RETRACTED: Detection of attacks in IoT sensors networks using machine learning algorithm (Retracted Article)

被引:17
|
作者
Bedi, Pradeep [1 ]
Mewada, Shivlal [2 ]
Vatti, Rasmbabu Arjunarao [3 ]
Singh, Chaitanya [4 ]
Dhindsa, Kanwalvir Singh [5 ]
Ponnusamy, Muruganantham [6 ]
Sikarwar, Ranjana [7 ]
机构
[1] Graph Era Hill Univ, Dept Comp Sci & Engn, Dehra Dun, Uttarakhand, India
[2] Govt Holkar Model Autonomous Sci Coll, Dept Comp Sci, Indore, Madhya Pradesh, India
[3] Bharat Inst Engn & Technol, Hyderabad, Telangana, India
[4] Shivajirao Kadam Inst Technol & Management, Dept Comp Sci & Engn, Indore, Madhya Pradesh, India
[5] Baba Banda Singh Bahadur Engn Coll, Dept Comp Sci & Engn, Fatehgarh Sahib, Punjab, India
[6] Saintgits Coll Engn, Dept Mech Engn, Kottayam, Kerala, India
[7] Dept Comp Sci & Engn, Bengaluru, India
关键词
Machine learning; Random forest; Internet of things; Support vector machine; Artificial intelligence; SCHEME;
D O I
10.1016/j.micpro.2020.103814
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Assault and peculiar location on the Internet of Things (IoT) framework is an increasing worry in the IoT region. By the expanded IoT foundation utilization in every area, assaults, and dangers in these frameworks are likewise developing proportionately. Malicious control, Spying, Forswearing of Service, Scan, Data Type Probing, Wrong setup, and malicious operation are such assaults and irregularities that may source an IOT framework disappointment. This project proposes a few Machine learning (ML) module that is contrasted with foresee assault and abnormalities on the IoT frameworks precisely. The ML algorithms that have been utilized here are Artificial Neural Network (ANN), Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT). The assessment measurements utilized in the examination of presentation are f1 score, exactness, area, recollect, and precision under the ROC Curve. Even though these strategies have similar accuracy, different measurements demonstrate that RF executes relatively preferable.
引用
收藏
页数:9
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