& Sadiq, Ahmad T.. Meerkat clan-based feature selection in random forest algorithm for IoT intrusion detection. Iraqi Journal of Computer, Communications and Control Engineering Vol. 22, no. 3 (Sep. 2022), pp.15-24.
https://search.emarefa.net/detail/BIM-1492771
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
al-Umran, Adil Yusuf Husayn& Sadiq, Ahmad T.. Meerkat clan-based feature selection in random forest algorithm for IoT intrusion detection. Iraqi Journal of Computer, Communications and Control Engineering. 2022. Vol. 22, no. 3, pp.15-24.
https://search.emarefa.net/detail/BIM-1492771
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references : p. 25
رقم السجل
BIM-1492771
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